Marco Spruit
Professor Advanced Data Science in Population Health
- Name
- Prof.dr. M.R. Spruit
- Telephone
- +31 71 526 9111
- m.r.spruit@lumc.nl
- ORCID iD
- 0000-0002-9237-221X
Marco Spruit is Professor Advanced Data Science in Population Health at the department of Public Health & Primary Care (PHEG) of the Faculty of Medicine (LUMC) and the Leiden Institute of Advanced Computer Science (LIACS) at the Faculty of Science (FWN) of Leiden University in the Netherlands. He is interested both in translating new algorithms to novel health applications as in implementing new insights from these novel applications into daily practices. Marco’s strategic research objective is to establish an authoritative national infrastructure for Dutch Natural Language Processing and Machine Learning to democratise Data Science. He focuses in particular on the Population Health and Wellbeing domain in his Translational Data Science Lab.
Marco Spruit is Professor Advanced Data Science in Population Health at the department of Public Health & Primary Care (PHEG) of the Faculty of Medicine (LUMC) and the Leiden Institute of Advanced Computer Science (LIACS) at the Faculty of Science (FWN) of Leiden University in the Netherlands. He is interested both in translating new algorithms to novel health applications as in implementing new insights from these novel applications into daily practices.
Marco’s strategic research objective is to establish an authoritative national infrastructure for Dutch Natural Language Processing and Machine Learning to democratise Data Science. He focuses in particular on the Population Health and Wellbeing domain in his Translational Data Science Lab.
Marco leads the research line Translational Data Science in Population Health at the Health Campus The Hague. This research line has three themes. First, in Data Engineering he investigates the further consolidation, standardisation and enrichment of the Extramural LUMC Academic Network (ELAN) data infrastructure, in line with national initiatives and in collaboration with his PHEG colleagues. Second, in Data Analytics he investigates Natural Language Processing and Machine Learning techniques for their suitability to answer current and novel types of translational research questions, especially from a democratising Data Science perspective, in collaboration with his LIACS colleagues. Third, in e-Health Implementation Marco designs and implements Data Science interventions through e-Health software solutions within the region in close collaboration with the Campus partners.
Until 2020 Marco worked as associate professor in the Natural Language Processing research group at the department of Information and Computing Sciences at Utrecht University, where he notably conducted numerous European-funded studies (OPERAM, SAF21, SMESEC, GEIGER, OPTICA) and nationally funded research projects (STRIMP, COVIDA). He participated in various leadership programmes and obtained academic qualifications such the Senior Research Qualification, Senior Teaching Qualification, and Ius Promovendi. From 2007-2018 he was an assistant professor Information Science, acting as the Information Science and Applied Data Science programmes manager for several years, among others.
From 2003-2007 Marco worked as a Ph.D. researcher in the Language Variation group of the Meertens Institute at the intersection of syntactic variation and dialectometry as a linguistic data scientist. In 2005 he notably received an Association for Literary and Linguistic Computing bursary award for his scientific work. Before 2003 he was active in industry for ten years as a Natural Language Processing and Big Data engineer at ZyLAB Europe B.V. and the Royal Dutch Navy, among others. In 1995 he graduated in Computational Linguistics at the University of Amsterdam.
Professor Advanced Data Science in Population Health
- Faculteit Geneeskunde
- Divisie 3
- Public Health en Eerstelijnsgeneeskunde
Professor Advanced Data Science in Population Health
- Science
- Leiden Inst of Advanced Computer Science
- Dijk B.M.A. van, Spruit M.R. & Duijn M.J. van (2023), Theory of mind in freely-told children’s narratives: a classification approach. Rogers A., Boyd-Graber J. & Okazaki N. (Eds.), Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics 9 July 2023 - 13 July 2023. Toronto: Assocation for Computational Linguistics. 12979-12993.
