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By Topic

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

Business Intelligence:64Data Analytics:61Text Analytics:58Health Informatics:53Information Security:30Social Media:17IT Strategy:15 Data Analytics:7... Business Intelligence:1

2023-01-20 16:48:56 (GMT)

Business Intelligence

  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. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. Pieket Weeserik,B., & Spruit,M. (2018). Improving Operational Risk Management using Business Performance Management technologies. Sustainability, 10(3), 640. [JIF: 3.251] [online]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. Lefebvre,A., Bakhtiari,B., & Spruit,M. (2020). Exploring Research Data Management Planning Challenges in Practice. it – Information Technology, 62(1), 29–37. [pdf] [online]
  16. 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]
  17. 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]
  18. 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]
  19. Otten,S., Spruit,M., & Helms,R. (2015). Towards decision analytics in product portfolio management. Decision Analytics, 2(4). [online]
  20. 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]
  21. Spruit,M., & Adriana,T. (2015). Quantifying education quality in secondary schools. International Journal of Knowledge Society Research, 6(1), 55-87. [pdf] [online]
  22. 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]
  23. 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]
  24. 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]
  25. 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]
  26. 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]
  27. 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]
  28. 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]
  29. 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]
  30. 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]
  31. 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]
  32. 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]
  33. 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]
  34. 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]
  35. 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]
  36. 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]
  37. 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]
  38. 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]
  39. 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]
  40. 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]
  41. 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]
  42. 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]
  43. 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]
  44. 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]
  45. 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]
  46. 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]
  47. 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]
  48. 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]
  49. 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]
  50. 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]
  51. 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]
  52. 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]
  53. 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]
  54. Spruit,M. (2013). Selecting data quality dimensions: towards a business impacts assessment. 6th World Summit on the Knowledge Society. WSKS 2013, June 19-21, Aveiro, Portugal. [pdf]
  55. 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]
  56. Stroe,A., Koelemeijer,S, & Spruit,M. (2013). Een PMO is meer dan een administratiekantoor. Controllers Magazine. Management Accounting & Control. [online]
  57. 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]
  58. 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]
  59. 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]
  60. 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]
  61. 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.
  62. 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]
  63. 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]
  64. 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]

Data Analytics

  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. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. Spruit,M. (2006). Measuring syntactic variation in Dutch dialects. Literary and Linguistic Computing, 21(4), Progress in Dialectometry: Toward Explanation, 493–506. [JIF: 0.894] [pdf] [online]
  23. 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]
  24. Spruit,M. (2009). Towards linguistic knowledge discovery in language variation databases. Zeitschrift für Dialektologie und Linguistik, ZDL-Beiheft 138, Low Saxon Dialects across borders, 179–193. [JIF: 0.125] [pdf] [online]
  25. 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]
  26. 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]
  27. 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]
  28. 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]
  29. Otten,S., Spruit,M., & Helms,R. (2015). Towards decision analytics in product portfolio management. Decision Analytics, 2(4). [online]
  30. 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]
  31. 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]
  32. 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]
  33. 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]
  34. 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]
  35. Spruit,M. (2008). Quantitative perspectives on syntactic variation in Dutch dialects. LOT Dissertation Series 174, Doctoral disseration, University of Amsterdam, The Netherlands, Utrecht: LOT. [pdf] [online]
  36. Spruit,M. (2022). Translational Data Science in Population Health. Inaugural lecture on the acceptance of the position of professor of Advanced Data Science in Population Health on 1 April 2022, [NL] [EN] transcripts, Leiden: Leiden University. [pdf] [online]
  37. 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]
  38. 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]
  39. 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]
  40. 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]
  41. 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]
  42. 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]
  43. Spruit,M. (2007). Discovery of association rules between syntactic variables: Data mining the Syntactic atlas of the Dutch dialects. In Dirix,P., Schuurman,I., Vandeghinste,V., & Eynde,F. van (Eds.), Computational Linguistics in the Netherlands 2006: Selected papers from the seventeenth CLIN meeting (pp. 83–98). Utrecht: LOT Occasional Series. [pdf] [online]
  44. Spruit,M. (2005). Classifying Dutch dialects using a syntactic measure: The perceptual Daan and Blok dialect map revisited. Linguistics in the Netherlands 2005 (pp. 179–190). Amsterdam: John Benjamins. [pdf] [online]
  45. 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]
  46. 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]
  47. 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]
  48. 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]
  49. 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]
  50. 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]
  51. 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]
  52. 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]
  53. 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]
  54. 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]
  55. 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]
  56. 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]
  57. 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]
  58. 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]
  59. 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]
  60. 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]
  61. 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]

