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By Author

Below are my 217 publications in APA style ordered by co-author. The By Type, By Date and By Topic pages show my publications grouped by publication type, date and research interest topic, respectively.

Brinkkemper,S.:17 Spruit,M.:17Scheepers,F.:12Meulendijk,M.:10Helms,R.:9Menger,V.:8Lefebvre,A.:8Yigit Ozkan,B.:7...Reijmer,T.:1

2023-01-20 16:49:25 (GMT)

Brinkkemper,S.

  1. Eskes,P., Spruit,M., Brinkkemper,S., Vorstman,J., & Kas,M. (2016). The Sociability Score: App-based social profiling from a healthcare perspective. Computers in Human Behavior, 59, 39–48. [JIF: 6.829] [pdf] [online]
  2. Meulendijk,M., Spruit,M., Willeboordse,F., Numans,M., Brinkkemper,S., Knol,W., Jansen,P., & Askari,M. (2016). Efficiency of clinical decision support systems improves with experience. Journal of Medical Systems, 40(4), 1–7. [JIF: 4.46] [pdf] [online]
  3. Meulendijk,M., Spruit,M., Drenth-van Maanen,C., Numans,M., Brinkkemper,S., Jansen,P., & Knol,W (2015). Computerized decision support improves medication review effectiveness: an experiment evaluating the STRIP Assistant’s usability. Drugs & Aging, 32(6), 495–503. [JIF: 3.923] [pdf] [online]
  4. Meulendijk,M., Spruit,M., Drenth-van-Maanen,A., Numans,M., Brinkkemper,S., & Jansen,P. (2013). General practitioners’ attitudes towards decision-supported prescribing: an analysis of the Dutch primary care sector. Health Informatics Journal, 19(4), 247–263. [JIF: 2.681] [pdf] [online]
  5. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]
  6. Meulendijk,M., Spruit,M., Lefebvre,A., & Brinkkemper,S. (2017). To what extent can prescriptions be meaningfully exchanged between primary care terminologies? A case study of four Western European classification systems. IET Software, 11(5), 256–264. [JIF: 1.363] [online]
  7. Fotaki,G., Spruit,M., Brinkkemper,S., & Meijer,D. (2014). Exploring big data opportunities for online customer segmentation. International Journal of Business Intelligence Research, 5(3), 57–73. [pdf] [online]
  8. Omta,W., Egan,D., Klumperman,J., Spruit,M., & Brinkkemper,S. (2013). HTS-IA: High Throughput Screening Information Architecture for Genomics. International Journal of Healthcare Information Systems and Informatics, 8(4), 17–31. [online]
  9. Omta,W., Egan,D., Spruit,M., & Brinkkemper,S. (2012). Information Architecture in High Throughput Screening. Procedia Technology, 5, 696–705. [pdf] [online]
  10. Meulendijk,M., Drenth-van-Maanen,A., Jansen,P., Brinkkemper,S., Numans,M., & Spruit,M. (2013). Introducing the CORETEST feasibility analysis in medical informatics: a case study of a decision-supportive knowledge system in the Dutch primary care sector. In Miranda,I., Cruz-Cunha,M., & Gonçalves,P. (Eds.), Handbook of Research on ICTs for Healthcare and Social Services: Developments and Applications (pp. 1066–1087). IGI Global. [pdf]
  11. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]
  12. Dijk,J. van, Choenni,S., Leertouwer,E., Spruit,M., & Brinkkemper,S. (2013). A Data Space System for the Criminal Justice Chain. Lecture Notes in Computer Science 8185, Proceedings of On the Move to Meaningful Internet Systems: OTM 2013 Conferences (pp. 755-763). ODBASE 2013, 10-11 September 2013, Graz, Austria, Springer. [pdf] [online]
  13. Bekkers,W., Weerd,I. van de, Spruit,M., & Brinkkemper,S. (2010). A Framework for Process Improvement in Software Product Management. Systems. In Riel,A., O'Connor,R., Tichkiewitch,S., & Messnarz,R. (Eds.), Communications in Computer and Information Science 99, Software and Services Process Improvement - Proceedings of the 17th European Conference (pp. 1–12). EuroSPI 2010, September 1-3, 2010, Grenoble, France: Springer. [pdf] [online]
  14. Meulendijk,M., Spruit,M., & Brinkkemper,S. (2017). Risk mediation in association rules: the case of decision support in medication review. In Teije,A. ten, Popow,C., Holmes,J., & Sacchi,L. (Eds.), LNAI 10259, 16th Conference on Artificial Intelligence in Medicine (pp. 327 ff). AIME 2017, June 21-24, Vienna, Austria: Springer. [pdf] [online]
  15. Meulendijk,M., Spruit,M., Numans,M., Brinkkemper,S., & Jansen,P. (2015). STRIPA: a rule-based decision support system for medication reviews in primary care. 23rd European Conference on Information Systems (pp. Paper 29). ECIS 2015, 26-29 May, 2015, Münster, Germany: AIS Electronic Library (AISeL). [pdf] [online]
  16. Meulendijk,M., Spruit,M., & Brinkkemper,S. (2017). Risk Mediation in Association Rules: Application Examples. Technical report UU-CS-2017-004, Department of Information and Computing Sciences, Utrecht University. [online]
  17. Fotaki,G., Spruit,M., Brinkkemper,S., & Meijer,D. (2013). Exploring Big data opportunities for Online Customer Segmentation. Technical report UU-CS-2013-021, Department of Information and Computing Sciences, Utrecht University. [online]

Spruit,M.

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]
  2. Homberg,M. van den, Monné,R., & Spruit,M. (2018). Bridging the information gap of disaster responders by optimizing data selection using cost and quality. Computers & Geosciences, 120, 60–72. [JIF: 3.372] [pdf] [online]
  3. Pieket Weeserik,B., & Spruit,M. (2018). Improving Operational Risk Management using Business Performance Management technologies. Sustainability, 10(3), 640. [JIF: 3.251] [online]
  4. Siegersma,K., Evers,M., Bots,S., Groepenhoff,F., Appelman,Y., Hofstra,L., Tulevski,I., Somsen,A., Den Ruijter,H., Spruit,M., & Onland-Moret,C. (2022). Adverse Drug Reactions Identification in clinical Notes (ADRIN): Word embedding models and string matching. JMIR Medical Informatics, 10(1), e31063. [JIF: 2.96] [pdf] [online]
  5. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]
  6. Haastrecht,M. van, Yigit Ozkan,B., Brinkhuis,M., & Spruit,M. (2021). Respite for SMEs: A Systematic Review of Socio-Technical Cybersecurity Metrics. Applied Sciences, 11(15), Human Factors in the Digital Society, 6909. [JIF: 2.679] [pdf] [online]
  7. Ooms,R., & Spruit,M. (2020). Self-Service Data Science in Healthcare with Automated Machine Learning. Applied Sciences, 10(9), Medical Artificial Intelligence, 2992. [JIF: 2.679] [pdf] [online]
  8. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]
  9. van Dijk,F., Gadellaa,J., Spruit,M., van Toledo,C., Brinkkemper,S., & Brinkhuis,M. (In press). Uncovering the Structures of Privacy Research using Bibliometric Network Analysis and Topic Modelling. Organizational Cybersecurity Journal: Practice, Process and People.
  10. Haastrecht,M. van, Sarhan,I., Yigit Ozkan,B., Brinkhuis,M., & Spruit,M. (2021). SYMBALS: A Systematic Review Methodology Blending Active Learning and Snowballing. Frontiers in Research Metrics and Analytics, 6, Section Text-mining and Literature-based Discovery. [pdf] [online]
  11. Homberg,M. van den, Monné,R., & Spruit,M. (2018). Bridging the Information Gap: Mapping Data Sets on Information Needs in the Preparedness and Response Phase. In Hostettler,S., Najih Besson,S., & Bolay J (Eds.), Technologies for Development (pp. 213–225). Cham: Springer. [pdf] [online]
  12. Dijk,J. van, Bargh,M., Choenni,S., & Spruit,M. (2017). Maturing Pay-as-you-go Data Management: Towards decision support for paying the larger bills. In Helfert,M., Holzinger,A., Belo,O., & Francalanci,C. (Eds.), Data Management Technologies and Applications: 5th International Conference, DATA 2016, Revised Selected papers (pp. 102–124). Springer. [pdf]
  13. Rijcken,E., Mosteiro,P., Zervanou,K., Spruit,M., Scheepers,Fl., & Kaymak,U. (2022). FuzzyTM: a Software Package for Fuzzy Topic Modeling. IEEE International Conference on Fuzzy Systems 2022 (pp. 1–8). IEEE WCCI 2022: FUZZ-IEEE, 18-23 July, Padua, Italy: IEEE.
  14. Rijcken,E., Scheepers, Mosteiro,P., Zervanou,K., Spruit,M., & Kaymak,U.,F. (2021). A Comparative Study of Fuzzy Topic Models and LDA for Interpretability in Text Classification. IEEE Symposium Series on Computational Intelligence (SSCI). Orlando, Florida. [pdf] [online]
  15. Homberg,M. van den, Monné,R., & Spruit,M. (2016). Bridging the Information Gap: Mapping Data Sets on Information Needs in the Preparedness and Response Phase. Proceedings of the UNESCO Chair in Technologies for Development: From Innovation to Social Impact, Tech4Dev: UNESCO Chair in Technologies for Development: From Innovation to Social Impact. [online]
  16. Haan,E. de, Spruit,M., & Zoet,M. (2019). Fundamental Constructs for Derivation Business Rules. Technical report UU-CS-2019-010, Department of Information and Computing Sciences, Utrecht University. [online]
  17. Shen, Z., Meulendijk,M., Knol,W., Huibers,L., Wilting,I., Jansen,P., & Spruit,M. (2016). STRIPA Investigational Medical Device Dossier (IMDD). version 2.03, Department of Information and Computing Sciences, Utrecht University.

Scheepers,F.

  1. Menger,V., Spruit,M., Est,R. van, Nap,E., & Scheepers,F. (2019). Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records. JAMA Network Open, 2(7), e196709. [JIF: 8.483] [pdf] [online]
  2. Borger,T., Mosteiro,P., Kaya,H., Rijcken,E., Salah,A., Scheepers,F., & Spruit,M. (2022). Federated Learning for Violence Incident Prediction in a Simulated Cross-institutional Psychiatric Setting. Expert Systems with Applications, 199, 116720. [JIF: 6.954] [pdf] [online]
  3. Menger,V., Scheepers,F., Wijk,L. van, & Spruit,M. (2018). DEDUCE: A pattern matching method for automatic de-identification of Dutch medical text. Telematics and Informatics, 35(4), Patient Centric Healthcare, 727–736. [JIF: 6.182] [pdf] [online]
  4. Spruit,M., Verkleij,S., Schepper,C. de, & Scheepers,F. (2022). Exploring Language Markers of Mental Health in Psychiatric Stories. Applied Sciences, 12(4), Current Approaches and Applications in Natural Language Processing, 2179. [JIF: 2.679] [pdf] [online]
  5. Menger,V., Scheepers,F., & Spruit,M. (2018). Comparing Deep Learning and Classical Machine Learning Approaches for Predicting Inpatient Violence Incidents from Clinical Text. Applied Sciences, 8(6), Data Analytics in Smart Healthcare, 981. [JIF: 2.679] [pdf] [online]
  6. Menger,V., Spruit,M., Hagoort,K., & Scheepers,F. (2016). Transitioning to a data driven mental health practice: collaborative expert sessions for knowledge and hypothesis finding. Computational and Mathematical Methods in Medicine, Article ID 9089321, 11. [JIF: 2.238] [pdf] [online]
  7. Mosteiro,P., Kuiper,J., Masthoff,J., Scheepers,F., & Spruit,M. (2022). Bias Discovery in Machine Learning Models for Mental Health. Information, 13(5), Advances in Explainable Artificial Intelligence, 237. [pdf] [online]
  8. Rijcken,E., Mosteiro,P., Zervanou,K., Spruit,M., Scheepers,F., & Kaymak,U. (2022). FuzzyTM: a Software Package for Fuzzy Topic Modeling. IEEE International Conference on Fuzzy Systems (pp. 1–8). FUZZ-IEEE, 18-23 July 2022, Padua, Italy: IEEE. [online]
  9. Rijcken,E., Zervanou,K., Spruit,M., Mosteiro,P., Scheepers,F., & Kaymak,U. (2022). Exploring Embedding Spaces for more Coherent Topic Modeling in Electronic Health Records. IEEE International Conference on Systems, Man, and Cybernetics (pp. 2669–2674). SMC 2022, Oct 9-12, 2022, Prague, Czech Republic: IEEE. [pdf] [online]
  10. Menger,V., Spruit,M., Bruin,J. de, Kelder,T., & Scheepers,F. (2019). Supporting Reuse of EHR Data in Healthcare Organizations: the CARED Research Infrastructure Framework. In Moucek,R., Fred,A., & Gamboa,H. (Eds.), Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 5 (pp. 41–50). HEALTHINF 2019, February 22-24, Prague, ScitePress. [pdf] [online]
  11. Menger,V., Spruit,M., Klift,W. van der, & Scheepers,F. (2019). Using Cluster Ensembles to Identify Psychiatric Patient Subgroups. In Riaño,D., Wilk,S., & ten Teije,A. (Eds.), Lecture Notes in Computer Science 11526, Artificial Intelligence in Medicine (pp. 252–262). AIME 2019, Poznan, Poland, June 26-29, 2019: Springer. [pdf] [online]
  12. Menger,V., Spruit,M., & Scheepers,F. (2021). Kennisontwikkeling in de klinische psychiatrie: leren van elektronische patiëntendossiers. Tijdschrift voor Psychiatrie, 63(4), 294–300. [pdf] [online]

Meulendijk,M.

  1. Meulendijk,M., Spruit,M., Willeboordse,F., Numans,M., Brinkkemper,S., Knol,W., Jansen,P., & Askari,M. (2016). Efficiency of clinical decision support systems improves with experience. Journal of Medical Systems, 40(4), 1–7. [JIF: 4.46] [pdf] [online]
  2. Meulendijk,M., Spruit,M., Drenth-van Maanen,C., Numans,M., Brinkkemper,S., Jansen,P., & Knol,W (2015). Computerized decision support improves medication review effectiveness: an experiment evaluating the STRIP Assistant’s usability. Drugs & Aging, 32(6), 495–503. [JIF: 3.923] [pdf] [online]
  3. Meulendijk,M., Spruit,M., Drenth-van-Maanen,A., Numans,M., Brinkkemper,S., & Jansen,P. (2013). General practitioners’ attitudes towards decision-supported prescribing: an analysis of the Dutch primary care sector. Health Informatics Journal, 19(4), 247–263. [JIF: 2.681] [pdf] [online]
  4. Meulendijk,M., Spruit,M., Lefebvre,A., & Brinkkemper,S. (2017). To what extent can prescriptions be meaningfully exchanged between primary care terminologies? A case study of four Western European classification systems. IET Software, 11(5), 256–264. [JIF: 1.363] [online]
  5. Meulendijk,M., Drenth-van-Maanen,A., Jansen,P., Brinkkemper,S., Numans,M., & Spruit,M. (2013). Introducing the CORETEST feasibility analysis in medical informatics: a case study of a decision-supportive knowledge system in the Dutch primary care sector. In Miranda,I., Cruz-Cunha,M., & Gonçalves,P. (Eds.), Handbook of Research on ICTs for Healthcare and Social Services: Developments and Applications (pp. 1066–1087). IGI Global. [pdf]
  6. Meulendijk,M., Spruit,M., & Brinkkemper,S. (2017). Risk mediation in association rules: the case of decision support in medication review. In Teije,A. ten, Popow,C., Holmes,J., & Sacchi,L. (Eds.), LNAI 10259, 16th Conference on Artificial Intelligence in Medicine (pp. 327 ff). AIME 2017, June 21-24, Vienna, Austria: Springer. [pdf] [online]
  7. Shen,Z., Meulendijk,M., & Spruit,M. (2016). A federated information architecture for multinational clinical trials: STRIPA revisited. 24th European Conference on Information Systems. Istanbul, Turkey. [pdf] [online]
  8. Meulendijk,M., Spruit,M., Numans,M., Brinkkemper,S., & Jansen,P. (2015). STRIPA: a rule-based decision support system for medication reviews in primary care. 23rd European Conference on Information Systems (pp. Paper 29). ECIS 2015, 26-29 May, 2015, Münster, Germany: AIS Electronic Library (AISeL). [pdf] [online]
  9. Meulendijk,M., Meulendijks,E., Jansen,P., Numans,M., & Spruit,M. (2014). What concerns users of medical apps? Exploring non-functional requirements of medical mobile applications. 22nd European Conference on Information Systems. Tel Aviv, Israel. [pdf] [online]
  10. Meulendijk,M., Spruit,M., & Brinkkemper,S. (2017). Risk Mediation in Association Rules: Application Examples. Technical report UU-CS-2017-004, Department of Information and Computing Sciences, Utrecht University. [online]

Helms,R.

  1. Otten,S., Spruit,M., & Helms,R. (2015). Towards decision analytics in product portfolio management. Decision Analytics, 2(4). [online]
  2. Bebensee,T., Helms,R., & Spruit,M. (2011). Exploring Web 2.0 Applications as a Mean of Bolstering up Knowledge Management. Electronic Journal of Knowledge Management, 9(1), ECKM Special Issue, 1–9. [pdf] [online]
  3. Faase,R., Helms,R., & Spruit,M. (2011). Web 2.0 In The CRM Domain: Defining Social CRM. International Journal of Electronic Customer Relationship Management, 5(1), 1–2. [pdf] [online]
  4. Bebensee,T., Helms,R., & Spruit,M. (2012). Exploring Web 2.0 Applications as a Mean of Bolstering up Knowledge Management. In Gurteen,David (Ed.), Leading Issues in Social Knowledge Management (pp. 22-41). Academic Publishing International. [pdf] [online]
  5. Bebensee,T., Helms,R., & Spruit,M. (2012). Exploring the Impact of Web 2.0 on Knowledge Management. In Boughzala,I., & Dudezert,A. (Eds.), Knowledge Management 2.0: Organizational Models and Enterprise Strategies (pp. 17–43). IGI Global. [pdf] [online]
  6. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]
  7. Helms,R., Booij,E., & Spruit,M. (2012). Reaching out: Involving users in innovation tasks through social media. Proceedings of the 20th European Conference on Information Systems (pp. Paper 193). ECIS 2012, June 10-13, 2012, Barcelona. [pdf] [online]
  8. Bebensee,T., Helms,R., & Spruit,M. (2010). Exploring Web 2.0 Applications as a Mean of Bolstering Up Knowledge Management in Non-Profit Organizations. 11th European Conference on Knowledge Management (pp. 65–73). ECKM, 2-3 September 2010, Universidade Lusíada de Vila Nova de Famalicão, Famalicão, Portugal. [pdf]
  9. Levantakis,T., Helms,R., & Spruit,M. (2008). Developing a Reference Method for Knowledge Auditing. In Yamagchi,T. (Ed.), Lecture Notes in Artificial Intelligence 5345, Proceedings of the 7th Conference of Practical Aspects on Knowledge Management (pp. 147–159), Appendices are available at http://m.spru.it/files/lhs2008pakm-appendix.pdf?attredirects=0&d=1. PAKM 2008, November 21-23, 2008, Yokohama, Japan: Springer. [pdf]

Menger,V.