- Ardesch F.H., Meulendijk M.C., Kist J.M., Vos R.C., Vos H.M.M., Kiefte-de Jong J.C., Spruit M.R., Bruijnzeels M.A., Bussemaker J., Numans M.E. & Struijs J.N. (2023), The introduction of a data-driven population health management approach in the Netherlands since 2019: the extramural LUMC academic network data infrastructure, Health Policy 132: 104769.
- Dijk B.M.A. van, Duijn M.J. van, Verberne S. & Spruit M. (2023), ChiSCor: a corpus of freely-told fantasy stories by Dutch children for computational linguistics and cognitive science. Jing J., Reitter D. & Deng S. (Eds.), Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL). 27th Conference on Computational Natural Language Learning (CoNLL) 6 December 2023 - 7 December 2023. Singapore: Association for Computational Linguistics. 352–363.
- Haastrecht M.A.N. van, Brinkhuis M.J.S., Wools S. & Spruit M.R. (2023), VAST: a practical validation framework for e-assessment solutions, Information Systems and E-Business Management 21: 603-627.
- Haastrecht M.A.N. van, Brinkhuis M.J.S., Peichl J., Remmele B. & Spruit M. (2023), Embracing trustworthiness and authenticity in the validation of learning analytics systems, Proceedings of the 13th International Learning Analytics and Knowledge Conference. 13th International Learning Analytics and Knowledge Conference 13 March 2023 - 17 March 2023. New York, NY, USA: Association for Computing Machinery (ACM). 552-558.
- Duijn M.J. van, Dijk B.M.A. van, Kouwenhoven T., Valk W. de, Spruit M. & Putten P.W.H. van der (2023), Theory of mind in large language models: examining performance of 11 state-of-the-art models vs. children aged 7-10 on advanced tests. Jiang J., Reitter D. & Deng S. (Eds.), Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL). 27th Conference on Computational Natural Language Learning (CoNLL) 6 December 2023 - 7 December 2023. Singapore: Association for Computational Linguistics. 389–402.
- Ferguson R., Khosravi H., Kovanović V., Viberg O., Aggarwal A., Brinkhuis M.J.S., Buckingham Shum S., Chen L., Drachsler H., Guerrero V.A., Hanses M., Hayward C., Hicks B., Jivet I., Kitto K, Kizilcec R., Lodge J.M., Manly C.A., Matz R.L., Meaney M.J., Ochoa X., Schuetze B.A., Spruit M.R., Haastrecht M.A.N. van, Leeuwen A. van, Rijn L. van, Tsai Y.S., Weidlich J., Williamson K. & Yan V.X. (2023), Aligning the goals of learning analytics with its research scholarship: an open peer commentary approach, Journal of Learning Analytics 10(2): 14-50.
- Dijk B.M.A. van, Kouwenhoven T., Spruit M. & Duijn M.J. van (2023), Large language models: the need for nuance in current debates and a pragmatic perspective on understanding. Bouamor H., Pino J. & Bali K. (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023 Conference on Empirical Methods in Natural Language Processing 6 December 2023 - 10 December 2023. Singapore: Association for Computational Linguistics. 12641–12654.
- Toledo, C. van; Schraagen, M.; Dijk, F. van; Brinkhuis, M. & Spruit, M. (2023), Readability metrics for machine translation in Dutch, Applied Sciences 13(7).
- Haastrecht, M. van; Brinkhuis, M.; Wools, S. & Spruit, M. (2023), VAST, Information Systems and E-Business Management.
- Jungo, K.T.; Ansorg, A.; Floriani, C.; Rozsnyai, Z.; Schwab, N.; Meier, R.; Valeri, F.; Stalder, O.; Limacher, A.; Schneider, C.; Bagattini, M.; Trelle, S.; Spruit, M.; Schwenkglenks, M.; Rodondi, N. & Streit, S. (2023), Optimizing prescribing in older adults with multimorbidity and polypharmacy in primary care: a cluster randomized clinical trial, Journal of the American Geriatrics Society 71: S122-S122.
