Translational Data Science LAB d.d. 08 Jan 2026

About

The Translational Data Science & AI (TDS) Lab is headed by Prof.dr. Marco Spruit and is primarily located at the Health Campus The Hague within the LUMC department on the third floor around room 3.20a, but also has a BioScience Park Leiden office in the Gorlaeus building on the third floor in room BM3.01. We are an energetic and innovating team with an AI mindset that participates in many Dutch and European research projects and is actively involved in developing and teaching novel AI and machine learning course modules throughout the Leiden University Medical Center (LUMC).

Mission Statement

The TDS Lab's mission is to connect practical problems in healthcare practices to fundamental challenges in data science & AI and to subsequently address both simultaneously. This is our encompassing Translational Data Science & AI (TDS) research theme, which bridges the best of both worlds. Pasteur's Quadrant in Figure 1 below visualises our drive to achieve a better fundamental understanding of the world around us through data science & AI innovations by being societally inspired, demand-driven and solution-oriented.

Figure 1: Translational data science in Pasteur's Quadrant (on the left) combines basic data science understanding with applied data science use considerations.

Strategic objectives

Establish an authoritative and open national infrastructure for Dutch health research, education and care to accelerate innovation and to democratise data science technologies through especially Natural Language Processing and Generative AI technologies, by
1. [ELAN] Professionalising the ELAN infrastructure
2. [FML] Developing federated learning techniques for EHR data
3. [NLP] Showcasing the Welzijn.AI bot for the vulnerable elderly
4. [GenAI] Generating synthetic EHR data for digital twins
5. [MLOps] Deploying clinical decision support systems in healthcare practices
Modernise education in Dutch healthcare for students and professionals with artificial intelligence and machine learning-focused modules
Communicate our findings and working prototypes to a broader audience through embedded applications on the tdslab.nl website

Figure 2: Top-5 strategic objectives in the TDS Lab. [nl]

TDS Lab members

Types of involvement

Current TDS Team: 14
Former members: 17
Completed dissertations: 13

Student theses

MSc theses: 103
BSc theses: 64
Total: 167

Research Group

This section presents the Translational Data Science & AI Lab's current members, former members, a chronological list of completed Ph.D. dissertations, including some awards that we won over the years. Please visit the TDS Lab website for much more information on our transdisciplinairy research.

Current team [ ^ ]