  1. Menger,V., Spruit,M., Est,R. van, Nap,E., & Scheepers,F. (2019). Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records. JAMA Network Open, 2(7), e196709. [JIF: 8.483] [pdf] [online]
  2. Menger,V., Scheepers,F., Wijk,L. van, & Spruit,M. (2018). DEDUCE: A pattern matching method for automatic de-identification of Dutch medical text. Telematics and Informatics, 35(4), Patient Centric Healthcare, 727–736. [JIF: 6.182] [pdf] [online]
  3. Menger,V., Scheepers,F., & Spruit,M. (2018). Comparing Deep Learning and Classical Machine Learning Approaches for Predicting Inpatient Violence Incidents from Clinical Text. Applied Sciences, 8(6), Data Analytics in Smart Healthcare, 981. [JIF: 2.679] [pdf] [online]
  4. Menger,V., Spruit,M., Hagoort,K., & Scheepers,F. (2016). Transitioning to a data driven mental health practice: collaborative expert sessions for knowledge and hypothesis finding. Computational and Mathematical Methods in Medicine, Article ID 9089321, 11. [JIF: 2.238] [pdf] [online]
  5. Spruit,M., Kais,M., & Menger,V. (2021). Automated Business Goal Extraction from E-mail Repositories to Bootstrap Business Understanding. Future Internet, 13(10), Trends of Data Science and Knowledge Discovery, 243. [pdf] [online]
  6. Menger,V., Spruit,M., Bruin,J. de, Kelder,T., & Scheepers,F. (2019). Supporting Reuse of EHR Data in Healthcare Organizations: the CARED Research Infrastructure Framework. In Moucek,R., Fred,A., & Gamboa,H. (Eds.), Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 5 (pp. 41–50). HEALTHINF 2019, February 22-24, Prague, ScitePress. [pdf] [online]
  7. Menger,V., Spruit,M., Klift,W. van der, & Scheepers,F. (2019). Using Cluster Ensembles to Identify Psychiatric Patient Subgroups. In Riaño,D., Wilk,S., & ten Teije,A. (Eds.), Lecture Notes in Computer Science 11526, Artificial Intelligence in Medicine (pp. 252–262). AIME 2019, Poznan, Poland, June 26-29, 2019: Springer. [pdf] [online]
  8. Menger,V., Spruit,M., & Scheepers,F. (2021). Kennisontwikkeling in de klinische psychiatrie: leren van elektronische patiëntendossiers. Tijdschrift voor Psychiatrie, 63(4), 294–300. [pdf] [online]

Lefebvre,A.

  1. Lefebvre,A., & Spruit,M. (2021). Laboratory forensics for open science readiness: an investigative approach to research data management. Information Systems Frontiers. [JIF: 6.191] [pdf] [online]
  2. Meulendijk,M., Spruit,M., Lefebvre,A., & Brinkkemper,S. (2017). To what extent can prescriptions be meaningfully exchanged between primary care terminologies? A case study of four Western European classification systems. IET Software, 11(5), 256–264. [JIF: 1.363] [online]
  3. Lefebvre,A., Bakhtiari,B., & Spruit,M. (2020). Exploring Research Data Management Planning Challenges in Practice. it – Information Technology, 62(1), 29–37. [pdf] [online]
  4. Lefebvre,A., & Spruit,M. (2019). A Socio-Technical Perspective on Reproducibility Challenges in Research Data Management . 13th Mediterranean Conference on Information Systems. Napels, Italy. [pdf] [online]
  5. Lefebvre,A., & Spruit,M. (2019). Designing Laboratory Forensics. In Pappas,I. Mikalef,P., Dwivedi,Y., Jaccheri,L., Krogstie,J., Mäntymäki,M. (Eds.), Lecture Notes in Computer Science 11701, Digital Transformation for a Sustainable Society in the 21st Century. I3E 2019. Trondheim, Norway. [online]
  6. Lefebvre,A., Schermerhorn,E., & Spruit,M. (2018). How research data management can contribute to efficient and reliable science. 26th European Conference on Information Systems. Portsmouth, UK. [pdf]
  7. Lefebvre,A., Spruit,M., & Omta,W (2015). Towards reusability of computational experiments: Capturing and sharing Research Objects from knowledge discovery processes. Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 456–462). KDIR 2015, November 12-14, Lisbon, Portugal: ScitePress. [pdf] [online]
  8. Lefebvre,A., Berendsen,J., & Spruit,M. (2019). Evaluation of classification models for retrieving experimental sections from full-text publications. Technical report UU-CS-2019-002, Department of Information and Computing Sciences, Utrecht University. [online]

Yigit Ozkan,B.

  1. Yigit Ozkan,B., Spruit,M., Wondolleck,R., & Burriel Coll,V. (2019). Modelling adaptive information security for SMEs in a cluster. Journal of Intellectual Capital, 21(2), 235–256. [JIF: 7.198] [pdf] [online]
  2. Yigit Ozkan,B., van Lingen,S., & Spruit,M. (2021). The Cybersecurity Focus Area Maturity (CYSFAM) Model. Journal of Cybersecurity and Privacy, 1, 119–140. [pdf] [online]
  3. Yigit Ozkan,B., & Spruit,M (2019). Cybersecurity Standardisation for SMEs: The Stakeholders’ Perspectives and a Research Agenda. International Journal of Standardization Research, 17(2), 1–25. [pdf] [online]
  4. Yigit Ozkan,B., & Spruit,M. (2020). Addressing SME Characteristics for Designing Information Security Maturity Models . In Clarke N., Furnell S. (Eds.), IFIP Advances in Information and Communication Technology: Human Aspects of Information Security and Assurance (pp. 161–174). HAISA 2020, 8-10 July, Online: IFIP. [pdf] [online]
  5. Yigit Ozkan,B., & Spruit,M (2019). A Questionnaire Model for Cybersecurity Maturity Assessment for Critical Infrastructures. In Fournaris,A., Lampropoulos,K., & Tordera,E. (Eds.), Lecture Notes in Computer Science (LNCS) 11398 11398, Information and Operational Technology Security Systems. First International Workshop, IOSec 2018, CIPSEC Project (pp. 49–60). IOSec 2018, 13 Sept 2018, Heraklion, Crete, Greece: Springer. [pdf] [online]
  6. Yigit Ozkan,B., & Spruit,M. (2018). Assessing and Improving Cybersecurity Maturity for SMEs: Standardization aspects. 1st SMESEC Workshop. 1st SMESEC Workshop, September 14, 2018. Also published as arXiv:2007.01751 [cs.CR]. [pdf] [online]
  7. Spruit,M, Lingen,S. van, & Yigit Ozkan,B. (2019). The CYSFAM Questionnaire: Assessing CYberSecurity Focus Area Maturity. Technical report UU-CS-2019-003, Department of Information and Computing Sciences, Utrecht University. [online]

Mosteiro,P.

  1. Borger,T., Mosteiro,P., Kaya,H., Rijcken,E., Salah,A., Scheepers,F., & Spruit,M. (2022). Federated Learning for Violence Incident Prediction in a Simulated Cross-institutional Psychiatric Setting. Expert Systems with Applications, 199, 116720. [JIF: 6.954] [pdf] [online]
  2. Mosteiro,P., Kuiper,J., Masthoff,J., Scheepers,F., & Spruit,M. (2022). Bias Discovery in Machine Learning Models for Mental Health. Information, 13(5), Advances in Explainable Artificial Intelligence, 237. [pdf] [online]
  3. Mosteiro,P., Rijcken,E., Zervanou,K., Kaymak,U., Scheepers,F., & Spruit,M. (2021). Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes. Journal of Artificial Intelligence for Medical Sciences, 2(1–2), 44–54. [pdf] [online]
  4. Rijcken,E., Mosteiro,P., Zervanou,K., Spruit,M., Scheepers,F., & Kaymak,U. (2022). FuzzyTM: a Software Package for Fuzzy Topic Modeling. IEEE International Conference on Fuzzy Systems (pp. 1–8). FUZZ-IEEE, 18-23 July 2022, Padua, Italy: IEEE. [online]
  5. Rijcken,E., Zervanou,K., Spruit,M., Mosteiro,P., Scheepers,F., & Kaymak,U. (2022). Exploring Embedding Spaces for more Coherent Topic Modeling in Electronic Health Records. IEEE International Conference on Systems, Man, and Cybernetics (pp. 2669–2674). SMC 2022, Oct 9-12, 2022, Prague, Czech Republic: IEEE. [pdf] [online]
  6. Sarhan,I., Mosteiro,P., & Spruit,M. (2022). UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation. Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (pp. 271–281). SemEval 2022, 15 July 2022, Seattle, Washington, United States: ACL. [pdf] [online]
  7. Mosteiro,P., Rijcken,E., Zervanou,K., Kaymak,U., Scheepers,F., & Spruit,M. (2020). Making sense of violence risk predictions using clinical notes. In Huang,Z, Siuly,S., Wang,H., Zhou,R., & Zhang,Y. (Eds.), Lecture Notes in Computer Science 12435, Health Information Science: 9th International Conference (pp. 3–14). HIS 2020, Leiden: Springer. [pdf] [online]

Tawfik,N.

  1. Tawfik,N., & Spruit,M. (2020). Evaluating Sentence Representations for Biomedical Text: Methods and Experimental Results. Journal of Biomedical Informatics, 104(April), 103396. [JIF: 6.317] [pdf] [online]
  2. Tawfik,N., & Spruit,M. (2018). The SNPCurator: Literature mining of SNP disease association. Database: The Journal of Biological Databases and Curation, 2018(January), bay020. [JIF: 3.451] [pdf] [online]
  3. Tawfik,N., & Spruit,M. (2020). Computer-Assisted Relevance Assessment: A Case Study of Updating Systematic Medical Reviews. Applied Sciences, 10(8), Data Technology Applications in Life, Diseases, and Health, 2845. [JIF: 2.679] [pdf] [online]
  4. Tawfik,N., & Spruit,M. (2019). UU_TAILS at 2019 MEDIQA Challenge: Learning Textual Entailment in the Medical Domain. Proceedings of the BioNLP 2019 workshop (pp. 493–499). BioNLP 2019, August 1, 2019, Florence, Italy: Association for Computational Linguistics (ACL). [pdf] [online]
  5. Tawfik,N., & Spruit,M. (2019). Towards Recognition of Textual Entailment in the Biomedical Domain. In Métais, E. et al. (Eds.), Lecture Notes in Computer Science 11608, NLDB 2019: International Conference on Applications of Natural Language to Information Systems (pp. 368–375). NLDB 2019, University of Salford, MediaCityUK Campus, United Kingdom, 26–28 June 2019: Springer. [pdf] [online]
  6. Tawfik,N., & Spruit,M. (2019). PreMedOnto: A Computer Assisted Ontology for Precision Medicine. In Métais, E. et al. (Eds.), Lecture Notes in Computer Science 11608, NLDB 2019: International Conference on Applications of Natural Language to Information Systems (pp. 329–336). NLDB 2019, University of Salford, MediaCityUK Campus, United Kingdom, 26–28 June 2019: Springer. [pdf] [online]
  7. Tawfik,N., & Spruit,M. (2018). Automated Contradiction Detection in Biomedical Literature. In Perner,P. (Ed.), 14th International Conference on Machine Learning and Data Mining (pp. 138–148). MLDM 2018, July 14-19, 2018, New York, NY, United States. [pdf] [online]

Sarhan,I.

  1. Sarhan,I., & Spruit,M. (2021). Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph. Knowledge-Based Systems, 233(107524). [JIF: 8.038] [pdf] [online]
  2. Sarhan,I., & Spruit,M. (2020). Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction . Applied Sciences, 10(17), Natural Language Processing: Emerging Neural Approaches and Applications, 5758. [JIF: 2.679] [pdf] [online]
  3. Sarhan,I., Mosteiro,P., & Spruit,M. (2022). UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation. Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (pp. 271–281). SemEval 2022, 15 July 2022, Seattle, Washington, United States: ACL. [pdf] [online]
  4. Haastrecht,M. van, Sarhan,I., Shojaifar,A., Baumgartner,L., Mallouli,W., & Spruit,M. (2021). A Threat-Based Cybersecurity Risk Assessment Approach Addressing SME Needs. 16th International Conference on Availability, Reliability and Security (ARES 2021), International Workshop on Security and Privacy for SMEs (pp. Paper 230). SME-SP 2021 at ARES 2021, Aug 17-20, 2021, Vienna, Austria: ACM. [pdf] [online]
  5. Sarhan,I., & Spruit,M. (2019). Contextualized Word Embeddings in a Neural Open Information Extraction Model. In Métais, E. et al. (Eds.), Lecture Notes in Computer Science 11608, NLDB 2019: International Conference on Applications of Natural Language to Information Systems (pp. 359–367). NLDB 2019, University of Salford, MediaCityUK Campus, United Kingdom, 26–28 June 2019: Springer. [pdf] [online]
  6. Sarhan,I., & Spruit,M. (2018). Uncovering Algorithmic Approaches in Open Information Extraction: A Literature Review. In Atzmueller, M., & Duivesteijn,W. (Eds.), 30th Benelux Conference on Artificial Intelligence (pp. 223–234). BNAIC, November 8-9, 2018, ‘s-Hertogenbosch, Netherlands: Springer CSAI / JADS. [pdf]

Syed,S.

  1. Syed,S., Aodha,L., Scougal,C., & Spruit,M. (2019). Mapping the global network of fisheries science collaboration. Fish and Fisheries, 20(5), 830–856. [JIF: 7.218] [pdf] [online]
  2. Syed,S., Borit,M., & Spruit,M. (2018). Narrow lenses for capturing the complexity of fisheries: A topic analysis of fisheries science from 1990 to 2016. Fish and Fisheries, 19(4), 643–661. [JIF: 7.218] [pdf] [online]
  3. Syed,S., & Spruit,M. (2018). Exploring Symmetrical and Asymmetrical Dirichlet Priors for Latent Dirichlet Allocation. International Journal of Semantic Computing, 12(3), 1–25. [pdf] [online]
  4. Syed,S., & Spruit,M. (2018). Selecting Priors for Latent Dirichlet Allocation. 12th IEEE International Conference on Semantic Computing (pp. 194-202). IEEE ICSC2018, Jan 31-Feb 2, 2018, Laguna Hills, California, USA. [pdf] [online]
  5. Syed,S., & Spruit,M. (2017). Full Text or Abstract - Examining Topic Coherence Scores Using Latent Dirichlet Allocation. 4th IEEE International Conference on Data Science and Advanced Analytics (pp. 165–174). DSAA 2017, Oct 19-21, 2017, Tokyo, Japan: IEEE. [pdf] [online]
  6. Syed,S., Spruit,M., & Borit,M. (2016). Bootstrapping a Semantic Lexicon on Verb Similarities. In Fred,A. et al. (Ed.), Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 189–196). KDIR 2016, November 11-13, 2016, Porto, Portugal: ScitePress. [pdf] [online]

Rijcken,E.

  1. Borger,T., Mosteiro,P., Kaya,H., Rijcken,E., Salah,A., Scheepers,F., & Spruit,M. (2022). Federated Learning for Violence Incident Prediction in a Simulated Cross-institutional Psychiatric Setting. Expert Systems with Applications, 199, 116720. [JIF: 6.954] [pdf] [online]
  2. Rijcken,E., Kaymak,U., Scheepers,F., Mosteiro,P., Zervanou,K., & Spruit,M. (2022). Topic Modeling for Interpretable Text Classification from EHRs. Frontiers in Big Data, 5, Section Data Mining and Management, 846930. [pdf] [online]
  3. Rijcken,E., Mosteiro,P., Zervanou,K., Spruit,M., Scheepers,Fl., & Kaymak,U. (2022). FuzzyTM: a Software Package for Fuzzy Topic Modeling. IEEE International Conference on Fuzzy Systems 2022 (pp. 1–8). IEEE WCCI 2022: FUZZ-IEEE, 18-23 July, Padua, Italy: IEEE.
  4. Rijcken,E., Mosteiro,P., Zervanou,K., Spruit,M., Scheepers,F., & Kaymak,U. (2022). FuzzyTM: a Software Package for Fuzzy Topic Modeling. IEEE International Conference on Fuzzy Systems (pp. 1–8). FUZZ-IEEE, 18-23 July 2022, Padua, Italy: IEEE. [online]
  5. Rijcken,E., Zervanou,K., Spruit,M., Mosteiro,P., Scheepers,F., & Kaymak,U. (2022). Exploring Embedding Spaces for more Coherent Topic Modeling in Electronic Health Records. IEEE International Conference on Systems, Man, and Cybernetics (pp. 2669–2674). SMC 2022, Oct 9-12, 2022, Prague, Czech Republic: IEEE. [pdf] [online]
  6. Rijcken,E., Scheepers, Mosteiro,P., Zervanou,K., Spruit,M., & Kaymak,U.,F. (2021). A Comparative Study of Fuzzy Topic Models and LDA for Interpretability in Text Classification. IEEE Symposium Series on Computational Intelligence (SSCI). Orlando, Florida. [pdf] [online]

Numans,M.

  1. Meulendijk,M., Spruit,M., Willeboordse,F., Numans,M., Brinkkemper,S., Knol,W., Jansen,P., & Askari,M. (2016). Efficiency of clinical decision support systems improves with experience. Journal of Medical Systems, 40(4), 1–7. [JIF: 4.46] [pdf] [online]
  2. Meulendijk,M., Spruit,M., Drenth-van Maanen,C., Numans,M., Brinkkemper,S., Jansen,P., & Knol,W (2015). Computerized decision support improves medication review effectiveness: an experiment evaluating the STRIP Assistant’s usability. Drugs & Aging, 32(6), 495–503. [JIF: 3.923] [pdf] [online]
  3. Meulendijk,M., Spruit,M., Drenth-van-Maanen,A., Numans,M., Brinkkemper,S., & Jansen,P. (2013). General practitioners’ attitudes towards decision-supported prescribing: an analysis of the Dutch primary care sector. Health Informatics Journal, 19(4), 247–263. [JIF: 2.681] [pdf] [online]
  4. Meulendijk,M., Drenth-van-Maanen,A., Jansen,P., Brinkkemper,S., Numans,M., & Spruit,M. (2013). Introducing the CORETEST feasibility analysis in medical informatics: a case study of a decision-supportive knowledge system in the Dutch primary care sector. In Miranda,I., Cruz-Cunha,M., & Gonçalves,P. (Eds.), Handbook of Research on ICTs for Healthcare and Social Services: Developments and Applications (pp. 1066–1087). IGI Global. [pdf]
  5. Meulendijk,M., Spruit,M., Numans,M., Brinkkemper,S., & Jansen,P. (2015). STRIPA: a rule-based decision support system for medication reviews in primary care. 23rd European Conference on Information Systems (pp. Paper 29). ECIS 2015, 26-29 May, 2015, Münster, Germany: AIS Electronic Library (AISeL). [pdf] [online]
  6. Meulendijk,M., Meulendijks,E., Jansen,P., Numans,M., & Spruit,M. (2014). What concerns users of medical apps? Exploring non-functional requirements of medical mobile applications. 22nd European Conference on Information Systems. Tel Aviv, Israel. [pdf] [online]

Jansen,P.