- Jungo, K.T.; Ansorg, A.K.; Floriani, C.; Rozsnyai, Z.; Schwab, N.; Meier, R.; Valeri, F.; Stalder, O.; Limacher, A.; Schneider, C.; Bagattini, M.; Trelle, S.; Spruit, M.; Schwenkglenks, M.; Rodondi, N. & Streit, S. (2023), Optimising prescribing in older adults with multimorbidity and polypharmacy in primary care (OPTICA), British Medical Journal (BMJ) 381.
- Haastrecht van Max , Brinkhuis Matthieu , Peichl Jessica , Remmele Bernd & Spruit Marco (2023), Embracing Trustworthiness and Authenticity in the Validation of Learning Analytics Systems.
- Ardesch, F.H.; Meulendijk, M.C.; Kist, J.M.; Vos, R.C.; Vos, H.M.M.; Kiefte-de Jong, J.C.; Spruit, M.; Bruijnzeels, M.A.; Bussemaker, M.J.; Numans, M.E. & Struijs, J.N. (2023), The introduction of a data-driven population health management approach in the Netherlands since 2019, Health Policy 132.
- Lefebvre, A. & Spruit, M. (2023), Laboratory Forensics for Open Science Readiness: an Investigative Approach to Research Data Management, Information Systems Frontiers 25(1): 381-399.
- Duijn M.J. van, Dijk B.M.A. van & Spuit M.R. (2022), Looking from the inside: how children render character’s perspectives in freely told fantasy stories. Clark E., Brahman F. & Iyyer M. (Eds.), ACL Proceedings of the 4th workshop of narrative understanding (WNU2022). . Seattle 66-76.
- 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).
- Ozkan, B.Y. & Spruit, M. (2022), Adaptable Security Maturity Assessment and Standardization for Digital SMEs, Journal of Computer Information Systems.
- Haastrecht, M. van; Golpur, G.; Tzismadia, G.; Kab, R.; Priboi, C.; David, D.; Racataian, A.; Baumgartner, L.; Fricker, S.; Ruiz, J.F.; Armas, E.; Brinkhuis, M. & Spruit, M. (2022), Correction: van Haastrecht et al. a shared cyber threat itelligence solution for SMEs. Electronics 2021, 10, 2913, Electronics 11(3).
- Mosteiro, P.; Kuiper, J.; Masthoff, J.; Scheepers, F. & Spruit, M. (2022), Bias discovery in machine learning models for mental health, Information 13(5).
- Siegersma, K.R.; Evers, M.; Bots, S.H.; Groepenhoff, F.; Appelman, Y.; Hofstra, L.; Tulevski, I.I.; Somsen, G.A.; Ruijter, H.M. den; Spruit, M. & Onland-Moret, N.C. (2022), Development of a pipeline for adverse drug reaction identification in clinical notes , JMIR Medical Informatics 10(1).
- Spruit, M.; Verkleij, S.; Schepper, K. de & Scheepers, F. (2022), Exploring language markers of mental health in psychiatric stories, Applied Sciences 12(4).
- Borger, T.; Mosteiro, P.; Kaya, H.; Rijcken, E.; Salah, A.A.; Scheepers, F. & Spruit, M. (2022), Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting, Expert Systems with Applications 199.
- 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.
- Spruit M.R. & Vries N. de (2021), Self-service data science for adverse event prediction in electronic healthcare records. Visvizi A., Lytras M.D. & Aljohani N.R. (Eds.), Research and Innovation Forum 2020: Disruptive Technologies in Times of Change. Research and Innovation Forum 2020: Disruptive Technologies in Times of Change 7 April 2020 - 9 April 2020. Cham: Springer. 517--535.
- Shen Z. & Spruit M.R. (2021), Automatic extraction of adverse drug reactions from summary of product characteristics, Applied Sciences 11(6): 2663.
- Mosteiro P., Rijcken E., Zervanou K., Kaymak U., Scheepers F. & Spruit M.R. (2021), Machine learning for violence risk assessment using Dutch clinical notes, Journal of Artificial Intelligence for Medical Sciences 2(1): 44-54.