2024-present: M. Vinkenoog (LUMC): Marieke is a postdoctoral researcher in the TDS Lab at the Health Campus The Hague with expertise in machine learning algorithms and MLOps. She received her PhD on Data-Driven Donation Strategies, Understanding and Predicting Blood Donor Deferral from LIACS at Leiden University and previously worked at NFI as a data scientist. Marieke is also active as a parttime lecturer in Statistics at LIACS.
2024-present: B. van Dijk (LUMC): Bram is a postdoctoral researcher at the intersection of NLP and ML, with a focus on large language models for open information extraction and synthetic data generation in healthcare applications within the INSAFEDARE project.
2023-present: A. Lefebvre (LUMC/LIACS): Armel is a senior researcher in the TDS Lab with expertise on research data management which is highly relevant for ELAN's further development at the Health Campus The Hague, and for LUMC's data strategy in general. Armel combines FAIR principles with Reproducible AI, and data management practices. He received his Phd on Research data management for open science and previously he worked at Erasmus University in Rotterdam and Tsinghua University in Beijing as a postdoc.
2023-present: M. Haas (LUMC): Marcel is a fulltime assistant professor in Health Data Science in the TDS Lab at the Health Campus The Hague. He obtained his PhD from Leiden University and started with a decade of data science experience in industry. Previous employers include DSW and ORTEC.
2018-2024: F. van Dijk: Privacy-by-Design (UU): Friso's research focuses on how organisations can demonstrate the responsible use of personal data in information systems through Privacy-by-Design to implement an effective data governance strategy (Funded by P&O Rijk). Supervisors: S. Brinkkemper (UU), M. Spruit, M. Brinkhuis (UU).
2020-2024: E. Rijcken: Dutch NLP in Mental Healthcare (TUE): Emil's research is embedded within the COVIDA programme on NLP for Dutch Mental Healthcare and explores how we can make text classification more interpretable and extend our topic modeling knowledge simultaneously, including by extracting semantic meaning from the dimensions withinin dense continuous word embeddings (Funded by Utrecht-Eindhoven Alliance Fund). Supervisors: U. Kaymak (TUE), F. Scheepers (UMCU), M. Spruit.
2021-2025: S. Alfaraj: Prediction of Type II Diabetes Progression (LUMC): Sukainah's research reuses routinely collected data from the GP office (ELAN-GP) to create clinical decision support to identify disease progression risk levels in Type Two Diabetes Mellitus (T2DM) patients. Supervisors: R. Groenwold (LUMC/EPI), M. Spruit (LUMC), D. Mook (LUMC/PHEG).
2022-2026: E. Roorda: Population Health Analytics (LUMC): Els' research focuses on maturity modelling for situational data infrastructure and scenario planning towards appropriate regional intelligence. Supervisors: M. Spruit (LUMC), M. Bruijnzeels (LUMC/PHEG).
2022-2026: S. Samir Khalil: Federated NLP in Mental Healthcare (LIACS): Samar's research focuses on how current NLP techniques can be applied and extended to support mental health detection and promotion, through collection and analysis of textual resources with multilingual, multimodal and federated techniques (Funded by AAST). Supervisors: M. Spruit (LIACS), N. Tawfik (AAST).
2023-2027: H. Muizelaar: Dutch NLP and ML for Risk Stratification (LUMC): Hielke's research in the HealthBox and ECOTIP projects is to develop NLP/ML-based Patient Segmentation and Risk Prediction models based on EHR, environmental, social and mobility data. Supervisors: M. Spruit (LUMC), M. Haas (LUMC).
2023-2027: J. Achterberg: Synthetic data generation and evaluation for HTAs (LUMC): Jim's research in the INSAFEDARE project revolves around the generation and evaluation of a benchmarking synthetic dataset amenable to regulatory processes, and analytical ML methods for the validation of digital health applications. Supervisors: M. Spruit (LUMC), M. Haas (LUMC), R. Vos (LUMC).
2025-2029: Y.M. Ng: Multimodal Language Markers for Perceived Quality-of-Life Assessment (MULAMAQ) (LIACS): Yee Man's research focuses on Quality-of-Life (QoL) assessments for non-invasive continuous monitoring of someone's current mental state of wellbeing using language markers detection in natural language utterances, primarily speech and text. Supervisors: M. Spruit (LIACS), G. van Oortmerssen (LIACS), B. van Dijk (LUMC).
2025-2029: K. Shahrasbi: Information Extraction for Cancer Data Use Harvesting (UNCAN-Connect) (LIACS): Kiana's research in the UNCAN-Connect project revolves around developing, validating and deploying an NLP&LLM-based Research Data Use Harvesting tool and contribute to an AI Observatory platform. Supervisors: M. Spruit (LIACS), A. Lefebvre (LIACS).
2025-2030: M. Pietersma: Dutch NLP for predicting treatment duration in mental healthcare (RU): Marijn's research at Stichting Pro Persona explores the use of natural language processing to predict treatment duration based on clinical notes in electronic health records, aiming to identify additional information and possibly biased language in clinical notes and assess their predictive value. Supervisors: B. Tiemens (RU), M. Spruit (LUMC/LIACS), N. Levshina (RU).

Completed dissertations [ ^ ]