  1. Meulendijk,M., Spruit,M., Willeboordse,F., Numans,M., Brinkkemper,S., Knol,W., Jansen,P., & Askari,M. (2016). Efficiency of clinical decision support systems improves with experience. Journal of Medical Systems, 40(4), 1–7. [JIF: 4.46] [pdf] [online]
  2. Meulendijk,M., Spruit,M., Drenth-van Maanen,C., Numans,M., Brinkkemper,S., Jansen,P., & Knol,W (2015). Computerized decision support improves medication review effectiveness: an experiment evaluating the STRIP Assistant’s usability. Drugs & Aging, 32(6), 495–503. [JIF: 3.923] [pdf] [online]
  3. Meulendijk,M., Spruit,M., Drenth-van-Maanen,A., Numans,M., Brinkkemper,S., & Jansen,P. (2013). General practitioners’ attitudes towards decision-supported prescribing: an analysis of the Dutch primary care sector. Health Informatics Journal, 19(4), 247–263. [JIF: 2.681] [pdf] [online]
  4. Meulendijk,M., Drenth-van-Maanen,A., Jansen,P., Brinkkemper,S., Numans,M., & Spruit,M. (2013). Introducing the CORETEST feasibility analysis in medical informatics: a case study of a decision-supportive knowledge system in the Dutch primary care sector. In Miranda,I., Cruz-Cunha,M., & Gonçalves,P. (Eds.), Handbook of Research on ICTs for Healthcare and Social Services: Developments and Applications (pp. 1066–1087). IGI Global. [pdf]
  5. Meulendijk,M., Spruit,M., Numans,M., Brinkkemper,S., & Jansen,P. (2015). STRIPA: a rule-based decision support system for medication reviews in primary care. 23rd European Conference on Information Systems (pp. Paper 29). ECIS 2015, 26-29 May, 2015, Münster, Germany: AIS Electronic Library (AISeL). [pdf] [online]
  6. Meulendijk,M., Meulendijks,E., Jansen,P., Numans,M., & Spruit,M. (2014). What concerns users of medical apps? Exploring non-functional requirements of medical mobile applications. 22nd European Conference on Information Systems. Tel Aviv, Israel. [pdf] [online]

Omta,W.

  1. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]
  2. Omta,W., Nobel,J. de, Klumperman,J., Egan,D., Spruit.M., & Brinkhuis,M. (2017). Improving Comprehension Efficiency of HCS Data Through Interactive Visualizations. ASSAY and Drug Development Technologies, 15(6), 247–256. [JIF: 1.738] [online]
  3. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]
  4. Omta,W., Heesbeen,R. van, Shen,I., Feelders,A., Brinkhuis,M., Egan,D., & Spruit,M. (2020). PurifyR: An R Package for Highly Automated Reproducible Variable Extraction and Standardization. Systems Medicine, 3(1), Integrative Data Analysis in Systems Medicine, 1–7. [pdf] [online]
  5. Omta,W., Egan,D., Klumperman,J., Spruit,M., & Brinkkemper,S. (2013). HTS-IA: High Throughput Screening Information Architecture for Genomics. International Journal of Healthcare Information Systems and Informatics, 8(4), 17–31. [online]
  6. Omta,W., Egan,D., Spruit,M., & Brinkkemper,S. (2012). Information Architecture in High Throughput Screening. Procedia Technology, 5, 696–705. [pdf] [online]

Shen,Z.

  1. Shen,Z., Krimpen,H. van, & Spruit,M. (2019). A lightweight API-based approach for building flexible clinical NLP systems. Journal of Healthcare Engineering, Article ID 3435609, 11. [JIF: 2.682] [pdf] [online]
  2. Shen,Z., & Spruit,M. (2021). Automatic Extraction of Adverse Drug Reactions from Summary of Product Characteristics. Applied Sciences, 11(6), Applications of Artificial Intelligence in Pharmaceutics, 2663. [JIF: 2.679] [pdf] [online]
  3. Shen,Z., & Spruit,M. (2019). A systematic review on open source clinical software on GitHub for improving software reuse in smart healthcare . Applied Sciences, 9, Data Analytics in Smart Healthcare, 150. [JIF: 2.679] [pdf] [online]
  4. Shen,Z., & Spruit,M. (2019). LOCATE: A web application to link open-source clinical software with literature. In Moucek,R., Fred,A., & Gamboa,H. (Eds.), Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 5 (pp. 294–301). HEALTHINF 2019, February 22-24, Prague, ScitePress. [pdf] [online]
  5. Shen,Z., Wang,X., & Spruit,M. (2019). Big Data Framework for Scalable and Efficient Biomedical Literature Mining in the Cloud. International Conference Proceedings Series by ACM, NLPIR 2019: Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval (pp. 80–86). NLPIR 2019, Tokushima, Japan: ACM. [pdf] [online]
  6. Shen,Z., Meulendijk,M., & Spruit,M. (2016). A federated information architecture for multinational clinical trials: STRIPA revisited. 24th European Conference on Information Systems. Istanbul, Turkey. [pdf] [online]

Baars,T.

  1. Mijnhardt,F., Baars,T., & Spruit,M. (2016). Organizational Characteristics Influencing SME Information Security Maturity. Journal of Computer Information Systems, 56(2), 106–115. [JIF: 3.4] [pdf] [online]
  2. Baars,T., & Spruit,M. (2012). Analysing the Security Risks of Cloud Adoption Using the SeCA Model: A Case Study. Journal of Universal Computer Science, 18(12), Security in Information Systems, 1662–1678. [JIF: 1.139] [pdf] [online]
  3. Baars,T., Mijnhardt,F., Vlaanderen,K., & Spruit,M. (2016). An Analytics Approach to Adaptive Maturity Models using Organizational Characteristics. Decision Analytics, 3(5). [pdf] [online]
  4. Baars,T., & Spruit,M. (2012). Designing a Secure Cloud Architecture: The SeCA Model. International Journal of Information Security and Privacy, 6(1), January-March 2012, 14–32. [pdf] [online]
  5. Baars,T., & Spruit,M. (2013). The SeCA model: Ins & Outs of a Secure Cloud Architecture. In Rosado,D., Mellado,D., Fernandez-Medina,E., & Piattini,M. (Eds.), Security Engineering for Cloud Computing: Approaches and Tools (pp. 19-35). IGI Global. [pdf] [online]

Brinkhuis,M.

  1. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]
  2. Haastrecht,M. van, Yigit Ozkan,B., Brinkhuis,M., & Spruit,M. (2021). Respite for SMEs: A Systematic Review of Socio-Technical Cybersecurity Metrics. Applied Sciences, 11(15), Human Factors in the Digital Society, 6909. [JIF: 2.679] [pdf] [online]
  3. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]
  4. van Dijk,F., Gadellaa,J., Spruit,M., van Toledo,C., Brinkkemper,S., & Brinkhuis,M. (In press). Uncovering the Structures of Privacy Research using Bibliometric Network Analysis and Topic Modelling. Organizational Cybersecurity Journal: Practice, Process and People.
  5. Haastrecht,M. van, Sarhan,I., Yigit Ozkan,B., Brinkhuis,M., & Spruit,M. (2021). SYMBALS: A Systematic Review Methodology Blending Active Learning and Snowballing. Frontiers in Research Metrics and Analytics, 6, Section Text-mining and Literature-based Discovery. [pdf] [online]

Haastrecht,M. van

  1. Haastrecht,M. van, Yigit Ozkan,B., Brinkhuis,M., & Spruit,M. (2021). Respite for SMEs: A Systematic Review of Socio-Technical Cybersecurity Metrics. Applied Sciences, 11(15), Human Factors in the Digital Society, 6909. [JIF: 2.679] [pdf] [online]
  2. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]
  3. Haastrecht,M. van, Sarhan,I., Yigit Ozkan,B., Brinkhuis,M., & Spruit,M. (2021). SYMBALS: A Systematic Review Methodology Blending Active Learning and Snowballing. Frontiers in Research Metrics and Analytics, 6, Section Text-mining and Literature-based Discovery. [pdf] [online]
  4. Smit,T., Haastrecht,M. van, & Spruit,M. (2021). The Effect of Countermeasure Readability on Security Intentions. Journal of Cybersecurity and Privacy, 1, Cyber Situational Awareness Techniques and Human Factors, 675–704. [pdf] [online]
  5. Haastrecht,M. van, Sarhan,I., Shojaifar,A., Baumgartner,L., Mallouli,W., & Spruit,M. (2021). A Threat-Based Cybersecurity Risk Assessment Approach Addressing SME Needs. 16th International Conference on Availability, Reliability and Security (ARES 2021), International Workshop on Security and Privacy for SMEs (pp. Paper 230). SME-SP 2021 at ARES 2021, Aug 17-20, 2021, Vienna, Austria: ACM. [pdf] [online]

Egan,D.

  1. Omta,W., Nobel,J. de, Klumperman,J., Egan,D., Spruit.M., & Brinkhuis,M. (2017). Improving Comprehension Efficiency of HCS Data Through Interactive Visualizations. ASSAY and Drug Development Technologies, 15(6), 247–256. [JIF: 1.738] [online]
  2. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]
  3. Omta,W., Heesbeen,R. van, Shen,I., Feelders,A., Brinkhuis,M., Egan,D., & Spruit,M. (2020). PurifyR: An R Package for Highly Automated Reproducible Variable Extraction and Standardization. Systems Medicine, 3(1), Integrative Data Analysis in Systems Medicine, 1–7. [pdf] [online]
  4. Omta,W., Egan,D., Klumperman,J., Spruit,M., & Brinkkemper,S. (2013). HTS-IA: High Throughput Screening Information Architecture for Genomics. International Journal of Healthcare Information Systems and Informatics, 8(4), 17–31. [online]
  5. Omta,W., Egan,D., Spruit,M., & Brinkkemper,S. (2012). Information Architecture in High Throughput Screening. Procedia Technology, 5, 696–705. [pdf] [online]

Brinkhuis,M.

  1. Omta,W., Nobel,J. de, Klumperman,J., Egan,D., Spruit.M., & Brinkhuis,M. (2017). Improving Comprehension Efficiency of HCS Data Through Interactive Visualizations. ASSAY and Drug Development Technologies, 15(6), 247–256. [JIF: 1.738] [online]
  2. Toledo, C. van, Schraagen,M., Dijk,F. van, Brinkhuis,M., & Spruit,M. (2022). Exploring the Utility of Dutch Question Answering Datasets for Human Resource Contact Centres. Information, 13(11), Novel Methods and Applications in Natural Language Processing, 513. [pdf] [online]
  3. Omta,W., Heesbeen,R. van, Shen,I., Feelders,A., Brinkhuis,M., Egan,D., & Spruit,M. (2020). PurifyR: An R Package for Highly Automated Reproducible Variable Extraction and Standardization. Systems Medicine, 3(1), Integrative Data Analysis in Systems Medicine, 1–7. [pdf] [online]
  4. Haastrecht,M., Brinkhuis,M., Peichl,J., Remmele,B., & Spruit,M. (In press). Embracing Trustworthiness and Authenticity in the Validation of Learning Analytics Systems. 13th International Conference on Learning Analytics and Knowledge. Arlington, Texas, USA. [pdf]
  5. Dijk,F. van, Spruit,M., Toledo,C. van, & Brinkhuis,M. (2021). Pillars of Privacy: Identifying Core Theory in a Network Analysis of Privacy. 29th European Conference on Information Systems. Marrakech, Morocco. [pdf] [online]

Pachidi,S.

  1. Pachidi,S., Spruit,M., & Weerd,I. van der (2014). Understanding Users' Behavior with Software Operation Data Mining. Computers in Human Behavior, 30, ICTs for Human Capital, 583–594. [JIF: 6.829] [pdf] [online]
  2. Pachidi,S., & Spruit,M. (2015). The Performance Mining method: Extracting performance knowledge from software operation data. International Journal of Business Intelligence Research, 6(1), 11–29. [pdf] [online]
  3. Pachidi,S., & Spruit,M. (2016). The Performance Mining method: Extracting performance knowledge from software operation data. In Information Science Reference (Ed.), Big Data: Concepts, Methodologies, Tools, and Applications (pp. 181–199). Hershey, PA: IGI Global. [online]
  4. Pachidi,S., & Spruit,M. (2012). Mining Performance Knowledge in User Logs. Proceedings of the 5th World Summit on the Knowledge Society (pp. Paper 47). WSKS 2012, June 20-22, 2012, Rome, Italy. [pdf] [online]

Batenburg,R.

  1. Spruit,M., Vroon,R., & Batenburg,R. (2014). Towards healthcare business intelligence in long-term care: an explorative case study in the Netherlands. Computers in Human Behavior, 30, ICTs for Human Capital, 698–707. [JIF: 6.829] [pdf] [online]
  2. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]
  3. Aarnoutse,F., Renes,C., Batenburg,R., & Spruit,M. (2016). STRIPA: The potential usefulness of a medical app. In Gasmelseid,T. (Ed.), Advancing Pharmaceutical Processes and Tools for Improved Health Outcomes (pp. 114–135). IGI Global. [pdf] [online]
  4. Peersman,H., Batenburg,R., & Spruit,M. (2013). Preventing credit card data breaches. A framework of critical indicators. In Shahim,Abbas (Ed.), IFIP TC11 Conference on IT Assurance and Audit. VU University Amsterdam. [pdf]

Scheper,W.

  1. Wijaya,S., Spruit,M., Scheper,W., & Versendaal,J. (2011). Web 2.0-based Webstrategies for Three Different Types of Organizations. Computers in Human Behavior, 27(4), 1399–1407. [JIF: 6.829] [pdf] [online]
  2. Knol,P., Spruit,M., & Scheper,W. (2010). The Emerging Value of Social Computing in Business Model Innovation. In Lytras,M., Ordoñez de Pablos,P., Lee,W., & Karwowski,W. (Eds.), Electronic Globalized Business And Sustainable Development Through IT Management: Strategies And Perspectives (pp. 112–134). IGI Global. [pdf] [online]
  3. Knol,P., Spruit,M., & Scheper,W. (2008). Web 2.0 Revealed - Business Model Innovation through Social Computing. Proceedings of the Seventh AIS SIGeBIZ Workshop on e-business. Paris, France. [pdf]
  4. Wijaya,S., Spruit,M., & Scheper,W. (2008). Webstrategy Formulation: benefiting from web 2.0 concepts to deliver business values. In Lytras, M., Carroll, J., Damiani,E., & Tennyson,R. (Eds.), Lecture Notes in Computer Science 5288, Emerging Technologies and Information Systems for the Knowledge Society (pp. 373–384). WSKS 2008, September 24-26, 2008, Athens, Greece: Springer. [pdf]

Sacu,C.

  1. Spruit,M., & Sacu,C. (2015). DWCMM: The Data Warehouse Capability Maturity Model. Journal of Universal Computer Science, 21(11), 1508-1534. [JIF: 1.139] [pdf] [online]
  2. Sacu,C., & Spruit,M. (2010). BIDM: The Business Intelligence development model. 12th International Conference on Enterprise Information Systems (pp. 288–293). ICEIS, 8- 12 June, 2010, Funchal, Madeira, Portugal. [pdf] [online]
  3. Sacu,C., & Spruit,M. (2010). BIDM: The Business Intelligence development model. Technical report UU-CS-2010-010, Department of Information and Computing Sciences, Utrecht University. [pdf] [online]
  4. Sacu,C., Spruit,M., & Habers,F. (2010). Data Warehouse Maturity Assessment Questionnaire. Technical report UU-CS-2010-021, Department of Information and Computing Sciences, Utrecht University. [pdf] [online]

Kaymak,U.

  1. Rijcken,E., Kaymak,U., Scheepers,F., Mosteiro,P., Zervanou,K., & Spruit,M. (2022). Topic Modeling for Interpretable Text Classification from EHRs. Frontiers in Big Data, 5, Section Data Mining and Management, 846930. [pdf] [online]
  2. Mosteiro,P., Rijcken,E., Zervanou,K., Kaymak,U., Scheepers,F., & Spruit,M. (2021). Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes. Journal of Artificial Intelligence for Medical Sciences, 2(1–2), 44–54. [pdf] [online]
  3. Rijcken,E., Mosteiro,P., Zervanou,K., Spruit,M., Scheepers,Fl., & Kaymak,U. (2022). FuzzyTM: a Software Package for Fuzzy Topic Modeling. IEEE International Conference on Fuzzy Systems 2022 (pp. 1–8). IEEE WCCI 2022: FUZZ-IEEE, 18-23 July, Padua, Italy: IEEE.
  4. Mosteiro,P., Rijcken,E., Zervanou,K., Kaymak,U., Scheepers,F., & Spruit,M. (2020). Making sense of violence risk predictions using clinical notes. In Huang,Z, Siuly,S., Wang,H., Zhou,R., & Zhang,Y. (Eds.), Lecture Notes in Computer Science 12435, Health Information Science: 9th International Conference (pp. 3–14). HIS 2020, Leiden: Springer. [pdf] [online]

Dijk,F. van

  1. Toledo, C. van, Schraagen,M., Dijk,F. van, Brinkhuis,M., & Spruit,M. (2022). Exploring the Utility of Dutch Question Answering Datasets for Human Resource Contact Centres. Information, 13(11), Novel Methods and Applications in Natural Language Processing, 513. [pdf] [online]
  2. Toledo,C. van, Dijk,F. van, & Spruit,M. (2020). Dutch Named Entity Recognition and De-identification Methods for the Human Resource Domain. International Journal on Natural Language Computing, 9(6). [online]
  3. Dijk,F. van, Spruit,M., Toledo,C. van, & Brinkhuis,M. (2021). Pillars of Privacy: Identifying Core Theory in a Network Analysis of Privacy. 29th European Conference on Information Systems. Marrakech, Morocco. [pdf] [online]
  4. Toledo,C. van, Dijk,F. van, & Spruit,M. (2020). Evaluating Dutch Named Entity Recognition and De-identification Methods in the Human Resources Domain. In Wyld,D. et al. (Ed.), Proceedings of the International Conference on NLP Techniques and Applications (pp. 239–249). NLPTA 2020, 28-29 Nov 2020, London, United Kingdom: AIRCC Publishing Corporation. [pdf]

Zervanou,K.