- Ozkan B.Y., Lingen S. & Spruit M.R. (2021), The Cybersecurity Focus Area Maturity (CYSFAM) model, Journal of Cybersecurity and Privacy 1(1): 119-139.
- Menger V.J., Spruit M.R. & Scheepers F.E. (2021), Kennisontwikkeling in de klinische psychiatrie: leren van elektronische patiëntendossiers, Tijdschrift voor Psychiatrie 63(4): 294-300.
- Jungo K.T., Meier R., Valeri F., Schwab N., Schneider C., Reeve E., Spruit M.R., 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(1): 123.
- 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: 685591.
- Haastrecht M. van, Ozkan B.Y., Brinkhuis M. & Spruit M. (2021), Respite for SMEs: A systematic review of socio-technical cybersecurity metrics, Applied Sciences 11(15): 6909.
- Blum M.R., Sallevelt B.T.G.M., Spinewine A., O'Mahony D., Moutzouri E., Feller M., Baumgartner C., Roumet M., Jungo K.T., Schwat N., Bretagne L., Beglinger S., Aubert C.E., Wilting I., Thevelin S., Murphy K., Huibers C.J.A. Drenth-van Maanen A.C., Boland B., Crowley E., Eichenberger A., Meulendijk M., Jennings E., Adam L., Roos M.J., Gleeson L., Shen Z.R., Marien S., Meinders A.J., Baretella O., Netzer S., Montmollin M., Fournier A., Mouzon A., O'Mahony C., Aujesky D., Mavridis D., Byrne S., Jansen P.A.F., Schwenkglenks M., Spruit M.R., Dalleur O., Knol W., Trelle S. & Rodondi N. (2021), Optimizing therapy to prevent avoidable hospital admissions in multimorbid older adults (OPERAM): cluster randomised controlled trial, BMJ 374: n1585.
- Haastrecht M. van, Ozkan B.Y., Brinkhuis M. & Spruit M. (2021), Respite for SMEs: A systematic review of socio-technical cybersecurity metrics, Applied Sciences 11(15): 6909.
- Haastrecht M., Sarhan I., Shojaifar A., Baumgartner L., Mallouli W. & Spruit M. (2021), A threat-based cybersecurity risk assessment approach addressing SME needs. In: ARES 2021: The 16th International Conference on Availability, Reliability and Security.: Association for Computing Machinery (ACM). 158.
- Haastrecht M.A.N. van, Golpur G., Tzismadia G., Kab R., Priboi C, David D., Răcătăian A., Baumgartner L., Fricker S., Ruiz J.F., Armas E., Brinkhuis M. & Spruit M.R. (2021), A shared cyber threat intelligence solution for SMEs, Electronics 10(23): 2913.
- Spruit M.R., Kais M. & Menger V. (2021), Automated business goal extraction from e-mail repositories to bootstrap business understanding, Future Internet 13(10): 243.
- Lefebvre A. & Spruit M.R. (2021), Laboratory forensics for open science readiness: an investigative approach to research data management, Information Systems Frontiers : .
- Sarhan I. & Spruit M.R. (2021), Open-CyKG: an open cyber threat intelligence knowledge graph, Knowledge-Based Systems 233: 107524.
- Smit T., Haastrecht M.A.N. van & Spruit M.R. (2021), The effect of countermeasure readability on security intentions, Journal of Cybersecurity and Privacy 1(4): 675-704.
- Spruit, M.; Kais, M. & Menger, V. (2021), Automated business goal extraction from e-mail repositories to bootstrap business understanding, Future Internet 13(10).
- Sarhan, I. & Spruit, M. (2021), Open-CyKG: an Open Cyber Threat Intelligence Knowledge Graph, Knowledge-Based Systems 233.
- Ozkan BY, van Lingen S & Spruit M (2021), The Cybersecurity Focus Area Maturity (CYSFAM) model, Journal of Cybersecurity and Privacy 1(1): 119--139.
- Spruit Marco & Vries de Niels (2021), Self-Service Data Science for Adverse Event Prediction in Electronic Healthcare Records.