3 Dec 2025: Z. Shen: Healthcare Information System Engineering: AI Technologies and Open Source Approaches: Ian's dissertation includes the Horizon2020 OPERAM activities related to the STRIP Assistant and furthers development of Healthcare Information Systems (HIS) in general, by employing various Machine Learning and Natural Language Processing techniques that address issues in healthcare with Open Source methodology. Funded by: OPERAM, PHC 17-2014: Comparing the effectiveness of existing healthcare interventions in the elderly, grant #634238. Supervisors: M. Spruit (LIACS), S. Brinkkemper (UU).
24 Jan 2025: M. van Haastrecht: Transdisciplinary Perspectives on Validity: bridging the gap between design and implementation for technology-enhanced learning systems: Max's dissertation forges a new transdisciplinary path towards holistic Technology-Enhanced-Learning validation that aids accelerated, but also responsible and trustworthy, impact. Supervisors: M. Spruit, M. Brinkhuis (UU). Funded by: GEIGER, Horizon 2020 - SU-DS03-2019-2020, grant #883588. Cum laude. hdl.handle.net/1887/4177362
19 January 2025: B. van Dijk: Theory of Mind in Language, Minds, and Machines: a Multidisciplinary Approach: Bram's dissertation intersects computational linguistics and NLP, investigating the relation between Theory of Mind (ToM) and natural language and cognition, as well as with Large Language Models as computational models of cognition. Supervisors: M. Spruit, M. van Duijn (ULEI/LIACS). Funded by: NWO/Veni. hdl.handle.net/1887/4176419
6 October 2023: A. Shojaifar: Volitional Cybersecurity: Alireza's work took place within the SMESEC and GEIGER EU projects. He co-designed and researched an automated cybersecurity assessment platform named Cybersecurity Coach (CySEC) which integrates personalised assessments, web usage behaviour, and advice adherence modelling, specifically for SMEs. Supervisors: S. Brinkkemper (UU), M. Spruit, S. Fricker (FHNW). Funded by: SMESEC and GEIGER, i.e. Horizon2020 projects #740787 and #883588. dspace.library.uu.nl/handle/1874/431418
5 June 2023: I. Sarhan: Open Information Extraction for Knowledge Representation: Ingy's research focuses on a systematic methodology that explores various Machine Learning (ML) and Natural Language Processing (NLP) algorithms to extract vital information from unstructured textual data to construct an effective representation of the mined information. Supervisors: S. Brinkkemper (UU), M. Spruit. Funded by: AAST and GEIGER, Horizon 2020 grant #883588. dspace.library.uu.nl/handle/1874/428396
11 July 2022: B. Yigit Ozkan: Cybersecurity Maturity Assessment and Standardisation: Bilge's research investigates how we can integrate cybersecurity maturity assessment and cybersecurity standardisation to provide tailored support for organisations in their cybersecurity improvement efforts. Her work was carried out in the context of the SMESEC project. Supervisors: S. Brinkkemper (UU), M. Spruit (LUMC/LIACS). Funded by: SMESEC, Horizon 2020 - H2020-DS-2016-2017, grant #740787. dspace.library.uu.nl/handle/1874/421285
15 March 2021: A. Levebfre: Research data management for open science: Armel's research investigates investigates research data management practices in laboratories in the context of open science. It discusses organizational and technological issues among stakeholders involved in research data management. Then, elaborates on the concept of reproducibility in experimental science. Finally, it illustrates several applications of FAIR technology and proposes a strategy for open science readiness. Supervisors: S. Brinkkemper (UU), B. Snel (UU), M. Spruit, B. van Breukelen (UU). Funded by: UU/ITS. dspace.library.uu.nl/handle/1874/401610
24 November 2020: N. Tawfik: Text Mining for Precision Medicine: Machine Learning and Information Extraction for Knowledge Discovery in the Health Domain: Noha's research investigates how biomedical natural language processing (BioNLP) can support and advance the Precision Medicine (PM) approach through collection and analysis of clinical and medical textual resources. The first two chapters contribute to the PM domain by obtaining valuable knowledge from unstructured resources. The other five chapters apply state-of-the-art NLP techniques to multiple data sources in order to better support the PM concept. This work focuses on combining traditional machine learning with deep learning techniques for the Natural Language Inference task, among others. Supervisors: S. Brinkkemper (UU), M. Spruit. Funded by: Arab Academy for Science, Technology & Maritime Transport (AAST). dspace.library.uu.nl/handle/1874/400797
14 October 2020: W. Omta: Knowledge Discovery in High Content Screening: Wienand's research investigates how multi-parametric data analysis can contribute to effective knowledge discovery in High Content Screening. His HC StratoMineR analytic system is designed and validated based on unsupervised data analysis methods. Gains and losses of using supervised data analytics methods and interactive visualizations are quantified. A standard data preprocessing pipeline is implemented in an R package, and a laboratory practice application of the systems to a chemical screen demonstrates this research's utility. Supervisors: S. Brinkkemper (UU), J. Klumperman (UMCU), M. Spruit. Funded by: UMCU/UU. dspace.library.uu.nl/handle/1874/399883
2 October 2019: V. Menger, Ph.D.: Knowledge Discovery in Clinical Psychiatry: Learning from Electronic Patient Records: Vincent's dissertation investigates how data from Electronic Health Records can provide relevant insights for psychiatric care. The first three chapters identify key technical, organizational and ethical challenges related to knowledge discovery in EHRs. The next three chapters focus on the knowledge discovery processing by employing natural language processing and cluster ensembling techniques to EHR data to obtain new insights with potential to improve care. Supervisors: S. Brinkkemper (UU), F. Scheepers (UMCU), M. Spruit. Funded by: UMCU. NB: Best departmental Dissertation award 2020. dspace.library.uu.nl/handle/1874/385129
20 March 2019: S. Syed, Ph.D.: Topic Discovery from Textual Data: Machine Learning and Natural Language Processing for Knowledge Discovery in the Fisheries Domain: Shaheen' s dissertation investigates how to optimally and efficiently apply and interpret probabilistic topic models to large collections of documents such as scientific publications. This work shows how different types of textual data, pre-processing steps, and hyper-parameter settings can affect the quality of the derived latent topics, using the Latent Dirichlet Allocation approach in particular. Supervisors: S. Brinkkemper (UU), M. Spruit. Funded by: Horizon2020 Marie Sklodowska-Curie MSC-ITN-ETN. dspace.library.uu.nl/handle/1874/374917
13 January 2016: M. Meulendijk, Ph.D.: Optimizing medication reviews through decision support: prescribing a better pill to swallow: Michiel' s dissertation investigates the conception and development of a decision support system to facilitate the conduct of structured medication reviews by physicians and pharmacists in primary care. The resulting STRIP Assistant system is validated in both a controlled environment and in daily practice, and is shown to significantly improve practitioners' effectiveness and efficiency in optimizing medication. This work deepens our understanding of barriers currently impeding the utility of decision support systems in primary care, most notably those of semantic interoperability and safe application of association rule mining. Supervisors: S. Brinkkemper (UU), M. Numans (LUMC), M. Spruit, P. Jansen (UMCU). Funded by: UMCU/UU. dspace.library.uu.nl/handle/1874/328063
13 November 2012: W. Bekkers, Ph.D.: Situational Process Improvement in Software Product Management: Willem's dissertation investigates how software product management (SPM) practices can be improved in a situational manner. The first part presents an overview of all practices that constitute SPM in the SPM competence model and the SPM maturity matrix. Then, the situational factors that affect SPM in the situational factor effects catalog are defined. The final part presents the situational assessment method (SAM) which software product management organizations can assess and improve their SPM in a situational manner. Supervisors: S. Brinkkemper (UU), M. Spruit. Funded by: Centric IT BV. dspace.library.uu.nl/handle/1874/256455