  1. Mosteiro,P., Rijcken,E., Zervanou,K., Kaymak,U., Scheepers,F., & Spruit,M. (2021). Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes. Journal of Artificial Intelligence for Medical Sciences, 2(1–2), 44–54. [pdf] [online]
  2. Rijcken,E., Mosteiro,P., Zervanou,K., Spruit,M., Scheepers,Fl., & Kaymak,U. (2022). FuzzyTM: a Software Package for Fuzzy Topic Modeling. IEEE International Conference on Fuzzy Systems 2022 (pp. 1–8). IEEE WCCI 2022: FUZZ-IEEE, 18-23 July, Padua, Italy: IEEE.
  3. Rijcken,E., Scheepers, Mosteiro,P., Zervanou,K., Spruit,M., & Kaymak,U.,F. (2021). A Comparative Study of Fuzzy Topic Models and LDA for Interpretability in Text Classification. IEEE Symposium Series on Computational Intelligence (SSCI). Orlando, Florida. [pdf] [online]
  4. Mosteiro,P., Rijcken,E., Zervanou,K., Kaymak,U., Scheepers,F., & Spruit,M. (2020). Making sense of violence risk predictions using clinical notes. In Huang,Z, Siuly,S., Wang,H., Zhou,R., & Zhang,Y. (Eds.), Lecture Notes in Computer Science 12435, Health Information Science: 9th International Conference (pp. 3–14). HIS 2020, Leiden: Springer. [pdf] [online]

Toledo,C. van

  1. Toledo,C. van, Dijk,F. van, & Spruit,M. (2020). Dutch Named Entity Recognition and De-identification Methods for the Human Resource Domain. International Journal on Natural Language Computing, 9(6). [online]
  2. Dijk,F. van, Spruit,M., Toledo,C. van, & Brinkhuis,M. (2021). Pillars of Privacy: Identifying Core Theory in a Network Analysis of Privacy. 29th European Conference on Information Systems. Marrakech, Morocco. [pdf] [online]
  3. Toledo,C. van, Dijk,F. van, & Spruit,M. (2020). Evaluating Dutch Named Entity Recognition and De-identification Methods in the Human Resources Domain. In Wyld,D. et al. (Ed.), Proceedings of the International Conference on NLP Techniques and Applications (pp. 239–249). NLPTA 2020, 28-29 Nov 2020, London, United Kingdom: AIRCC Publishing Corporation. [pdf]
  4. Toledo,C. van, & Spruit,M. (2016). Adopting privacy regulations in a data warehouse: A case of the anonimity versus utility dilemma. In Fred,A. et al. (Ed.), Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 67–72). KDIR 2016, November 11-13, 2016, Porto, Portugal: ScitePress. [pdf]

Bebensee,T.

  1. Bebensee,T., Helms,R., & Spruit,M. (2011). Exploring Web 2.0 Applications as a Mean of Bolstering up Knowledge Management. Electronic Journal of Knowledge Management, 9(1), ECKM Special Issue, 1–9. [pdf] [online]
  2. Bebensee,T., Helms,R., & Spruit,M. (2012). Exploring Web 2.0 Applications as a Mean of Bolstering up Knowledge Management. In Gurteen,David (Ed.), Leading Issues in Social Knowledge Management (pp. 22-41). Academic Publishing International. [pdf] [online]
  3. Bebensee,T., Helms,R., & Spruit,M. (2012). Exploring the Impact of Web 2.0 on Knowledge Management. In Boughzala,I., & Dudezert,A. (Eds.), Knowledge Management 2.0: Organizational Models and Enterprise Strategies (pp. 17–43). IGI Global. [pdf] [online]
  4. Bebensee,T., Helms,R., & Spruit,M. (2010). Exploring Web 2.0 Applications as a Mean of Bolstering Up Knowledge Management in Non-Profit Organizations. 11th European Conference on Knowledge Management (pp. 65–73). ECKM, 2-3 September 2010, Universidade Lusíada de Vila Nova de Famalicão, Famalicão, Portugal. [pdf]

Weerd,I. van de

  1. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]
  2. Bekkers,W., Spruit,M., Weerd,I. van de, Vliet,R. van, & Mahieu,A. (2010). A Situational Assessment Method For Software Product Management. Proceedings of the 18th European Conference on Information Systems. Pretoria, South Africa. [pdf] [online]
  3. Bekkers,W., Weerd,I. van de, Spruit,M., & Brinkkemper,S. (2010). A Framework for Process Improvement in Software Product Management. Systems. In Riel,A., O'Connor,R., Tichkiewitch,S., & Messnarz,R. (Eds.), Communications in Computer and Information Science 99, Software and Services Process Improvement - Proceedings of the 17th European Conference (pp. 1–12). EuroSPI 2010, September 1-3, 2010, Grenoble, France: Springer. [pdf] [online]
  4. Bekkers,W., Spruit,M., Weerd,I. van de, Vliet,R. van, & Mahieu,A. (2010). Modelmatig verbeteren van product software management. Informatie, 8(12), 8–14. [pdf]

Bekkers,W.

  1. Bekkers,W., & Spruit,M. (2010). The Situational Assessment Method Put to the Test: Improvements Based on Case Studies. 4th International Workshop on Software Product Management (pp. 7–16). IWSPM, September 27, 2010, Sydney, Australia. [pdf] [online]
  2. Bekkers,W., Spruit,M., Weerd,I. van de, Vliet,R. van, & Mahieu,A. (2010). A Situational Assessment Method For Software Product Management. Proceedings of the 18th European Conference on Information Systems. Pretoria, South Africa. [pdf] [online]
  3. Bekkers,W., Weerd,I. van de, Spruit,M., & Brinkkemper,S. (2010). A Framework for Process Improvement in Software Product Management. Systems. In Riel,A., O'Connor,R., Tichkiewitch,S., & Messnarz,R. (Eds.), Communications in Computer and Information Science 99, Software and Services Process Improvement - Proceedings of the 17th European Conference (pp. 1–12). EuroSPI 2010, September 1-3, 2010, Grenoble, France: Springer. [pdf] [online]
  4. Bekkers,W., Spruit,M., Weerd,I. van de, Vliet,R. van, & Mahieu,A. (2010). Modelmatig verbeteren van product software management. Informatie, 8(12), 8–14. [pdf]

Wijaya,S.

  1. Wijaya,S., Spruit,M., Scheper,W., & Versendaal,J. (2011). Web 2.0-based Webstrategies for Three Different Types of Organizations. Computers in Human Behavior, 27(4), 1399–1407. [JIF: 6.829] [pdf] [online]
  2. Wijaya,S., Spruit,M., & Scheper, W. (2008). Webstrategy Formulation: benefiting from web 2.0 concepts to deliver business values. In Lytras,M., Damiani,E., & Ordóñez de Pablos,P. (Eds.), Web 2.0: The Business Model (pp. 103–132). Springer. [pdf] [online]
  3. Wijaya,S., Spruit,M., & Scheper,W. (2008). Webstrategy Formulation: benefiting from web 2.0 concepts to deliver business values. In Lytras, M., Carroll, J., Damiani,E., & Tennyson,R. (Eds.), Lecture Notes in Computer Science 5288, Emerging Technologies and Information Systems for the Knowledge Society (pp. 373–384). WSKS 2008, September 24-26, 2008, Athens, Greece: Springer. [pdf]

Askari,M.

  1. Meulendijk,M., Spruit,M., Willeboordse,F., Numans,M., Brinkkemper,S., Knol,W., Jansen,P., & Askari,M. (2016). Efficiency of clinical decision support systems improves with experience. Journal of Medical Systems, 40(4), 1–7. [JIF: 4.46] [pdf] [online]
  2. Luchies,E., Spruit,M., & Askari,M. (2018). Speech Technology in the Dutch Health Care: A Qualitative Study. 11th International Conference on Health Informatics (pp. 339–348). HEALTHINF 2018, 19-21 January 2108, Funchal, Portugal: ScitePress. [pdf]
  3. Zweth,J. van der, Askari,M., Spruit,M., & Nimwegen,C. van (2018). Devices used for non-invasive tele homecare for cardiovascular patients: A systematic literature review. 11th International Conference on Health Informatics (pp. 300–307). HEALTHINF 2018, 19-21 January 2108, Funchal, Portugal: ScitePress. [pdf]

Homberg,M. van den

  1. Homberg,M. van den, Monné,R., & Spruit,M. (2018). Bridging the information gap of disaster responders by optimizing data selection using cost and quality. Computers & Geosciences, 120, 60–72. [JIF: 3.372] [pdf] [online]
  2. Homberg,M. van den, Monné,R., & Spruit,M. (2018). Bridging the Information Gap: Mapping Data Sets on Information Needs in the Preparedness and Response Phase. In Hostettler,S., Najih Besson,S., & Bolay J (Eds.), Technologies for Development (pp. 213–225). Cham: Springer. [pdf] [online]
  3. Homberg,M. van den, Monné,R., & Spruit,M. (2016). Bridging the Information Gap: Mapping Data Sets on Information Needs in the Preparedness and Response Phase. Proceedings of the UNESCO Chair in Technologies for Development: From Innovation to Social Impact, Tech4Dev: UNESCO Chair in Technologies for Development: From Innovation to Social Impact. [online]

Monné,R.

  1. Homberg,M. van den, Monné,R., & Spruit,M. (2018). Bridging the information gap of disaster responders by optimizing data selection using cost and quality. Computers & Geosciences, 120, 60–72. [JIF: 3.372] [pdf] [online]
  2. Homberg,M. van den, Monné,R., & Spruit,M. (2018). Bridging the Information Gap: Mapping Data Sets on Information Needs in the Preparedness and Response Phase. In Hostettler,S., Najih Besson,S., & Bolay J (Eds.), Technologies for Development (pp. 213–225). Cham: Springer. [pdf] [online]
  3. Homberg,M. van den, Monné,R., & Spruit,M. (2016). Bridging the Information Gap: Mapping Data Sets on Information Needs in the Preparedness and Response Phase. Proceedings of the UNESCO Chair in Technologies for Development: From Innovation to Social Impact, Tech4Dev: UNESCO Chair in Technologies for Development: From Innovation to Social Impact. [online]

Buijs,M.

  1. Buijs,M., & Spruit,M. (2017). Asynchronous social search as a single point of access to information. Library Hi Tech, 35(4), 656–671. [JIF: 2.357] [online]
  2. Buijs,M., & Spruit,M. (2015). Determining the Relative Importance of Webpages Based on Social Signals Using the Social Score and the Potential Role of the Social Score in an Asynchronous Social Search Engine. In Fred,A., Dietz,J., Aveiro,D., Liu,K., & Filipe,J. (Eds.), Knowledge Discovery, Knowledge Engineering and Knowledge Management - 6th International Joint Conference, IC3K 2014, Rome, Italy, October 21-24, 2014, Revised Selected Papers (pp. 118–131). ScitePress. [online]
  3. Buijs,M., & Spruit,M. (2014). The Social Score: determining the relative importance of webpages based on online social signals. Proceedings of the 6th International Conference on Knowledge Discovery and Information Retrieval (pp. 71–77). KDIR 2014, 21-24 October, Rome,Italy: SciTePress. [pdf] [online]

Klumperman,J.

  1. Omta,W., Nobel,J. de, Klumperman,J., Egan,D., Spruit.M., & Brinkhuis,M. (2017). Improving Comprehension Efficiency of HCS Data Through Interactive Visualizations. ASSAY and Drug Development Technologies, 15(6), 247–256. [JIF: 1.738] [online]
  2. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]
  3. Omta,W., Egan,D., Klumperman,J., Spruit,M., & Brinkkemper,S. (2013). HTS-IA: High Throughput Screening Information Architecture for Genomics. International Journal of Healthcare Information Systems and Informatics, 8(4), 17–31. [online]

Scheepers,F.

  1. Rijcken,E., Kaymak,U., Scheepers,F., Mosteiro,P., Zervanou,K., & Spruit,M. (2022). Topic Modeling for Interpretable Text Classification from EHRs. Frontiers in Big Data, 5, Section Data Mining and Management, 846930. [pdf] [online]
  2. Mosteiro,P., Rijcken,E., Zervanou,K., Kaymak,U., Scheepers,F., & Spruit,M. (2021). Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes. Journal of Artificial Intelligence for Medical Sciences, 2(1–2), 44–54. [pdf] [online]
  3. Mosteiro,P., Rijcken,E., Zervanou,K., Kaymak,U., Scheepers,F., & Spruit,M. (2020). Making sense of violence risk predictions using clinical notes. In Huang,Z, Siuly,S., Wang,H., Zhou,R., & Zhang,Y. (Eds.), Lecture Notes in Computer Science 12435, Health Information Science: 9th International Conference (pp. 3–14). HIS 2020, Leiden: Springer. [pdf] [online]

Mosteiro,P.

  1. Rijcken,E., Kaymak,U., Scheepers,F., Mosteiro,P., Zervanou,K., & Spruit,M. (2022). Topic Modeling for Interpretable Text Classification from EHRs. Frontiers in Big Data, 5, Section Data Mining and Management, 846930. [pdf] [online]
  2. Rijcken,E., Mosteiro,P., Zervanou,K., Spruit,M., Scheepers,Fl., & Kaymak,U. (2022). FuzzyTM: a Software Package for Fuzzy Topic Modeling. IEEE International Conference on Fuzzy Systems 2022 (pp. 1–8). IEEE WCCI 2022: FUZZ-IEEE, 18-23 July, Padua, Italy: IEEE.
  3. Rijcken,E., Scheepers, Mosteiro,P., Zervanou,K., Spruit,M., & Kaymak,U.,F. (2021). A Comparative Study of Fuzzy Topic Models and LDA for Interpretability in Text Classification. IEEE Symposium Series on Computational Intelligence (SSCI). Orlando, Florida. [pdf] [online]

Zervanou,K.

  1. Rijcken,E., Kaymak,U., Scheepers,F., Mosteiro,P., Zervanou,K., & Spruit,M. (2022). Topic Modeling for Interpretable Text Classification from EHRs. Frontiers in Big Data, 5, Section Data Mining and Management, 846930. [pdf] [online]
  2. Rijcken,E., Mosteiro,P., Zervanou,K., Spruit,M., Scheepers,F., & Kaymak,U. (2022). FuzzyTM: a Software Package for Fuzzy Topic Modeling. IEEE International Conference on Fuzzy Systems (pp. 1–8). FUZZ-IEEE, 18-23 July 2022, Padua, Italy: IEEE. [online]
  3. Rijcken,E., Zervanou,K., Spruit,M., Mosteiro,P., Scheepers,F., & Kaymak,U. (2022). Exploring Embedding Spaces for more Coherent Topic Modeling in Electronic Health Records. IEEE International Conference on Systems, Man, and Cybernetics (pp. 2669–2674). SMC 2022, Oct 9-12, 2022, Prague, Czech Republic: IEEE. [pdf] [online]

Spruit,M

  1. Yigit Ozkan,B., & Spruit,M (2019). Cybersecurity Standardisation for SMEs: The Stakeholders’ Perspectives and a Research Agenda. International Journal of Standardization Research, 17(2), 1–25. [pdf] [online]
  2. Yigit Ozkan,B., & Spruit,M (2019). A Questionnaire Model for Cybersecurity Maturity Assessment for Critical Infrastructures. In Fournaris,A., Lampropoulos,K., & Tordera,E. (Eds.), Lecture Notes in Computer Science (LNCS) 11398 11398, Information and Operational Technology Security Systems. First International Workshop, IOSec 2018, CIPSEC Project (pp. 49–60). IOSec 2018, 13 Sept 2018, Heraklion, Crete, Greece: Springer. [pdf] [online]
  3. Spruit,M, Lingen,S. van, & Yigit Ozkan,B. (2019). The CYSFAM Questionnaire: Assessing CYberSecurity Focus Area Maturity. Technical report UU-CS-2019-003, Department of Information and Computing Sciences, Utrecht University. [online]

Dijk,J. van

  1. Christoulakis,M., Spruit,M., & Dijk,J. van (2015). Data Quality Management in the public domain: A case study within the Dutch Justice System. International Journal of Information Quality, 4(1), 1–17. [pdf] [online]
  2. Dijk,J. van, Bargh,M., Choenni,S., & Spruit,M. (2017). Maturing Pay-as-you-go Data Management: Towards decision support for paying the larger bills. In Helfert,M., Holzinger,A., Belo,O., & Francalanci,C. (Eds.), Data Management Technologies and Applications: 5th International Conference, DATA 2016, Revised Selected papers (pp. 102–124). Springer. [pdf]
  3. Dijk,J. van, Choenni,S., Leertouwer,E., Spruit,M., & Brinkkemper,S. (2013). A Data Space System for the Criminal Justice Chain. Lecture Notes in Computer Science 8185, Proceedings of On the Move to Meaningful Internet Systems: OTM 2013 Conferences (pp. 755-763). ODBASE 2013, 10-11 September 2013, Graz, Austria, Springer. [pdf] [online]

Bruijn,W. de

  1. Spruit,M., & Bruijn,W. de (2012). CITS:The Cost of IT Security Framework. International Journal of Information Security and Privacy, 6(4), October-December 2012, 94–116. [pdf]
  2. Bruijn,W. de, Spruit,M., & Heuvel,M. van der (2010). Identifying the Cost of Security. Journal of Information Assurance and Security, 5(1), 074–083. [pdf] [online]
  3. Bruijn,W. de, Spruit,M., & Heuvel,M. van der (2008). Identifying the Cost of Security. Proceedings of the AIS SIGSEC Workshop on Information Security & Privacy. Paris, France. [pdf]

Sallevelt,B.

  1. Blum,M., Sallevelt,B., Spinewine,A., O'Mahony,D., ... Spruit,M., & ... Rodondi,N. (2021). Optimizing Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older Adults (OPERAM): Cluster Randomised Controlled Trial. BMJ, 374(n1585). [JIF: 39.89] [pdf] [online]
  2. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]

Borit,M.

  1. Syed,S., Borit,M., & Spruit,M. (2018). Narrow lenses for capturing the complexity of fisheries: A topic analysis of fisheries science from 1990 to 2016. Fish and Fisheries, 19(4), 643–661. [JIF: 7.218] [pdf] [online]
  2. Syed,S., Spruit,M., & Borit,M. (2016). Bootstrapping a Semantic Lexicon on Verb Similarities. In Fred,A. et al. (Ed.), Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 189–196). KDIR 2016, November 11-13, 2016, Porto, Portugal: ScitePress. [pdf] [online]

Pietzka,K.

  1. Spruit,M., & Pietzka,K. (2015). MD3M: The Master Data Management Maturity Model. Computers in Human Behavior, 51(B), 1068–1076. [JIF: 6.829] [pdf] [online]
  2. Spruit,M., & Pietzka,K. (2014). The MD3M Questionnaire: Assessing Master Data Management Maturity. Technical report UU-CS-2014-022, Department of Information and Computing Sciences, Utrecht University. [online]

Vroon,R.