Text Analytics

  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. Sarhan,I., & Spruit,M. (2021). Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph. Knowledge-Based Systems, 233(107524). [JIF: 8.038] [pdf] [online]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. Spruit,M. (2006). Measuring syntactic variation in Dutch dialects. Literary and Linguistic Computing, 21(4), Progress in Dialectometry: Toward Explanation, 493–506. [JIF: 0.894] [pdf] [online]
  17. 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]
  18. Spruit,M. (2009). Towards linguistic knowledge discovery in language variation databases. Zeitschrift für Dialektologie und Linguistik, ZDL-Beiheft 138, Low Saxon Dialects across borders, 179–193. [JIF: 0.125] [pdf] [online]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. 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]
  25. 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]
  26. 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]
  27. 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]
  28. Spruit,M. (2008). Quantitative perspectives on syntactic variation in Dutch dialects. LOT Dissertation Series 174, Doctoral disseration, University of Amsterdam, The Netherlands, Utrecht: LOT. [pdf] [online]
  29. Spruit,M. (2022). Translational Data Science in Population Health. Inaugural lecture on the acceptance of the position of professor of Advanced Data Science in Population Health on 1 April 2022, [NL] [EN] transcripts, Leiden: Leiden University. [pdf] [online]
  30. Spruit,M. (1995). FILTER prototype. In Scholtes,J. (Ed.), Artificial neural networks for information retrieval in a libraries context (pp. 213–251). European Commission, DG XIII-E3. [pdf] [online]
  31. 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]
  32. Spruit,M. (2007). Discovery of association rules between syntactic variables: Data mining the Syntactic atlas of the Dutch dialects. In Dirix,P., Schuurman,I., Vandeghinste,V., & Eynde,F. van (Eds.), Computational Linguistics in the Netherlands 2006: Selected papers from the seventeenth CLIN meeting (pp. 83–98). Utrecht: LOT Occasional Series. [pdf] [online]
  33. Spruit,M. (2005). Classifying Dutch dialects using a syntactic measure: The perceptual Daan and Blok dialect map revisited. Linguistics in the Netherlands 2005 (pp. 179–190). Amsterdam: John Benjamins. [pdf] [online]
  34. 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.
  35. 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]
  36. 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]
  37. 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]
  38. 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]
  39. 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]
  40. 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]
  41. 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]
  42. 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]
  43. 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]
  44. 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]
  45. 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]
  46. 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]
  47. 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]
  48. 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]
  49. 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]
  50. 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]
  51. 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]
  52. 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]
  53. 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]
  54. 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]
  55. 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]
  56. 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]
  57. 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]
  58. 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]

Health Informatics

  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. 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. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. 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]
  25. 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]
  26. 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]
  27. 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]
  28. 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]
  29. 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]
  30. 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]
  31. Omta,W., Egan,D., Spruit,M., & Brinkkemper,S. (2012). Information Architecture in High Throughput Screening. Procedia Technology, 5, 696–705. [pdf] [online]
  32. 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]
  33. 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]
  34. 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]
  35. 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]
  36. 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]
  37. 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]
  38. 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]
  39. 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]
  40. 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]
  41. 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]
  42. 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]
  43. 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]
  44. 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]
  45. 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]
  46. 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]
  47. 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]
  48. 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]
  49. 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]
  50. 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]
  51. 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]
  52. 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]
  53. 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]

Information Security

  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,Bilge, & Spruit,M. (2022). Adaptable Security Maturity Assessment and Standardization for Digital SMEs. Journal of Computer Information Systems. [JIF: 3.4] [pdf] [online]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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.
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. Spruit,M., & Roeling,M. (2014). ISFAM: the Information Security Focus Area Maturity model. 22nd European Conference on Information Systems. Tel Aviv, Israel. [pdf] [online]
  25. 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]
  26. 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]
  27. ETSI (2021). CYBER; Cybersecurity for SMEs; Part 1: Cybersecurity Standardization Essentials. ETSI TR 103 787-1. Authored by B Yigit Ozkan & M Spruit. [online]
  28. 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]
  29. 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]
  30. 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]

Social Media

  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. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]

IT Strategy

  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. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]

Data Analytics

  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. 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. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]

Cloud Computing

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]

Knowledge Management

  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. 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]
  5. 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]
  6. 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]
  7. 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]

Health Informatics

  1. 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]
  2. Spruit,M. (2022). Translational Data Science in Population Health. Inaugural lecture on the acceptance of the position of professor of Advanced Data Science in Population Health on 1 April 2022, [NL] [EN] transcripts, Leiden: Leiden University. [pdf] [online]
  3. 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]
  4. 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.

Software Product Management

  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]

Education

  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. Haasbroek,J., & Spruit,M. (2015). De ideale docent anno 2015: Docentgedrag en studenttevredenheid binnen het universitaire bachelor onderwijs. Onderwijs Research Dagen . Leiden. [pdf]
  3. 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]

Information Security

  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. 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]

Text Analytics

  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. 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]

Data analytics

  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]

IT Sustainability

  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]

Knowledge Discovery

  1. 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]

Business Intelligence

  1. Spruit,M. (1995). FILTER prototype. In Scholtes,J. (Ed.), Artificial neural networks for information retrieval in a libraries context (pp. 213–251). European Commission, DG XIII-E3. [pdf] [online]