Former members [ ^ ]

Awards [ ^ ]

2024: Clinical Pearls Award.: S. Alfaraj et al., North American Primary Care Research Group (NAPCRG) conference, 23 November 2024, Toronto.
2023: Best Paper Award.: B. van Dijk et al., SIGNLL Conference on Computational Natural Language Learning (CoNNL), 6 December 2023, Singapore.
2020: Best Dissertation Award.: V. Menger, Dept. of Information and Computing Sciences, Utrecht University.
2005: Bursary award.: Association for Literary and Linguistic Computing (ACLC), Moncton (750 EUR).

Systems

Throughout the years, many functional AI systems have been developed in our lab. Some exemplary examples are shown below.

Welzijn.AI [ STRIPA | DEDUCE | SNPcurator ]

Welzijn.AI is a new digital solution for continuously monitoring the mental wellbeing in the vulnerable elderly. Currently in the form of a mobile app, its ultimate aim is to be embedded within a furry cat toy through a speech-only interface. The Maya AI virtual assistant is an implementation tailored for geriatric assessments support, in a collaboration with LUMC Geriatrics. [ Maya AI demo video in English ]

Additionally, we have developed various alternative prototypes to assess the different technological LLM approaches. For example, Roderick Leito investigated RASA's CALM approach for administering the EQ-5D: video.

Another extended English demo video of Wellbeing.AI with explanatory captions has also been prepared by Bram.
STRIPA [ DEDUCE | SNPcurator | Welzijn.AI ]

The STRIP Assistant (STRIPA) is a stand-alone, web-based software tool that was used to perform a pharmaceutical analysis, an important step of the STRIP process. Data on diagnoses and current drug use (collected via SHiM and the actual medical record), recent measurements and laboratory values (e.g. renal function, blood pressure) and possible adverse drug reactions, as listed in the patient’s medical record and according to patient information (SHiM) were entered in STRIPA. The assignment of drugs to diseases has been implemented through a drag and drop mechanism.
[ talk | paper1 | paper2 | video ]

PySTRIPA is the reimagined proof-of-concept version of STRIP Assistant version 5.0, built from scratch in python3 by a LIACS project team to perform the medication review process in a simplified and much more futureproof fashion.
[ demo video ]
DEDUCE [ Welzijn.AI | STRIPA | SNPCurator ]

DEDUCE is a pattern matching method for automatic de-identification of Dutch medical text. De-identification is done using fuzzy string matching and lookup lists. Validation shows generally good results, with a total micro-averaged recall of 0.916. For person names, a recall of 0.961 was achieved, missing no patient names. In followup research we compare DEDUCE with other de-identification methods such as Deidentify.
NB: DEDUCE is actively being used in various hosptitals in the Netherlands to deidentify clinical notes before access is granted as a mandatory part of the ethical approval procedure.
[ paper | benchmark ]
SNPcurator [ Welzijn.AI | STRIPA | DEDUCE ]

SNPcurator is a system for information extraction specifically in the genome wide and candidate genes studies, constructed out of different natural language processing (NLP) modules to aid scientists in their search for relevant disease-associated SNPs through an intuitive web interface. It incorporates both syntactic and semantic methods to extract relevant information from PubMed abstracts such as cohort size and ethnicity, SNP ids and the reported results.
[ paper ] [ Live Demo ]

Below are the 46 projects -- representing a grand total of 4.5M+ EUR in allocated research resources -- that have driven our research efforts over the years. Next to the 26 grants that were awarded, the TDS Lab has also been involved in 20 research collaborations on a payment-in-kind basis.