  1. Spruit,M., Vroon,R., & Batenburg,R. (2014). Towards healthcare business intelligence in long-term care: an explorative case study in the Netherlands. Computers in Human Behavior, 30, ICTs for Human Capital, 698–707. [JIF: 6.829] [pdf] [online]
  2. Spruit,M., & Vroon,R. (2013). Information needs in the Dutch long-term care sector. 6th World Summit on the Knowledge Society. WSKS 2013, June 19-21, Aveiro, Portugal. [pdf]

Versendaal,J.

  1. Wijaya,S., Spruit,M., Scheper,W., & Versendaal,J. (2011). Web 2.0-based Webstrategies for Three Different Types of Organizations. Computers in Human Behavior, 27(4), 1399–1407. [JIF: 6.829] [pdf] [online]
  2. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]

Shen,Z.

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]
  2. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]

Wilting,I.

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]
  2. Shen, Z., Meulendijk,M., Knol,W., Huibers,L., Wilting,I., Jansen,P., & Spruit,M. (2016). STRIPA Investigational Medical Device Dossier (IMDD). version 2.03, Department of Information and Computing Sciences, Utrecht University.

Knol,W.

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]
  2. Shen, Z., Meulendijk,M., Knol,W., Huibers,L., Wilting,I., Jansen,P., & Spruit,M. (2016). STRIPA Investigational Medical Device Dossier (IMDD). version 2.03, Department of Information and Computing Sciences, Utrecht University.

Mijnhardt,F.

  1. Mijnhardt,F., Baars,T., & Spruit,M. (2016). Organizational Characteristics Influencing SME Information Security Maturity. Journal of Computer Information Systems, 56(2), 106–115. [JIF: 3.4] [pdf] [online]
  2. Baars,T., Mijnhardt,F., Vlaanderen,K., & Spruit,M. (2016). An Analytics Approach to Adaptive Maturity Models using Organizational Characteristics. Decision Analytics, 3(5). [pdf] [online]

Brinkkemper,S.

  1. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]
  2. van Dijk,F., Gadellaa,J., Spruit,M., van Toledo,C., Brinkkemper,S., & Brinkhuis,M. (In press). Uncovering the Structures of Privacy Research using Bibliometric Network Analysis and Topic Modelling. Organizational Cybersecurity Journal: Practice, Process and People.

Drenth-van-Maanen,A.

  1. Meulendijk,M., Spruit,M., Drenth-van-Maanen,A., Numans,M., Brinkkemper,S., & Jansen,P. (2013). General practitioners’ attitudes towards decision-supported prescribing: an analysis of the Dutch primary care sector. Health Informatics Journal, 19(4), 247–263. [JIF: 2.681] [pdf] [online]
  2. Meulendijk,M., Drenth-van-Maanen,A., Jansen,P., Brinkkemper,S., Numans,M., & Spruit,M. (2013). Introducing the CORETEST feasibility analysis in medical informatics: a case study of a decision-supportive knowledge system in the Dutch primary care sector. In Miranda,I., Cruz-Cunha,M., & Gonçalves,P. (Eds.), Handbook of Research on ICTs for Healthcare and Social Services: Developments and Applications (pp. 1066–1087). IGI Global. [pdf]

Yigit Ozkan,B.

  1. Haastrecht,M. van, Yigit Ozkan,B., Brinkhuis,M., & Spruit,M. (2021). Respite for SMEs: A Systematic Review of Socio-Technical Cybersecurity Metrics. Applied Sciences, 11(15), Human Factors in the Digital Society, 6909. [JIF: 2.679] [pdf] [online]
  2. Haastrecht,M. van, Sarhan,I., Yigit Ozkan,B., Brinkhuis,M., & Spruit,M. (2021). SYMBALS: A Systematic Review Methodology Blending Active Learning and Snowballing. Frontiers in Research Metrics and Analytics, 6, Section Text-mining and Literature-based Discovery. [pdf] [online]

Ooms,R.

  1. Ooms,R., & Spruit,M. (2020). Self-Service Data Science in Healthcare with Automated Machine Learning. Applied Sciences, 10(9), Medical Artificial Intelligence, 2992. [JIF: 2.679] [pdf] [online]
  2. Ooms,R., Spruit,M., & Overbeek,S. (2019). 3PM Revisited: Dissecting the Three Phases Method for Outsourcing Knowledge Discovery. International Journal of Business Intelligence Research, 10(1), 80–93. [pdf] [online]

Heeringa,W.

  1. Spruit,M., Heeringa,W., & Nerbonne,J. (2009). Associations among linguistic levels. Lingua, 119(11), The forests behind the trees, 1624–1642. [JIF: 0.719] [pdf] [online]
  2. Heeringa,W., Nerbonne,J., Bezooijen,R. van, & Spruit,M. (2007). Geografie en inwoneraantallen als verklarende factoren voor variatie in het Nederlandse dialectgebied. Tijdschrift voor Nederlandse taal- en letterkunde, 123(1), Kwantitatieve benaderingen in de taal- en letterkunde, 70–82. [pdf] [online]

Nerbonne,J.

  1. Spruit,M., Heeringa,W., & Nerbonne,J. (2009). Associations among linguistic levels. Lingua, 119(11), The forests behind the trees, 1624–1642. [JIF: 0.719] [pdf] [online]
  2. Heeringa,W., Nerbonne,J., Bezooijen,R. van, & Spruit,M. (2007). Geografie en inwoneraantallen als verklarende factoren voor variatie in het Nederlandse dialectgebied. Tijdschrift voor Nederlandse taal- en letterkunde, 123(1), Kwantitatieve benaderingen in de taal- en letterkunde, 70–82. [pdf] [online]

Rijcken,E.

  1. Mosteiro,P., Rijcken,E., Zervanou,K., Kaymak,U., Scheepers,F., & Spruit,M. (2021). Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes. Journal of Artificial Intelligence for Medical Sciences, 2(1–2), 44–54. [pdf] [online]
  2. Mosteiro,P., Rijcken,E., Zervanou,K., Kaymak,U., Scheepers,F., & Spruit,M. (2020). Making sense of violence risk predictions using clinical notes. In Huang,Z, Siuly,S., Wang,H., Zhou,R., & Zhang,Y. (Eds.), Lecture Notes in Computer Science 12435, Health Information Science: 9th International Conference (pp. 3–14). HIS 2020, Leiden: Springer. [pdf] [online]

Ferati,D.

  1. Spruit,M., & Ferati,D. (2020). Text Mining Business Policy Documents: Applied Data Science in Finance. International Journal of Business Intelligence Research, 11(2), 1–19. [pdf] [online]
  2. Spruit,M., & Ferati,D. (2019). Applied Data Science in Financial Industry: Natural Language Processing Techniques for Bank Policies. In Visvizi,A., & Lytras,M. (Eds.), Springer Proceedings in Complexity, Research & Innovation Forum 2019 (pp. 351–367). RII 2019, Rome, Italy: Springer. [pdf] [online]

Stroe,A.

  1. Stroe,A., Spruit,M., Koelemeijer,S., & Beltman,B. (2016). PMOMM: The Project Management Office Maturity Model. International Journal of Knowledge Society Research, 7(3), 47-61. [pdf] [online]
  2. Stroe,A., Koelemeijer,S, & Spruit,M. (2013). Een PMO is meer dan een administratiekantoor. Controllers Magazine. Management Accounting & Control. [online]

Otten,S.

  1. Otten,S., Spruit,M., & Helms,R. (2015). Towards decision analytics in product portfolio management. Decision Analytics, 2(4). [online]
  2. Otten,S., & Spruit,M. (2011). Linguistic engineering and its applicability to business intelligence: towards an integrated framework. International Conference on Knowledge Discovery and Information Retrieval (pp. 460–464). Paris, France: SciTePress. [pdf] [online]

Adriana,T.

  1. Spruit,M., & Adriana,T. (2015). Quantifying education quality in secondary schools. International Journal of Knowledge Society Research, 6(1), 55-87. [pdf] [online]
  2. Spruit,M., & Adriana,T. (2018). Business Intelligence in Secondary Education: Data-driven Innovation by Quality Measurement. In Lytras,M., Daniela,L., & Visvizi,A. (Eds.), Enhancing Knowledge Discovery and Innovation in the Digital Era (pp. 56–90). IGI Global. [online]

Fotaki,G.

  1. Fotaki,G., Spruit,M., Brinkkemper,S., & Meijer,D. (2014). Exploring big data opportunities for online customer segmentation. International Journal of Business Intelligence Research, 5(3), 57–73. [pdf] [online]
  2. Fotaki,G., Spruit,M., Brinkkemper,S., & Meijer,D. (2013). Exploring Big data opportunities for Online Customer Segmentation. Technical report UU-CS-2013-021, Department of Information and Computing Sciences, Utrecht University. [online]

Meijer,D.

  1. Fotaki,G., Spruit,M., Brinkkemper,S., & Meijer,D. (2014). Exploring big data opportunities for online customer segmentation. International Journal of Business Intelligence Research, 5(3), 57–73. [pdf] [online]
  2. Fotaki,G., Spruit,M., Brinkkemper,S., & Meijer,D. (2013). Exploring Big data opportunities for Online Customer Segmentation. Technical report UU-CS-2013-021, Department of Information and Computing Sciences, Utrecht University. [online]

Boer,T. de

  1. Spruit,M., & Boer,T. de (2014). Business Intelligence as a Service: A Vendor’s Approach . International Journal of Business Intelligence Research, 5(4), 26–43. [pdf]
  2. Boer,T. de, & Spruit,M. (2014). The business intelligence as a service capability maturity model. Technical report UU-CS-2014-023, Department of Information and Computing Sciences, Utrecht University. [online]

Smeitink,M.

  1. Smeitink,M., & Spruit,M. (2013). Maturity for Sustainability in IT: Introducing the MITS. International Journal of Information Technologies and Systems Approach, 6(1), IT goes Green: Systemic Approaches to IT Policy Making, Design, Evaluation and Management, 39–56. [pdf] [online]
  2. Smeitink,M., & Spruit,M. (2012). IT sustainability measures: the Strategic Green Ontology. In Ordoñez de Pablos,P. (Ed.), Green Technologies and Business Practices: An IT Approach (pp. 36–57). IGI Global. [pdf] [online]

Abdat,N.

  1. Spruit,M., & Abdat,N. (2012). The Pricing Strategy Guideline Framework for SaaS Vendors. International Journal of Strategic Information Technology and Applications, 3(1), January-March 2012, 38–54. [pdf]
  2. Abdat,N., Spruit,M., & Bos,M. (2011). Software as a Service and the Pricing Strategy for Vendors. In Strader,T. (Ed.), Digital Product Management, Technology and Practice: Interdisciplinary Perspectives, Advances in E-Business Research (AEBR) Book Series (pp. 154–192). IGI Global. [pdf] [online]

Weeghel,R. van

  1. Weeghel,R. van, & Spruit,M. (2012). Corporate Strategy Optimization for Dutch Notaries with the use of IT. International Journal of Computer Information Systems and Industrial Management Applications, 4(1), 317–325. [pdf] [online]
  2. Weeghel,R. van, & Spruit,M. (2010). Using IT to Optimize Corporate Strategy for Dutch Notaries. In Bradley,G. (Ed.), Proceedings of the IADIS International Conference: ICT, Society and Human Beings 2010 (pp. 3–10). 29-31 July 2010, Freiburg, Germany. [pdf]

Heuvel,M. van der

  1. Bruijn,W. de, Spruit,M., & Heuvel,M. van der (2010). Identifying the Cost of Security. Journal of Information Assurance and Security, 5(1), 074–083. [pdf] [online]
  2. Bruijn,W. de, Spruit,M., & Heuvel,M. van der (2008). Identifying the Cost of Security. Proceedings of the AIS SIGSEC Workshop on Information Security & Privacy. Paris, France. [pdf]

Vleugel,A.

  1. Vleugel,A., Spruit,M., & Daal,A. van (2010). Historical data analysis through data mining from an outsourcing perspective: the three-phases method. International Journal of Business Intelligence Research, 1(3), 42–65. [pdf] [online]
  2. Vleugel,A., Spruit,M., & Daal,A. van (2012). Historical data analysis through data mining from an outsourcing perspective: the three-phases method. In Herschel,R. (Ed.), Organizational Applications of Business Intelligence Management: Emerging Trends (pp. 236–260). IGI Global. [online]

Daal,A. van

  1. Vleugel,A., Spruit,M., & Daal,A. van (2010). Historical data analysis through data mining from an outsourcing perspective: the three-phases method. International Journal of Business Intelligence Research, 1(3), 42–65. [pdf] [online]
  2. Vleugel,A., Spruit,M., & Daal,A. van (2012). Historical data analysis through data mining from an outsourcing perspective: the three-phases method. In Herschel,R. (Ed.), Organizational Applications of Business Intelligence Management: Emerging Trends (pp. 236–260). IGI Global. [online]

Tijssen,R.

  1. Tijssen,R., Spruit,M., Ridder,M. van de, & Raaij,B. van (2011). BI-FIT: Aligning Business Intelligence end-users, tasks and technologies. In Cruz-Cunha,M., & Varajão,J. (Eds.), Enterprise Information Systems Design, Implementation and Management: Organizational Applications (pp. 162–177). [pdf] [online]
  2. Tijssen,R., Spruit,M., Ridder,M. van de, & Raaij,B. van (2009). BI-FIT : The fit between Business Intelligence end-users, tasks and technologies. Conference on ENTERprise Information Systems (pp. 523–535). CENTERIS 2009, 7-9 October 2009, Ofir, Portugal. [pdf]

Ridder,M. van de

  1. Tijssen,R., Spruit,M., Ridder,M. van de, & Raaij,B. van (2011). BI-FIT: Aligning Business Intelligence end-users, tasks and technologies. In Cruz-Cunha,M., & Varajão,J. (Eds.), Enterprise Information Systems Design, Implementation and Management: Organizational Applications (pp. 162–177). [pdf] [online]
  2. Tijssen,R., Spruit,M., Ridder,M. van de, & Raaij,B. van (2009). BI-FIT : The fit between Business Intelligence end-users, tasks and technologies. Conference on ENTERprise Information Systems (pp. 523–535). CENTERIS 2009, 7-9 October 2009, Ofir, Portugal. [pdf]

Raaij,B. van

  1. Tijssen,R., Spruit,M., Ridder,M. van de, & Raaij,B. van (2011). BI-FIT: Aligning Business Intelligence end-users, tasks and technologies. In Cruz-Cunha,M., & Varajão,J. (Eds.), Enterprise Information Systems Design, Implementation and Management: Organizational Applications (pp. 162–177). [pdf] [online]
  2. Tijssen,R., Spruit,M., Ridder,M. van de, & Raaij,B. van (2009). BI-FIT : The fit between Business Intelligence end-users, tasks and technologies. Conference on ENTERprise Information Systems (pp. 523–535). CENTERIS 2009, 7-9 October 2009, Ofir, Portugal. [pdf]

Knol,P.

  1. Knol,P., Spruit,M., & Scheper,W. (2010). The Emerging Value of Social Computing in Business Model Innovation. In Lytras,M., Ordoñez de Pablos,P., Lee,W., & Karwowski,W. (Eds.), Electronic Globalized Business And Sustainable Development Through IT Management: Strategies And Perspectives (pp. 112–134). IGI Global. [pdf] [online]
  2. Knol,P., Spruit,M., & Scheper,W. (2008). Web 2.0 Revealed - Business Model Innovation through Social Computing. Proceedings of the Seventh AIS SIGeBIZ Workshop on e-business. Paris, France. [pdf]

Renes,C.

  1. Aarnoutse,F., Renes,C., Batenburg,R., & Spruit,M. (2016). STRIPA: The potential usefulness of a medical app. In Gasmelseid,T. (Ed.), Advancing Pharmaceutical Processes and Tools for Improved Health Outcomes (pp. 114–135). IGI Global. [pdf] [online]
  2. Renes,C., & Spruit,M. (2019). What do you mean? The CIRCA-DIPS method for root cause analysis of data interoperability problems within aviation information systems . Technical report UU-CS-2019-011, Department of Information and Computing Sciences, Utrecht University. [online]

Krens,R.

  1. Krens,R., Spruit,M., & Urbanus,N. (2012). Evaluating information security effectiveness with Health Professionals. In Fred,A., Filipe,J., & Gamboa,H. (Eds.), Communications in Computer and Information Science 274, BIOSTEC 2011 (pp. 324–334). Springer. [pdf]
  2. Krens,R., Spruit,M., & Urbanus,N. (2011). Information security in Health care: Evaluation with Health Professionals. Proceedings of the 4th International Conference on Health Informatics (pp. 61–69). HEALTHINF 2011, 26-29 January, 2011, Rome, Italy. [pdf] [online]

Urbanus,N.

  1. Krens,R., Spruit,M., & Urbanus,N. (2012). Evaluating information security effectiveness with Health Professionals. In Fred,A., Filipe,J., & Gamboa,H. (Eds.), Communications in Computer and Information Science 274, BIOSTEC 2011 (pp. 324–334). Springer. [pdf]
  2. Krens,R., Spruit,M., & Urbanus,N. (2011). Information security in Health care: Evaluation with Health Professionals. Proceedings of the 4th International Conference on Health Informatics (pp. 61–69). HEALTHINF 2011, 26-29 January, 2011, Rome, Italy. [pdf] [online]

Vliet,R. van

  1. Bekkers,W., Spruit,M., Weerd,I. van de, Vliet,R. van, & Mahieu,A. (2010). A Situational Assessment Method For Software Product Management. Proceedings of the 18th European Conference on Information Systems. Pretoria, South Africa. [pdf] [online]
  2. Bekkers,W., Spruit,M., Weerd,I. van de, Vliet,R. van, & Mahieu,A. (2010). Modelmatig verbeteren van product software management. Informatie, 8(12), 8–14. [pdf]

Mahieu,A.

  1. Bekkers,W., Spruit,M., Weerd,I. van de, Vliet,R. van, & Mahieu,A. (2010). A Situational Assessment Method For Software Product Management. Proceedings of the 18th European Conference on Information Systems. Pretoria, South Africa. [pdf] [online]
  2. Bekkers,W., Spruit,M., Weerd,I. van de, Vliet,R. van, & Mahieu,A. (2010). Modelmatig verbeteren van product software management. Informatie, 8(12), 8–14. [pdf]

Kaymak,U.

  1. Rijcken,E., Mosteiro,P., Zervanou,K., Spruit,M., Scheepers,F., & Kaymak,U. (2022). FuzzyTM: a Software Package for Fuzzy Topic Modeling. IEEE International Conference on Fuzzy Systems (pp. 1–8). FUZZ-IEEE, 18-23 July 2022, Padua, Italy: IEEE. [online]
  2. Rijcken,E., Zervanou,K., Spruit,M., Mosteiro,P., Scheepers,F., & Kaymak,U. (2022). Exploring Embedding Spaces for more Coherent Topic Modeling in Electronic Health Records. IEEE International Conference on Systems, Man, and Cybernetics (pp. 2669–2674). SMC 2022, Oct 9-12, 2022, Prague, Czech Republic: IEEE. [pdf] [online]

Blum,M.

