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
- Ozkan BY, van Lingen S & Spruit M (2021), The Cybersecurity Focus Area Maturity (CYSFAM) Model, Journal of Cybersecurity and Privacy 1(1): 119--139.
- 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.
- Sarhan I. & Spruit M.R. (2021), Open-CyKG: an open cyber threat intelligence knowledge graph, Knowledge-Based Systems 233: 107524.
- Sarhan, I. & Spruit, M. (2021), Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph, Knowledge-Based Systems 233.
- 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).
- 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.
- 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.R., Kais M. & Menger V. (2021), Automated business goal extraction from e-mail repositories to bootstrap business understanding, Future Internet 13(10): 243.
- Spruit, M.; Kais, M. & Menger, V. (2021), Automated business goal extraction from e-mail repositories to bootstrap business understanding, Future Internet 13(10).
- Lefebvre A. & Spruit M.R. (2021), Laboratory forensics for open science readiness: an investigative approach to research data management, Information Systems Frontiers.
- Lefebvre, A. & Spruit, M. (2021), Laboratory forensics for open science readiness, Information Systems Frontiers.
- 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).
- 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.
- 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), BMJ-BRITISH MEDICAL JOURNAL 374.
- 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.
- 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 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 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).
- 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.
- 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.
- Shen, Z.R. & Spruit, M. (2021), Automatic extraction of adverse drug reactions from summary of product characteristics, APPLIED SCIENCES-BASEL 11(6).
- 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.
- Shen Z. & Spruit M.R. (2021), Automatic extraction of adverse drug reactions from summary of product characteristics, Applied Sciences 11(6): 2663.
- 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.
- Spruit M.R. & Vries N. de (2021), Self-service data science for adverse event prediction in electronic healthcare records. In: Visvizi A., Lytras M.D. & Aljohani N.R. (Eds.) Research and Innovation Forum 2020: Disruptive Technologies in Times of Change. Cham: Springer. 517--535.
- 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.
- Ozkan Yigit Bilge & Spruit Marco (2021), Cybersecurity Standardisation for SMEs.
- Menger V, Spruit M & Scheepers F (2021), Kennisontwikkeling in de klinische psychiatrie: leren van elektronische patiëntendossiers, Tijdschrift voor Psychiatrie 63(4): 294–300.
- 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.
- ICT adviezen geven, masterclasses verzorgen, toezicht houden (oa mijnIBDcoach)