- Ozkan Yigit Bilge & Spruit Marco (2021), Cybersecurity Standardisation for SMEs.
- Haastrecht, M. van; Ozkan, B.Y.; Brinkhuis, M. & Spruit, M. (2021), Respite for SMEs: a systematic review of socio-technical cybersecurity metrics, APPLIED SCIENCES-BASEL 11(15).
- Blum, M.R.; Sallevelt, B.T.G.M.; Spinewine, A.; O'Mahony, D.; Moutzouri, E.; Feller, M.; Baumgartner, C.; Roumet, M.; Jungo, K.T.; Schwab, N.; Bretagne, L.; Beglinger, S.; Aubert, C.E.; Wilting, I.; Thevelin, S.; Murphy, K.; Huibers, C.J.A.; Drenth-van Maanen, A.C.; Boland, B.; Crowley, E.; Eichenberger, A.; Meulendijk, M.; Jennings, E.; Adam, L.; Roos, M.J.; Gleeson, L.; Shen, Z.R.; Marien, S.; Meinders, A.J.; Baretella, O.; Netzer, S.; Montmollin, M. de; Fournier, A.; Mouzon, A.; O'Mahony, C.; Aujesky, D.; Mavridis, D.; Byrne, S.; Jansen, P.A.F.; Schwenkglenks, M.; Spruit, M.; Dalleur, O.; Knol, W.; Trelle, S. & Rodondi, N. (2021), Optimizing therapy to prevent avoidable hospital admissions in multimorbid older adults (OPERAM), BRITISH MEDICAL JOURNAL 374.
- Lefebvre, A. & Spruit, M. (2021), Laboratory forensics for open science readiness, Information Systems Frontiers.
- Jungo, K.T.; 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(1).
- Haastrecht Mv, 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(6).
- Menger V, Spruit M & Scheepers F (2021), Kennisontwikkeling in de klinische psychiatrie: leren van elektronische patiëntendossiers, Tijdschrift voor Psychiatrie 63(4): 294–300.
- Mosteiro P, Rijcken E, Zervanou K, Kaymak U, Scheepers F & Spruit M (2021), Machine learning for violence risk assessment using Dutch clinical notes, Jama Network Open 2(1–2): 44–54.
- Dijk Fv, Spruit M, Toledo Cv & Brinkhuis M (2021), Pillars of Privacy: Identifying Core Theory in a Network Analysis of Privacy.
- Haastrecht M, Sarhan I, Shojaifar A, Baumgartner L, Mallouli W & Spruit M (2021), A Threat-Based Cybersecurity Risk Assessment Approach Addressing SME Needs.
- Sallevelt, B.T.G.M.; Huibers, C.J.A.; Heij, J.M.J.O.; Egberts, T.C.G.; Puijenbroek, E.P. van; Shen, Z.R.; Spruit, M.R.; Jungo, K.T.; 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 and Aging 39.
- Haastrecht, M. van; Golpur, G.; Tzismadia, G.; Kab, R.; Priboi, C.; David, D.; Racataian, A.; Brinkhuis, M. & Spruit, M. (2021), A shared cyber threat intelligence solution for SMEs, Electronics 10(23).
- Shen, Z.R. & Spruit, M. (2021), Automatic extraction of adverse drug reactions from summary of product characteristics, APPLIED SCIENCES-BASEL 11(6).
- Meulendijk, M.C.; Spruit, M.R.; Willeboordse, F.; Numans, M.E.; Brinkkemper, S.; Knol, W.; Jansen, P.A.F. & Askari, M. (2016), Efficiency of Clinical Decision Support Systems Improves with Experience, Journal of Medical Systems 40(4).
- Meulendijk, M.C.; Spruit, M.R.; Drenth-van Maanen, A.C.; Numans, M.E.; Brinkkemper, S.; Jansen, P.A.F. & Knol, W. (2015), Computerized Decision Support Improves Medication Review Effectiveness: An Experiment Evaluating the STRIP Assistant's Usability, Drugs and Aging 32(6): 495-503.
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