  1. Blum,M., Sallevelt,B., Spinewine,A., O'Mahony,D., ... Spruit,M., & ... Rodondi,N. (2021). Optimizing Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older Adults (OPERAM): Cluster Randomised Controlled Trial. BMJ, 374(n1585). [JIF: 39.89] [pdf] [online]

Spinewine,A.

  1. Blum,M., Sallevelt,B., Spinewine,A., O'Mahony,D., ... Spruit,M., & ... Rodondi,N. (2021). Optimizing Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older Adults (OPERAM): Cluster Randomised Controlled Trial. BMJ, 374(n1585). [JIF: 39.89] [pdf] [online]

O'Mahony,D.

  1. Blum,M., Sallevelt,B., Spinewine,A., O'Mahony,D., ... Spruit,M., & ... Rodondi,N. (2021). Optimizing Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older Adults (OPERAM): Cluster Randomised Controlled Trial. BMJ, 374(n1585). [JIF: 39.89] [pdf] [online]

... Spruit,M.

  1. Blum,M., Sallevelt,B., Spinewine,A., O'Mahony,D., ... Spruit,M., & ... Rodondi,N. (2021). Optimizing Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older Adults (OPERAM): Cluster Randomised Controlled Trial. BMJ, 374(n1585). [JIF: 39.89] [pdf] [online]

... Rodondi,N.

  1. Blum,M., Sallevelt,B., Spinewine,A., O'Mahony,D., ... Spruit,M., & ... Rodondi,N. (2021). Optimizing Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older Adults (OPERAM): Cluster Randomised Controlled Trial. BMJ, 374(n1585). [JIF: 39.89] [pdf] [online]

Est,R. van

  1. Menger,V., Spruit,M., Est,R. van, Nap,E., & Scheepers,F. (2019). Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records. JAMA Network Open, 2(7), e196709. [JIF: 8.483] [pdf] [online]

Nap,E.

  1. Menger,V., Spruit,M., Est,R. van, Nap,E., & Scheepers,F. (2019). Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records. JAMA Network Open, 2(7), e196709. [JIF: 8.483] [pdf] [online]

Aodha,L.

  1. Syed,S., Aodha,L., Scougal,C., & Spruit,M. (2019). Mapping the global network of fisheries science collaboration. Fish and Fisheries, 20(5), 830–856. [JIF: 7.218] [pdf] [online]

Scougal,C.

  1. Syed,S., Aodha,L., Scougal,C., & Spruit,M. (2019). Mapping the global network of fisheries science collaboration. Fish and Fisheries, 20(5), 830–856. [JIF: 7.218] [pdf] [online]

Wondolleck,R.

  1. Yigit Ozkan,B., Spruit,M., Wondolleck,R., & Burriel Coll,V. (2019). Modelling adaptive information security for SMEs in a cluster. Journal of Intellectual Capital, 21(2), 235–256. [JIF: 7.198] [pdf] [online]

Burriel Coll,V.

  1. Yigit Ozkan,B., Spruit,M., Wondolleck,R., & Burriel Coll,V. (2019). Modelling adaptive information security for SMEs in a cluster. Journal of Intellectual Capital, 21(2), 235–256. [JIF: 7.198] [pdf] [online]

Borger,T.

  1. Borger,T., Mosteiro,P., Kaya,H., Rijcken,E., Salah,A., Scheepers,F., & Spruit,M. (2022). Federated Learning for Violence Incident Prediction in a Simulated Cross-institutional Psychiatric Setting. Expert Systems with Applications, 199, 116720. [JIF: 6.954] [pdf] [online]

Kaya,H.

  1. Borger,T., Mosteiro,P., Kaya,H., Rijcken,E., Salah,A., Scheepers,F., & Spruit,M. (2022). Federated Learning for Violence Incident Prediction in a Simulated Cross-institutional Psychiatric Setting. Expert Systems with Applications, 199, 116720. [JIF: 6.954] [pdf] [online]

Salah,A.

  1. Borger,T., Mosteiro,P., Kaya,H., Rijcken,E., Salah,A., Scheepers,F., & Spruit,M. (2022). Federated Learning for Violence Incident Prediction in a Simulated Cross-institutional Psychiatric Setting. Expert Systems with Applications, 199, 116720. [JIF: 6.954] [pdf] [online]

Eskes,P.

  1. Eskes,P., Spruit,M., Brinkkemper,S., Vorstman,J., & Kas,M. (2016). The Sociability Score: App-based social profiling from a healthcare perspective. Computers in Human Behavior, 59, 39–48. [JIF: 6.829] [pdf] [online]

Vorstman,J.

  1. Eskes,P., Spruit,M., Brinkkemper,S., Vorstman,J., & Kas,M. (2016). The Sociability Score: App-based social profiling from a healthcare perspective. Computers in Human Behavior, 59, 39–48. [JIF: 6.829] [pdf] [online]

Kas,M.

  1. Eskes,P., Spruit,M., Brinkkemper,S., Vorstman,J., & Kas,M. (2016). The Sociability Score: App-based social profiling from a healthcare perspective. Computers in Human Behavior, 59, 39–48. [JIF: 6.829] [pdf] [online]

Weerd,I. van der

  1. Pachidi,S., Spruit,M., & Weerd,I. van der (2014). Understanding Users' Behavior with Software Operation Data Mining. Computers in Human Behavior, 30, ICTs for Human Capital, 583–594. [JIF: 6.829] [pdf] [online]

Wijk,L. van

  1. Menger,V., Scheepers,F., Wijk,L. van, & Spruit,M. (2018). DEDUCE: A pattern matching method for automatic de-identification of Dutch medical text. Telematics and Informatics, 35(4), Patient Centric Healthcare, 727–736. [JIF: 6.182] [pdf] [online]

Lytras,M.

  1. Spruit,M., & Lytras,M. (2018). Applied Data Science in Patient-centric Healthcare: Adaptive Analytic Systems for Empowering Physicians and Patients. Telematics and Informatics, 35(4), Patient Centric Healthcare, 643–653. [JIF: 6.182] [pdf] [online]

Willeboordse,F.

  1. Meulendijk,M., Spruit,M., Willeboordse,F., Numans,M., Brinkkemper,S., Knol,W., Jansen,P., & Askari,M. (2016). Efficiency of clinical decision support systems improves with experience. Journal of Medical Systems, 40(4), 1–7. [JIF: 4.46] [pdf] [online]

Knol,W.

  1. Meulendijk,M., Spruit,M., Willeboordse,F., Numans,M., Brinkkemper,S., Knol,W., Jansen,P., & Askari,M. (2016). Efficiency of clinical decision support systems improves with experience. Journal of Medical Systems, 40(4), 1–7. [JIF: 4.46] [pdf] [online]

Huibers,C.

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]

Heij,J.

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]

Egberts,T.

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]

Puijenbroek,E. van

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]

Jungo,K.

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]

Rodondi,N.

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]

Dalleur,O.

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]

Spinewine,A. Jennings,E.

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]

O'Mahony,D.

  1. Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging. [JIF: 3.923] [pdf] [online]

Drenth-van Maanen,C.

  1. Meulendijk,M., Spruit,M., Drenth-van Maanen,C., Numans,M., Brinkkemper,S., Jansen,P., & Knol,W (2015). Computerized decision support improves medication review effectiveness: an experiment evaluating the STRIP Assistant’s usability. Drugs & Aging, 32(6), 495–503. [JIF: 3.923] [pdf] [online]

Knol,W

  1. Meulendijk,M., Spruit,M., Drenth-van Maanen,C., Numans,M., Brinkkemper,S., Jansen,P., & Knol,W (2015). Computerized decision support improves medication review effectiveness: an experiment evaluating the STRIP Assistant’s usability. Drugs & Aging, 32(6), 495–503. [JIF: 3.923] [pdf] [online]

Yigit Ozkan,Bilge

  1. Yigit Ozkan,Bilge, & Spruit,M. (2022). Adaptable Security Maturity Assessment and Standardization for Digital SMEs. Journal of Computer Information Systems. [JIF: 3.4] [pdf] [online]

Verkooij,K.

  1. Verkooij,K., & Spruit,M. (2013). Mobile Business Intelligence: Key considerations for implementation projects. Journal of Computer Information Systems, 54(1), 23–33. [JIF: 3.4] [pdf] [online]

Meppelink,J.

  1. Meppelink,J., Langen,J. van, Siebes,A., & Spruit,M. (2020). Beware Thy Bias: Scaling Mobile Phone Data to Measure Traffic Intensities. Sustainability, 12(9), Exploring the Impact of AI on Politics and Society , 3631. [JIF: 3.251] [pdf] [online]

Langen,J. van

  1. Meppelink,J., Langen,J. van, Siebes,A., & Spruit,M. (2020). Beware Thy Bias: Scaling Mobile Phone Data to Measure Traffic Intensities. Sustainability, 12(9), Exploring the Impact of AI on Politics and Society , 3631. [JIF: 3.251] [pdf] [online]

Siebes,A.

  1. Meppelink,J., Langen,J. van, Siebes,A., & Spruit,M. (2020). Beware Thy Bias: Scaling Mobile Phone Data to Measure Traffic Intensities. Sustainability, 12(9), Exploring the Impact of AI on Politics and Society , 3631. [JIF: 3.251] [pdf] [online]

Pieket Weeserik,B.

  1. Pieket Weeserik,B., & Spruit,M. (2018). Improving Operational Risk Management using Business Performance Management technologies. Sustainability, 10(3), 640. [JIF: 3.251] [online]

Siegersma,K.

  1. Siegersma,K., Evers,M., Bots,S., Groepenhoff,F., Appelman,Y., Hofstra,L., Tulevski,I., Somsen,A., Den Ruijter,H., Spruit,M., & Onland-Moret,C. (2022). Adverse Drug Reactions Identification in clinical Notes (ADRIN): Word embedding models and string matching. JMIR Medical Informatics, 10(1), e31063. [JIF: 2.96] [pdf] [online]

Evers,M.

  1. Siegersma,K., Evers,M., Bots,S., Groepenhoff,F., Appelman,Y., Hofstra,L., Tulevski,I., Somsen,A., Den Ruijter,H., Spruit,M., & Onland-Moret,C. (2022). Adverse Drug Reactions Identification in clinical Notes (ADRIN): Word embedding models and string matching. JMIR Medical Informatics, 10(1), e31063. [JIF: 2.96] [pdf] [online]

Bots,S.

  1. Siegersma,K., Evers,M., Bots,S., Groepenhoff,F., Appelman,Y., Hofstra,L., Tulevski,I., Somsen,A., Den Ruijter,H., Spruit,M., & Onland-Moret,C. (2022). Adverse Drug Reactions Identification in clinical Notes (ADRIN): Word embedding models and string matching. JMIR Medical Informatics, 10(1), e31063. [JIF: 2.96] [pdf] [online]

Groepenhoff,F.

  1. Siegersma,K., Evers,M., Bots,S., Groepenhoff,F., Appelman,Y., Hofstra,L., Tulevski,I., Somsen,A., Den Ruijter,H., Spruit,M., & Onland-Moret,C. (2022). Adverse Drug Reactions Identification in clinical Notes (ADRIN): Word embedding models and string matching. JMIR Medical Informatics, 10(1), e31063. [JIF: 2.96] [pdf] [online]

Appelman,Y.

  1. Siegersma,K., Evers,M., Bots,S., Groepenhoff,F., Appelman,Y., Hofstra,L., Tulevski,I., Somsen,A., Den Ruijter,H., Spruit,M., & Onland-Moret,C. (2022). Adverse Drug Reactions Identification in clinical Notes (ADRIN): Word embedding models and string matching. JMIR Medical Informatics, 10(1), e31063. [JIF: 2.96] [pdf] [online]

Hofstra,L.

  1. Siegersma,K., Evers,M., Bots,S., Groepenhoff,F., Appelman,Y., Hofstra,L., Tulevski,I., Somsen,A., Den Ruijter,H., Spruit,M., & Onland-Moret,C. (2022). Adverse Drug Reactions Identification in clinical Notes (ADRIN): Word embedding models and string matching. JMIR Medical Informatics, 10(1), e31063. [JIF: 2.96] [pdf] [online]

Tulevski,I.

  1. Siegersma,K., Evers,M., Bots,S., Groepenhoff,F., Appelman,Y., Hofstra,L., Tulevski,I., Somsen,A., Den Ruijter,H., Spruit,M., & Onland-Moret,C. (2022). Adverse Drug Reactions Identification in clinical Notes (ADRIN): Word embedding models and string matching. JMIR Medical Informatics, 10(1), e31063. [JIF: 2.96] [pdf] [online]

Somsen,A.

  1. Siegersma,K., Evers,M., Bots,S., Groepenhoff,F., Appelman,Y., Hofstra,L., Tulevski,I., Somsen,A., Den Ruijter,H., Spruit,M., & Onland-Moret,C. (2022). Adverse Drug Reactions Identification in clinical Notes (ADRIN): Word embedding models and string matching. JMIR Medical Informatics, 10(1), e31063. [JIF: 2.96] [pdf] [online]

Den Ruijter,H.

  1. Siegersma,K., Evers,M., Bots,S., Groepenhoff,F., Appelman,Y., Hofstra,L., Tulevski,I., Somsen,A., Den Ruijter,H., Spruit,M., & Onland-Moret,C. (2022). Adverse Drug Reactions Identification in clinical Notes (ADRIN): Word embedding models and string matching. JMIR Medical Informatics, 10(1), e31063. [JIF: 2.96] [pdf] [online]

Onland-Moret,C.

  1. Siegersma,K., Evers,M., Bots,S., Groepenhoff,F., Appelman,Y., Hofstra,L., Tulevski,I., Somsen,A., Den Ruijter,H., Spruit,M., & Onland-Moret,C. (2022). Adverse Drug Reactions Identification in clinical Notes (ADRIN): Word embedding models and string matching. JMIR Medical Informatics, 10(1), e31063. [JIF: 2.96] [pdf] [online]

van Heesbeen,R

  1. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]

de Nobel,J.

  1. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]

van der Velden,L.

  1. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]

Medema,R.

  1. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]

Siebes,A.

  1. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]

Feelders,A.

  1. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]

Klumperman,J.

  1. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]

Egan,D.

  1. Omta,W., van Heesbeen,R, Shen,Z., de Nobel,J., van der Velden,L., Medema,R., Siebes,A., Feelders,A., Brinkkemper,S., Klumperman,J., Spruit,M., Brinkhuis,M., & Egan,D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. [JIF: 2.918] [pdf] [online]

Adam,L., ..., Spruit,M. et al.

  1. Adam,L., ..., Spruit,M. et al. (2019). Rationale and design of OPtimising thERapy to prevent Avoidable hospital admissions in Multimorbid older people (OPERAM): a cluster randomised controlled trial. BMJ Open, 9, e02676. [JIF: 2.692] [pdf] [online]

Jungo,K., ..., Spruit,M. et al.

  1. Jungo,K., ..., Spruit,M. et al. (2019). Optimising PharmacoTherapy In the multimorbid elderly in primary CAre" (OPTICA) to improve medication appropriateness: study protocol of a cluster randomised controlled trial. BMJ Open, 9, e031080. [JIF: 2.692] [pdf] [online]

Krimpen,H. van

  1. Shen,Z., Krimpen,H. van, & Spruit,M. (2019). A lightweight API-based approach for building flexible clinical NLP systems. Journal of Healthcare Engineering, Article ID 3435609, 11. [JIF: 2.682] [pdf] [online]

Verkleij,S.

  1. Spruit,M., Verkleij,S., Schepper,C. de, & Scheepers,F. (2022). Exploring Language Markers of Mental Health in Psychiatric Stories. Applied Sciences, 12(4), Current Approaches and Applications in Natural Language Processing, 2179. [JIF: 2.679] [pdf] [online]

Schepper,C. de

  1. Spruit,M., Verkleij,S., Schepper,C. de, & Scheepers,F. (2022). Exploring Language Markers of Mental Health in Psychiatric Stories. Applied Sciences, 12(4), Current Approaches and Applications in Natural Language Processing, 2179. [JIF: 2.679] [pdf] [online]

Crowley,E., ..., Spruit,M. et al.

  1. Crowley,E., ..., Spruit,M. et al. (2020). Intervention protocol: OPtimising thERapy to prevent avoidable hospital Admission in the Multi-morbid elderly (OPERAM): a structured medication review with support of a computerised decision support system. BMC Health Services Research, 20(220). [JIF: 2.655] [pdf] [online]

Jungo, K.

  1. Jungo, K., Meier,R., Valeri,F., Schwab,N., Schneider,C., Reeve,E., Spruit,M., Schwenkglenks,M., Rodondi,N., & Streit,S. (2021). Baseline characteristics and comparability of older multimorbid patients with polypharmacy and general practitioners participating in a randomized controlled primary care trial. BMC Family Practice, 22(123). [JIF: 2.497] [pdf] [online]

Meier,R.

  1. Jungo, K., Meier,R., Valeri,F., Schwab,N., Schneider,C., Reeve,E., Spruit,M., Schwenkglenks,M., Rodondi,N., & Streit,S. (2021). Baseline characteristics and comparability of older multimorbid patients with polypharmacy and general practitioners participating in a randomized controlled primary care trial. BMC Family Practice, 22(123). [JIF: 2.497] [pdf] [online]

Valeri,F.

  1. Jungo, K., Meier,R., Valeri,F., Schwab,N., Schneider,C., Reeve,E., Spruit,M., Schwenkglenks,M., Rodondi,N., & Streit,S. (2021). Baseline characteristics and comparability of older multimorbid patients with polypharmacy and general practitioners participating in a randomized controlled primary care trial. BMC Family Practice, 22(123). [JIF: 2.497] [pdf] [online]

Schwab,N.

  1. Jungo, K., Meier,R., Valeri,F., Schwab,N., Schneider,C., Reeve,E., Spruit,M., Schwenkglenks,M., Rodondi,N., & Streit,S. (2021). Baseline characteristics and comparability of older multimorbid patients with polypharmacy and general practitioners participating in a randomized controlled primary care trial. BMC Family Practice, 22(123). [JIF: 2.497] [pdf] [online]

Schneider,C.

  1. Jungo, K., Meier,R., Valeri,F., Schwab,N., Schneider,C., Reeve,E., Spruit,M., Schwenkglenks,M., Rodondi,N., & Streit,S. (2021). Baseline characteristics and comparability of older multimorbid patients with polypharmacy and general practitioners participating in a randomized controlled primary care trial. BMC Family Practice, 22(123). [JIF: 2.497] [pdf] [online]

Reeve,E.

  1. Jungo, K., Meier,R., Valeri,F., Schwab,N., Schneider,C., Reeve,E., Spruit,M., Schwenkglenks,M., Rodondi,N., & Streit,S. (2021). Baseline characteristics and comparability of older multimorbid patients with polypharmacy and general practitioners participating in a randomized controlled primary care trial. BMC Family Practice, 22(123). [JIF: 2.497] [pdf] [online]

Schwenkglenks,M.

  1. Jungo, K., Meier,R., Valeri,F., Schwab,N., Schneider,C., Reeve,E., Spruit,M., Schwenkglenks,M., Rodondi,N., & Streit,S. (2021). Baseline characteristics and comparability of older multimorbid patients with polypharmacy and general practitioners participating in a randomized controlled primary care trial. BMC Family Practice, 22(123). [JIF: 2.497] [pdf] [online]

Rodondi,N.

  1. Jungo, K., Meier,R., Valeri,F., Schwab,N., Schneider,C., Reeve,E., Spruit,M., Schwenkglenks,M., Rodondi,N., & Streit,S. (2021). Baseline characteristics and comparability of older multimorbid patients with polypharmacy and general practitioners participating in a randomized controlled primary care trial. BMC Family Practice, 22(123). [JIF: 2.497] [pdf] [online]

Streit,S.

  1. Jungo, K., Meier,R., Valeri,F., Schwab,N., Schneider,C., Reeve,E., Spruit,M., Schwenkglenks,M., Rodondi,N., & Streit,S. (2021). Baseline characteristics and comparability of older multimorbid patients with polypharmacy and general practitioners participating in a randomized controlled primary care trial. BMC Family Practice, 22(123). [JIF: 2.497] [pdf] [online]

Gulpur,G.

  1. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]

Tzismadia,G.

  1. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]

Kab,R.

  1. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]

Priboi,C.

  1. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]

David,D.

  1. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]

Răcătăian,A.

  1. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]

Baumgartner,L.

  1. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]

Fricker,S.

  1. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]

Ruiz,J.

  1. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]

Armas,E.

  1. Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Răcătăian,A., Baumgartner,L., Fricker,S., Ruiz,J., Armas,E., Brinkhuis,M., & Spruit,M. (2021). A Shared Cyber Threat Intelligence Solution for SMEs. Electronics, 10(23), Emerging Applications of Information Security Technology in Digital Environment, 2913. [JIF: 2.397] [pdf] [online]

Hagoort,K.

  1. Menger,V., Spruit,M., Hagoort,K., & Scheepers,F. (2016). Transitioning to a data driven mental health practice: collaborative expert sessions for knowledge and hypothesis finding. Computational and Mathematical Methods in Medicine, Article ID 9089321, 11. [JIF: 2.238] [pdf] [online]

Nobel,J. de

  1. Omta,W., Nobel,J. de, Klumperman,J., Egan,D., Spruit.M., & Brinkhuis,M. (2017). Improving Comprehension Efficiency of HCS Data Through Interactive Visualizations. ASSAY and Drug Development Technologies, 15(6), 247–256. [JIF: 1.738] [online]

Spruit.M.

  1. Omta,W., Nobel,J. de, Klumperman,J., Egan,D., Spruit.M., & Brinkhuis,M. (2017). Improving Comprehension Efficiency of HCS Data Through Interactive Visualizations. ASSAY and Drug Development Technologies, 15(6), 247–256. [JIF: 1.738] [online]

Heesbeen,R. van

  1. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]

Pagliero,R.

  1. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]

Velden,L. van der

  1. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]

Lelieveld,D.

  1. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]

Nellen,M.

  1. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]

Kramer,M.

  1. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]

Yeong,M.

  1. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]

Saeidi,A.

  1. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]

Medema,R.

  1. Omta,W., Heesbeen,R. van, Pagliero,R., Velden,L. van der, Lelieveld,D., Nellen,M., Kramer,M., Yeong,M., Saeidi,A., Medema,R., Spruit,M., Brinkkemper,S., Klumperman,J., & Egan,D. (2016). HC StratoMineR: A web-based tool for the rapid analysis of high content datasets. ASSAY and Drug Development Technologies, 14(8), 439–452. [JIF: 1.738] [online]

van Dijk,F.

  1. van Dijk,F., Gadellaa,J., Spruit,M., van Toledo,C., Brinkkemper,S., & Brinkhuis,M. (In press). Uncovering the Structures of Privacy Research using Bibliometric Network Analysis and Topic Modelling. Organizational Cybersecurity Journal: Practice, Process and People.

Gadellaa,J.

  1. van Dijk,F., Gadellaa,J., Spruit,M., van Toledo,C., Brinkkemper,S., & Brinkhuis,M. (In press). Uncovering the Structures of Privacy Research using Bibliometric Network Analysis and Topic Modelling. Organizational Cybersecurity Journal: Practice, Process and People.

van Toledo,C.

  1. van Dijk,F., Gadellaa,J., Spruit,M., van Toledo,C., Brinkkemper,S., & Brinkhuis,M. (In press). Uncovering the Structures of Privacy Research using Bibliometric Network Analysis and Topic Modelling. Organizational Cybersecurity Journal: Practice, Process and People.

Kuiper,J.

  1. Mosteiro,P., Kuiper,J., Masthoff,J., Scheepers,F., & Spruit,M. (2022). Bias Discovery in Machine Learning Models for Mental Health. Information, 13(5), Advances in Explainable Artificial Intelligence, 237. [pdf] [online]

Masthoff,J.

  1. Mosteiro,P., Kuiper,J., Masthoff,J., Scheepers,F., & Spruit,M. (2022). Bias Discovery in Machine Learning Models for Mental Health. Information, 13(5), Advances in Explainable Artificial Intelligence, 237. [pdf] [online]

Toledo, C. van

  1. Toledo, C. van, Schraagen,M., Dijk,F. van, Brinkhuis,M., & Spruit,M. (2022). Exploring the Utility of Dutch Question Answering Datasets for Human Resource Contact Centres. Information, 13(11), Novel Methods and Applications in Natural Language Processing, 513. [pdf] [online]

Schraagen,M.

  1. Toledo, C. van, Schraagen,M., Dijk,F. van, Brinkhuis,M., & Spruit,M. (2022). Exploring the Utility of Dutch Question Answering Datasets for Human Resource Contact Centres. Information, 13(11), Novel Methods and Applications in Natural Language Processing, 513. [pdf] [online]

Felix,S.

  1. Felix,S., Bagheri,A., Ramjankhan,F., Spruit,M., Oberski,D., Jonge,N. de, Laake, L. van, Suyker,W., & Asselbergs,F. (2021). A Data Mining-based Cross-Industry Process for Predicting Major Bleeding in Mechanical Circulatory Support. European Heart Journal - Digital Health, 2(4). [pdf] [online]

Bagheri,A.

  1. Felix,S., Bagheri,A., Ramjankhan,F., Spruit,M., Oberski,D., Jonge,N. de, Laake, L. van, Suyker,W., & Asselbergs,F. (2021). A Data Mining-based Cross-Industry Process for Predicting Major Bleeding in Mechanical Circulatory Support. European Heart Journal - Digital Health, 2(4). [pdf] [online]

Ramjankhan,F.

  1. Felix,S., Bagheri,A., Ramjankhan,F., Spruit,M., Oberski,D., Jonge,N. de, Laake, L. van, Suyker,W., & Asselbergs,F. (2021). A Data Mining-based Cross-Industry Process for Predicting Major Bleeding in Mechanical Circulatory Support. European Heart Journal - Digital Health, 2(4). [pdf] [online]

Oberski,D.

  1. Felix,S., Bagheri,A., Ramjankhan,F., Spruit,M., Oberski,D., Jonge,N. de, Laake, L. van, Suyker,W., & Asselbergs,F. (2021). A Data Mining-based Cross-Industry Process for Predicting Major Bleeding in Mechanical Circulatory Support. European Heart Journal - Digital Health, 2(4). [pdf] [online]

Jonge,N. de

  1. Felix,S., Bagheri,A., Ramjankhan,F., Spruit,M., Oberski,D., Jonge,N. de, Laake, L. van, Suyker,W., & Asselbergs,F. (2021). A Data Mining-based Cross-Industry Process for Predicting Major Bleeding in Mechanical Circulatory Support. European Heart Journal - Digital Health, 2(4). [pdf] [online]

Laake, L. van

  1. Felix,S., Bagheri,A., Ramjankhan,F., Spruit,M., Oberski,D., Jonge,N. de, Laake, L. van, Suyker,W., & Asselbergs,F. (2021). A Data Mining-based Cross-Industry Process for Predicting Major Bleeding in Mechanical Circulatory Support. European Heart Journal - Digital Health, 2(4). [pdf] [online]

Suyker,W.

  1. Felix,S., Bagheri,A., Ramjankhan,F., Spruit,M., Oberski,D., Jonge,N. de, Laake, L. van, Suyker,W., & Asselbergs,F. (2021). A Data Mining-based Cross-Industry Process for Predicting Major Bleeding in Mechanical Circulatory Support. European Heart Journal - Digital Health, 2(4). [pdf] [online]

Asselbergs,F.

  1. Felix,S., Bagheri,A., Ramjankhan,F., Spruit,M., Oberski,D., Jonge,N. de, Laake, L. van, Suyker,W., & Asselbergs,F. (2021). A Data Mining-based Cross-Industry Process for Predicting Major Bleeding in Mechanical Circulatory Support. European Heart Journal - Digital Health, 2(4). [pdf] [online]

Sarhan,I.

  1. Haastrecht,M. van, Sarhan,I., Yigit Ozkan,B., Brinkhuis,M., & Spruit,M. (2021). SYMBALS: A Systematic Review Methodology Blending Active Learning and Snowballing. Frontiers in Research Metrics and Analytics, 6, Section Text-mining and Literature-based Discovery. [pdf] [online]

Smit,T.

  1. Smit,T., Haastrecht,M. van, & Spruit,M. (2021). The Effect of Countermeasure Readability on Security Intentions. Journal of Cybersecurity and Privacy, 1, Cyber Situational Awareness Techniques and Human Factors, 675–704. [pdf] [online]

Kais,M.

  1. Spruit,M., Kais,M., & Menger,V. (2021). Automated Business Goal Extraction from E-mail Repositories to Bootstrap Business Understanding. Future Internet, 13(10), Trends of Data Science and Knowledge Discovery, 243. [pdf] [online]

van Lingen,S.

  1. Yigit Ozkan,B., van Lingen,S., & Spruit,M. (2021). The Cybersecurity Focus Area Maturity (CYSFAM) Model. Journal of Cybersecurity and Privacy, 1, 119–140. [pdf] [online]

Bakhtiari,B.

  1. Lefebvre,A., Bakhtiari,B., & Spruit,M. (2020). Exploring Research Data Management Planning Challenges in Practice. it – Information Technology, 62(1), 29–37. [pdf] [online]

Heesbeen,R. van

  1. Omta,W., Heesbeen,R. van, Shen,I., Feelders,A., Brinkhuis,M., Egan,D., & Spruit,M. (2020). PurifyR: An R Package for Highly Automated Reproducible Variable Extraction and Standardization. Systems Medicine, 3(1), Integrative Data Analysis in Systems Medicine, 1–7. [pdf] [online]

Shen,I.

  1. Omta,W., Heesbeen,R. van, Shen,I., Feelders,A., Brinkhuis,M., Egan,D., & Spruit,M. (2020). PurifyR: An R Package for Highly Automated Reproducible Variable Extraction and Standardization. Systems Medicine, 3(1), Integrative Data Analysis in Systems Medicine, 1–7. [pdf] [online]

Feelders,A.

  1. Omta,W., Heesbeen,R. van, Shen,I., Feelders,A., Brinkhuis,M., Egan,D., & Spruit,M. (2020). PurifyR: An R Package for Highly Automated Reproducible Variable Extraction and Standardization. Systems Medicine, 3(1), Integrative Data Analysis in Systems Medicine, 1–7. [pdf] [online]

Overbeek,S.

  1. Ooms,R., Spruit,M., & Overbeek,S. (2019). 3PM Revisited: Dissecting the Three Phases Method for Outsourcing Knowledge Discovery. International Journal of Business Intelligence Research, 10(1), 80–93. [pdf] [online]

Vlaanderen,K.

  1. Baars,T., Mijnhardt,F., Vlaanderen,K., & Spruit,M. (2016). An Analytics Approach to Adaptive Maturity Models using Organizational Characteristics. Decision Analytics, 3(5). [pdf] [online]

Koelemeijer,S.

  1. Stroe,A., Spruit,M., Koelemeijer,S., & Beltman,B. (2016). PMOMM: The Project Management Office Maturity Model. International Journal of Knowledge Society Research, 7(3), 47-61. [pdf] [online]

Beltman,B.

  1. Stroe,A., Spruit,M., Koelemeijer,S., & Beltman,B. (2016). PMOMM: The Project Management Office Maturity Model. International Journal of Knowledge Society Research, 7(3), 47-61. [pdf] [online]

Christoulakis,M.

  1. Christoulakis,M., Spruit,M., & Dijk,J. van (2015). Data Quality Management in the public domain: A case study within the Dutch Justice System. International Journal of Information Quality, 4(1), 1–17. [pdf] [online]

Vlug,B.

  1. Spruit,M., & Vlug,B. (2015). Effective and Efficient Classification of Topically-Enriched Domain-Specific Text Snippets. International Journal of Strategic Decision Sciences, 6(3), 1–17. [pdf] [online]

Maass,D.

  1. Maass,D., Spruit,M., & Waal,P. de (2014). Improving Short-Term Demand Forecasting For Short-Lifecycle Consumer Products With Data Mining Techniques: A Case Study In The Retail Industry. Decision Analytics, 1(1), 4. [online]

Waal,P. de

  1. Maass,D., Spruit,M., & Waal,P. de (2014). Improving Short-Term Demand Forecasting For Short-Lifecycle Consumer Products With Data Mining Techniques: A Case Study In The Retail Industry. Decision Analytics, 1(1), 4. [online]

Snijders,R.

  1. Snijders,R., & Spruit,M. (2014). Towards Improved Music Recommendation: Using Blogs And Micro-Blogs. International Journal of Multimedia Data Engineering and Management , 5(1), 34–51. [pdf]

Wasmann,M.

  1. Wasmann,M., & Spruit,M. (2012). Performance Management within Social Network Sites: The Social Network Intelligence Process Method. International Journal of Business Intelligence Research, 3(2), April-June 2012, 49–63. [pdf] [online]

Faase,R.

  1. Faase,R., Helms,R., & Spruit,M. (2011). Web 2.0 In The CRM Domain: Defining Social CRM. International Journal of Electronic Customer Relationship Management, 5(1), 1–2. [pdf] [online]

Bezooijen,R. van

  1. Heeringa,W., Nerbonne,J., Bezooijen,R. van, & Spruit,M. (2007). Geografie en inwoneraantallen als verklarende factoren voor variatie in het Nederlandse dialectgebied. Tijdschrift voor Nederlandse taal- en letterkunde, 123(1), Kwantitatieve benaderingen in de taal- en letterkunde, 70–82. [pdf] [online]

Haag,P.

  1. Haag,P., & Spruit,M. (2012). Selecting and implementing Identity and Access Management technologies: the AIM Services Assessment Model. In Sharman,R., Gupta,M., & Das-Smith,S. (Eds.), Digital Identity and Access Management: Technologies and Frameworks (pp. 348–365). IGI Global. [pdf] [online]

Bos,M.

  1. Abdat,N., Spruit,M., & Bos,M. (2011). Software as a Service and the Pricing Strategy for Vendors. In Strader,T. (Ed.), Digital Product Management, Technology and Practice: Interdisciplinary Perspectives, Advances in E-Business Research (AEBR) Book Series (pp. 154–192). IGI Global. [pdf] [online]

Nieuwerth,J.

  1. Nieuwerth,J., Spruit,M., & Zijlstra,D. (2011). An assessment tool for establishing Infrastructure as a Service capability maturity. In Demirkan,H., Spohrer,J., & Krishna,V. (Eds.), Service Systems Implementation volume of Service Science: Research and Innovations (SSRI) in the Service Economy (pp. 133–144). IGI Global. [pdf] [online]

Zijlstra,D.

  1. Nieuwerth,J., Spruit,M., & Zijlstra,D. (2011). An assessment tool for establishing Infrastructure as a Service capability maturity. In Demirkan,H., Spohrer,J., & Krishna,V. (Eds.), Service Systems Implementation volume of Service Science: Research and Innovations (SSRI) in the Service Economy (pp. 133–144). IGI Global. [pdf] [online]

Wijngaert,L. van de

  1. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]

Bos,R.

  1. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]

Jansen,S.

  1. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]

Ravesteijn,P.

  1. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]

Huizer,E.

  1. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]

Baaren,E.

  1. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]

Plomp,M.

  1. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]

Schuur,H. van der

  1. Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217–240). [pdf] [online]

Scheper, W.

  1. Wijaya,S., Spruit,M., & Scheper, W. (2008). Webstrategy Formulation: benefiting from web 2.0 concepts to deliver business values. In Lytras,M., Damiani,E., & Ordóñez de Pablos,P. (Eds.), Web 2.0: The Business Model (pp. 103–132). Springer. [pdf] [online]

Joosten,P.

  1. Spruit,M., & Joosten,P. (2020). Managing student engagement in higher education: The case of CURPA. In Visvizi,A., Lytras,M., & Sarirete,A. (Eds.), Management and Administration of Higher Education Institutions in Times of change (pp. 167–187). Emerald. [pdf]

Rijnst, Sander van der

  1. Spruit,M., & Rijnst, Sander van der (2020). Clinical decision support for infection control in surgical care. In Lytras,M., Visvizi,A., & Sarirete,A. (Eds.), Innovation in Health Informatics: a Smart Healthcare Primer (pp. 101–121). Elsevier. [pdf] [online]

Lammertink,M.

  1. Spruit,M., & Lammertink,M. (2018). Effective and efficient business intelligence dashboard design: Gestalt theory in Dutch long-term and chronic healthcare. In Lytras,M., & Papadopoulou,P. (Eds.), Applying Big Data Analytics in Bioinformatics and Medicine (pp. 243–271). Hershey,PA: IGI Global. [pdf] [online]

Bargh,M.

  1. Dijk,J. van, Bargh,M., Choenni,S., & Spruit,M. (2017). Maturing Pay-as-you-go Data Management: Towards decision support for paying the larger bills. In Helfert,M., Holzinger,A., Belo,O., & Francalanci,C. (Eds.), Data Management Technologies and Applications: 5th International Conference, DATA 2016, Revised Selected papers (pp. 102–124). Springer. [pdf]

Choenni,S.

  1. Dijk,J. van, Bargh,M., Choenni,S., & Spruit,M. (2017). Maturing Pay-as-you-go Data Management: Towards decision support for paying the larger bills. In Helfert,M., Holzinger,A., Belo,O., & Francalanci,C. (Eds.), Data Management Technologies and Applications: 5th International Conference, DATA 2016, Revised Selected papers (pp. 102–124). Springer. [pdf]

Slot,G.

  1. Spruit,M., & Slot,G. (2017). ISFAM 2.0: Revisiting the information security assessment model. In Boskovic, M. (Eds.), Security Risks: Assessment, Management and Current Challenges (pp. 87–108). Nova. [pdf]

Aarnoutse,F.

  1. Aarnoutse,F., Renes,C., Batenburg,R., & Spruit,M. (2016). STRIPA: The potential usefulness of a medical app. In Gasmelseid,T. (Ed.), Advancing Pharmaceutical Processes and Tools for Improved Health Outcomes (pp. 114–135). IGI Global. [pdf] [online]

Houten,R. van den

  1. Houten,R. van den, & Spruit,M. (2015). Proactive Business Intelligence: Discovering Key Performance Indicators with the Rule Extraction Matrix Method. Business Intelligence: Technologies, Applications and Challenges (pp. 1–26). Nova Publishers. [pdf] [online]

Choenni,S.

  1. Dijk,J. van, Choenni,S., Leertouwer,E., Spruit,M., & Brinkkemper,S. (2013). A Data Space System for the Criminal Justice Chain. Lecture Notes in Computer Science 8185, Proceedings of On the Move to Meaningful Internet Systems: OTM 2013 Conferences (pp. 755-763). ODBASE 2013, 10-11 September 2013, Graz, Austria, Springer. [pdf] [online]

Leertouwer,E.

  1. Dijk,J. van, Choenni,S., Leertouwer,E., Spruit,M., & Brinkkemper,S. (2013). A Data Space System for the Criminal Justice Chain. Lecture Notes in Computer Science 8185, Proceedings of On the Move to Meaningful Internet Systems: OTM 2013 Conferences (pp. 755-763). ODBASE 2013, 10-11 September 2013, Graz, Austria, Springer. [pdf] [online]

Booij,E.

  1. Helms,R., Booij,E., & Spruit,M. (2012). Reaching out: Involving users in innovation tasks through social media. Proceedings of the 20th European Conference on Information Systems (pp. Paper 193). ECIS 2012, June 10-13, 2012, Barcelona. [pdf] [online]

Polman,T.

  1. Polman,T., & Spruit,M. (2012). Integrating knowledge engineering and data mining in e-commerce fraud prediction. In Ruan,D., Tennyson,R., Ordonez De Pablos,P., García Peñalvo,F., & Rusu,L. (Eds.), Communications in Computer and Information Science 278, Information Systems, E-learning and Knowledge Management Research for the Knowledge Society: The era of Social Networks, Web 2.0 and Open Source Paradigms (pp. 460-466). Mykonos, 21-23 September 2011: Springer. [pdf] [online]

Kormaris,G.

  1. Kormaris,G., & Spruit,M. (2010). Bridging the Gap between Web 2.0 Technologies and Social Computing Principles. Communications in Computer and Information Science 87, Networked Digital Technologies - Second International Conference (pp. 430–443). NDT 2010, July 7-9, 2010, Prague, Czech Republic. [pdf] [online]

Levantakis,T.

  1. Levantakis,T., Helms,R., & Spruit,M. (2008). Developing a Reference Method for Knowledge Auditing. In Yamagchi,T. (Ed.), Lecture Notes in Artificial Intelligence 5345, Proceedings of the 7th Conference of Practical Aspects on Knowledge Management (pp. 147–159), Appendices are available at http://m.spru.it/files/lhs2008pakm-appendix.pdf?attredirects=0&d=1. PAKM 2008, November 21-23, 2008, Yokohama, Japan: Springer. [pdf]

Haastrecht,M.

  1. Haastrecht,M., Brinkhuis,M., Peichl,J., Remmele,B., & Spruit,M. (In press). Embracing Trustworthiness and Authenticity in the Validation of Learning Analytics Systems. 13th International Conference on Learning Analytics and Knowledge. Arlington, Texas, USA. [pdf]

Peichl,J.

  1. Haastrecht,M., Brinkhuis,M., Peichl,J., Remmele,B., & Spruit,M. (In press). Embracing Trustworthiness and Authenticity in the Validation of Learning Analytics Systems. 13th International Conference on Learning Analytics and Knowledge. Arlington, Texas, USA. [pdf]

Remmele,B.

  1. Haastrecht,M., Brinkhuis,M., Peichl,J., Remmele,B., & Spruit,M. (In press). Embracing Trustworthiness and Authenticity in the Validation of Learning Analytics Systems. 13th International Conference on Learning Analytics and Knowledge. Arlington, Texas, USA. [pdf]

Scheepers,Fl.

  1. Rijcken,E., Mosteiro,P., Zervanou,K., Spruit,M., Scheepers,Fl., & Kaymak,U. (2022). FuzzyTM: a Software Package for Fuzzy Topic Modeling. IEEE International Conference on Fuzzy Systems 2022 (pp. 1–8). IEEE WCCI 2022: FUZZ-IEEE, 18-23 July, Padua, Italy: IEEE.

Van Duijn,M.

  1. Van Duijn,M., Van Dijk,B., & Spruit,M. (2022). Looking from the Inside: How Children Render Character’s Perspectives in Freely Told Fantasy Stories. Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (pp. 66–76). Wordplay 2022, 15 July 2022, Seattle, Washington, United States: ACL. [pdf] [online]

Van Dijk,B.

  1. Van Duijn,M., Van Dijk,B., & Spruit,M. (2022). Looking from the Inside: How Children Render Character’s Perspectives in Freely Told Fantasy Stories. Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (pp. 66–76). Wordplay 2022, 15 July 2022, Seattle, Washington, United States: ACL. [pdf] [online]

Shojaifar,A.

  1. Haastrecht,M. van, Sarhan,I., Shojaifar,A., Baumgartner,L., Mallouli,W., & Spruit,M. (2021). A Threat-Based Cybersecurity Risk Assessment Approach Addressing SME Needs. 16th International Conference on Availability, Reliability and Security (ARES 2021), International Workshop on Security and Privacy for SMEs (pp. Paper 230). SME-SP 2021 at ARES 2021, Aug 17-20, 2021, Vienna, Austria: ACM. [pdf] [online]

Baumgartner,L.

  1. Haastrecht,M. van, Sarhan,I., Shojaifar,A., Baumgartner,L., Mallouli,W., & Spruit,M. (2021). A Threat-Based Cybersecurity Risk Assessment Approach Addressing SME Needs. 16th International Conference on Availability, Reliability and Security (ARES 2021), International Workshop on Security and Privacy for SMEs (pp. Paper 230). SME-SP 2021 at ARES 2021, Aug 17-20, 2021, Vienna, Austria: ACM. [pdf] [online]

Mallouli,W.

  1. Haastrecht,M. van, Sarhan,I., Shojaifar,A., Baumgartner,L., Mallouli,W., & Spruit,M. (2021). A Threat-Based Cybersecurity Risk Assessment Approach Addressing SME Needs. 16th International Conference on Availability, Reliability and Security (ARES 2021), International Workshop on Security and Privacy for SMEs (pp. Paper 230). SME-SP 2021 at ARES 2021, Aug 17-20, 2021, Vienna, Austria: ACM. [pdf] [online]

Scheepers

  1. Rijcken,E., Scheepers, Mosteiro,P., Zervanou,K., Spruit,M., & Kaymak,U.,F. (2021). A Comparative Study of Fuzzy Topic Models and LDA for Interpretability in Text Classification. IEEE Symposium Series on Computational Intelligence (SSCI). Orlando, Florida. [pdf] [online]

Kaymak,U.,F.

  1. Rijcken,E., Scheepers, Mosteiro,P., Zervanou,K., Spruit,M., & Kaymak,U.,F. (2021). A Comparative Study of Fuzzy Topic Models and LDA for Interpretability in Text Classification. IEEE Symposium Series on Computational Intelligence (SSCI). Orlando, Florida. [pdf] [online]

Vries,N. de

  1. Spruit,M., & Vries,N. de (2021). Self-Service Data Science for Adverse Event Prediction in Electronic Healthcare Records. In Visvizi,A., Lytras,M., & Aljohani,N. (Eds.), Springer Proceedings in Complexity, Research and Innovation Forum 2020: Disruptive Technologies in Times of Change (pp. 517–535). RII 2020, Athens, Greece: Springer. [pdf] [online]

Dedding,T.

  1. Spruit,M., Dedding,T., & Vijlbrief,D. (2020). Self-Service Data Science for Healthcare Professionals: A Data Preparation Approach. Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF (pp. 724–734). HEALTHINF 2020, February 24-26, Valletta, Malta: ScitePress. [pdf] [online]

Vijlbrief,D.

  1. Spruit,M., Dedding,T., & Vijlbrief,D. (2020). Self-Service Data Science for Healthcare Professionals: A Data Preparation Approach. Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF (pp. 724–734). HEALTHINF 2020, February 24-26, Valletta, Malta: ScitePress. [pdf] [online]

Bruin,J. de

  1. Menger,V., Spruit,M., Bruin,J. de, Kelder,T., & Scheepers,F. (2019). Supporting Reuse of EHR Data in Healthcare Organizations: the CARED Research Infrastructure Framework. In Moucek,R., Fred,A., & Gamboa,H. (Eds.), Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 5 (pp. 41–50). HEALTHINF 2019, February 22-24, Prague, ScitePress. [pdf] [online]

Kelder,T.

  1. Menger,V., Spruit,M., Bruin,J. de, Kelder,T., & Scheepers,F. (2019). Supporting Reuse of EHR Data in Healthcare Organizations: the CARED Research Infrastructure Framework. In Moucek,R., Fred,A., & Gamboa,H. (Eds.), Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 5 (pp. 41–50). HEALTHINF 2019, February 22-24, Prague, ScitePress. [pdf] [online]

Klift,W. van der

  1. Menger,V., Spruit,M., Klift,W. van der, & Scheepers,F. (2019). Using Cluster Ensembles to Identify Psychiatric Patient Subgroups. In Riaño,D., Wilk,S., & ten Teije,A. (Eds.), Lecture Notes in Computer Science 11526, Artificial Intelligence in Medicine (pp. 252–262). AIME 2019, Poznan, Poland, June 26-29, 2019: Springer. [pdf] [online]

Wang,X.

  1. Shen,Z., Wang,X., & Spruit,M. (2019). Big Data Framework for Scalable and Efficient Biomedical Literature Mining in the Cloud. International Conference Proceedings Series by ACM, NLPIR 2019: Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval (pp. 80–86). NLPIR 2019, Tokushima, Japan: ACM. [pdf] [online]

Meijers,S.

  1. Spruit,M., & Meijers,S. (2019). Big Data for the Masses: The CRISP-DCW Method for Distributed Computing Workflows. In Visvizi,A., & Lytras,M. (Eds.), Springer Proceedings in Complexity, Research & Innovation Forum 2019 (pp. 325–341). RII 2019, Rome, Italy: Springer. [pdf] [online]

Schermerhorn,E.

  1. Lefebvre,A., Schermerhorn,E., & Spruit,M. (2018). How research data management can contribute to efficient and reliable science. 26th European Conference on Information Systems. Portsmouth, UK. [pdf]

Luchies,E.

  1. Luchies,E., Spruit,M., & Askari,M. (2018). Speech Technology in the Dutch Health Care: A Qualitative Study. 11th International Conference on Health Informatics (pp. 339–348). HEALTHINF 2018, 19-21 January 2108, Funchal, Portugal: ScitePress. [pdf]

Zweth,J. van der

  1. Zweth,J. van der, Askari,M., Spruit,M., & Nimwegen,C. van (2018). Devices used for non-invasive tele homecare for cardiovascular patients: A systematic literature review. 11th International Conference on Health Informatics (pp. 300–307). HEALTHINF 2018, 19-21 January 2108, Funchal, Portugal: ScitePress. [pdf]

Nimwegen,C. van

  1. Zweth,J. van der, Askari,M., Spruit,M., & Nimwegen,C. van (2018). Devices used for non-invasive tele homecare for cardiovascular patients: A systematic literature review. 11th International Conference on Health Informatics (pp. 300–307). HEALTHINF 2018, 19-21 January 2108, Funchal, Portugal: ScitePress. [pdf]

Brakenhoff,L.

  1. Brakenhoff,L., & Spruit,M. (2017). Consumer Engagement Characteristics in Mobile Advertising. Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 206–2014). KDIR 2017, November 1-3, 2017, Funchal, Portugal: ScitePress. [pdf]

Schalk,I van der

  1. Schalk,I van der, & Spruit,M. (2017). Sign-Lingo: Feasibility of a Serious Game for Involving Parents in the Language Development of their Deaf or Hearing Impaired Child. In Broek,E. van der, Fred,A., Gamboa,H., & Vaz,M. (Eds.), Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) (pp. 191-198). HEALTHINF 2017, Febr 21-23, 2017, Porto, Portugal: SciTePress. [pdf] [online]

Jagesar,R.

  1. Spruit,M., & Jagesar,R. (2016). Power to the People! Meta-algorithmic modelling in applied data science. In Fred,A. et al. (Ed.), Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 400–406). KDIR 2016, November 11-13, 2016, Porto, Portugal: ScitePress. [pdf]

Haasbroek,J.

  1. Haasbroek,J., & Spruit,M. (2015). De ideale docent anno 2015: Docentgedrag en studenttevredenheid binnen het universitaire bachelor onderwijs. Onderwijs Research Dagen . Leiden. [pdf]

Omta,W

  1. Lefebvre,A., Spruit,M., & Omta,W (2015). Towards reusability of computational experiments: Capturing and sharing Research Objects from knowledge discovery processes. Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 456–462). KDIR 2015, November 12-14, Lisbon, Portugal: ScitePress. [pdf] [online]

Cepoi,A.

  1. Spruit,M., & Cepoi,A. (2015). CIRA: A competitive intelligence reference architecture for dynamic solutions. Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 249–258). KDIR 2015, November 12-14, Lisbon, Portugal: ScitePress. [pdf] [online]

Visee,Y.

  1. Spruit,M., Visee,Y., & Jong,E. de (2015). DIA: het Docent-ICT Adoptie raamwerk - Verbinden van onderwijsvormen en onderwijstechnieken via onderwijstaken. Onderwijs Research Dagen . Leiden. [pdf]

Jong,E. de

  1. Spruit,M., Visee,Y., & Jong,E. de (2015). DIA: het Docent-ICT Adoptie raamwerk - Verbinden van onderwijsvormen en onderwijstechnieken via onderwijstaken. Onderwijs Research Dagen . Leiden. [pdf]

Meulendijks,E.

  1. Meulendijk,M., Meulendijks,E., Jansen,P., Numans,M., & Spruit,M. (2014). What concerns users of medical apps? Exploring non-functional requirements of medical mobile applications. 22nd European Conference on Information Systems. Tel Aviv, Israel. [pdf] [online]

Roeling,M.

  1. Spruit,M., & Roeling,M. (2014). ISFAM: the Information Security Focus Area Maturity model. 22nd European Conference on Information Systems. Tel Aviv, Israel. [pdf] [online]

Peersman,H.

  1. Peersman,H., Batenburg,R., & Spruit,M. (2013). Preventing credit card data breaches. A framework of critical indicators. In Shahim,Abbas (Ed.), IFIP TC11 Conference on IT Assurance and Audit. VU University Amsterdam. [pdf]

Koelemeijer,S

  1. Stroe,A., Koelemeijer,S, & Spruit,M. (2013). Een PMO is meer dan een administratiekantoor. Controllers Magazine. Management Accounting & Control. [online]

Wester,W.

  1. Spruit,M., & Wester,W. (2013). RFID Security and Privacy: Threats and Countermeasures. Technical report UU-CS-2013-001, Department of Information and Computing Sciences, Utrecht University. [pdf] [online]

Habers,F.

  1. Sacu,C., Spruit,M., & Habers,F. (2010). Data Warehouse Maturity Assessment Questionnaire. Technical report UU-CS-2010-021, Department of Information and Computing Sciences, Utrecht University. [pdf] [online]

ETSI

  1. ETSI (2021). CYBER; Cybersecurity for SMEs; Part 1: Cybersecurity Standardization Essentials. ETSI TR 103 787-1. Authored by B Yigit Ozkan & M Spruit. [online]

Joosten,L.

  1. Joosten,L., & Spruit,M. (2021). Sentiment analysis of Dutch tweets: a comparison of automatic and manual sentiment analysis. Annotated dataset for sentiment analysis of Dutch Twitter messages. [online]

Haan,E. de

  1. Haan,E. de, Spruit,M., & Zoet,M. (2019). Fundamental Constructs for Derivation Business Rules. Technical report UU-CS-2019-010, Department of Information and Computing Sciences, Utrecht University. [online]

Zoet,M.

  1. Haan,E. de, Spruit,M., & Zoet,M. (2019). Fundamental Constructs for Derivation Business Rules. Technical report UU-CS-2019-010, Department of Information and Computing Sciences, Utrecht University. [online]

Janssen,J.

  1. Janssen,J., & Spruit,M. (2019). M-RAM: a Mobile Risk Assessment Method for Enterprise Mobile Security. Technical report UU-CS-2019-009, Department of Information and Computing Sciences, Utrecht University. [online]

Berendsen,J.

  1. Lefebvre,A., Berendsen,J., & Spruit,M. (2019). Evaluation of classification models for retrieving experimental sections from full-text publications. Technical report UU-CS-2019-002, Department of Information and Computing Sciences, Utrecht University. [online]

Lingen,S. van

  1. Spruit,M, Lingen,S. van, & Yigit Ozkan,B. (2019). The CYSFAM Questionnaire: Assessing CYberSecurity Focus Area Maturity. Technical report UU-CS-2019-003, Department of Information and Computing Sciences, Utrecht University. [online]

Linden,V. van der

  1. Spruit,M., & Linden,V. van der (2019). BIDQI: The Business Impacts of Data Quality Interdependencies Model. Technical report UU-CS-2019-001, Department of Information and Computing Sciences, Utrecht University. [online]

Shen, Z.

  1. Shen, Z., Meulendijk,M., Knol,W., Huibers,L., Wilting,I., Jansen,P., & Spruit,M. (2016). STRIPA Investigational Medical Device Dossier (IMDD). version 2.03, Department of Information and Computing Sciences, Utrecht University.

Meulendijk,M.

  1. Shen, Z., Meulendijk,M., Knol,W., Huibers,L., Wilting,I., Jansen,P., & Spruit,M. (2016). STRIPA Investigational Medical Device Dossier (IMDD). version 2.03, Department of Information and Computing Sciences, Utrecht University.

Huibers,L.

  1. Shen, Z., Meulendijk,M., Knol,W., Huibers,L., Wilting,I., Jansen,P., & Spruit,M. (2016). STRIPA Investigational Medical Device Dossier (IMDD). version 2.03, Department of Information and Computing Sciences, Utrecht University.

Jansen,P.

  1. Shen, Z., Meulendijk,M., Knol,W., Huibers,L., Wilting,I., Jansen,P., & Spruit,M. (2016). STRIPA Investigational Medical Device Dossier (IMDD). version 2.03, Department of Information and Computing Sciences, Utrecht University.

Reijmer,T.

  1. Reijmer,T., & Spruit,M. (2014). Cybersecurity in the news: A grounded theory approach to better understand its emerging prominence. Technical report UU-CS-2014-006, Department of Information and Computing Sciences, Utrecht University. [online]

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