Projects overview

Statistics

Grants: 26
Collaborations: 20
Total: 46

Pipeline

Under review: 10
In preparation: 1
Total: 11

Research Projects

Funded research | Research collaborations

2026-2029: SharedGovInfra, 240K EUR (LUMC).: Improving availability, access, and usage of socio-medical data through unified governance - for researchers, health professionals, policymakers, and citizens. NWO/Open Science Infrastructure Call, 2024 1st round: 0.7 fte postdoc for 36 months. Remark: grant total: 1.4M EUR. url
2025-2029: UNCAN-Connect, 475K EUR (LIACS).: Decentralized Collaborative Network for Advancing Cancer Research and Innovation. HORIZON-RIA: HORIZON-MISS-2024-CANCER-01-01 (Research and Innovation actions supporting the implementation of the Mission on Cancer). Remarks: grant total: 30M EUR, 53 partners from 19 European and associated countries, comprising 6 SMEs, 3 LEs, 42 RTOs, 3 affiliated partners, and 1 NGO. Researcher(s): 1 PhD student, 1 postdoc. cordis.europa.eu
2025-2029: MULAMAQ, 300K EUR (LIACS).: Multimodal Language Markers for Perceived Quality-of-Life Assessment. InSPIRe 2025 (LIACS Initiative for Strategic PhD/Postdoc and Innovative Research). Remark: grant total: 300K EUR. Researcher(s): Ng,Y.M. easychair.org/inspire2025
2024-2026: ECOTIP, EUR 130K (LUMC).: Identifying tipping points of the effects of living environments on ecosyndemics of lifestyle-related illnesses by ML/NLP modelling of a patient segmentation model based on EHR and environmental data. Financer(s): NWO New Science Agenda (NWA-ORC). Applicant(s): Kiefte,J., Spruit,M., Vos,R., et al. Remark: NWO dossier NWA.1518.22.151; grant total: 4.4M EUR. Researcher(s): Muizelaar,H. www.nwo.nl/en/projects/nwa151822151
2024-2026: Phaeton, EUR 150K (LUMC) + EUR 50K (LIACS).: Pandemic preparedness. Portable platform as a service for crowdsourced and privacy respecting data analysis and modeling. Financer: ZonMW Modelleren voor Pandemische Paraatheid: een oproep tot innovatie en kennisontwikkeling SA 2023. Applicant(s): Bouwman,J., Haas,M., Spruit,M.. Remark: ZonMW dossier #10710062310030, grant total: 500K EUR. Researcher(s): Vinkenoog,M. universiteitleiden.nl/.../liacs-phaeton
2023-2026: INSAFEDARE, EUR 571K (LUMC).: Innovative applications of assessment and assurance of data and synthetic data for regulatory decision support. Generation and evaluation of a benchmarking synthetic dataset amenable to the regulatory process, analytical methods for validation of digital health applications, and components for data integration pipelines. Financer(s): Horizon Europe: HORIZON-HLTH-2022-TOOL-11-02: Tools and technologies for a healthy society. Applicant(s): Despotou,G. et al. Remark: HEU project #101095661; grant total: 4.8M EUR. Researcher(s): Achterberg,J. & Dijk,B. van 10.3030/101095661
2023-2025: HealthBox, EUR 66,000 (LUMC).: A personalized, home-based eHealth intervention to treat metabolic syndrome and prevent its complications by ML/NLP modelling of a patient segmentation model based on EHR and environmental data. Applicant(s): Chavannes,N., Atsma,D., Pijl,H., Vos,R., et al. Remark: grant total: 2.5M EUR. Researcher(s): Muizelaar,H. nwo.nl/en/projects/kich1gz0321007
2025: GenAI4EU, 9K EUR (LUMC).: Grant proposal support for LUMC internal call 2025: Stimulating coordination of Horizon Europe - Health 2025 projects.
2021-2025: VIPP, EUR 60K (LUMC).: Virtual Patients and Population Dataset. Develop a synthetic ELAN dataset to improve teaching data science. Financer(s): LUMC Interprofessional Education (IPE) programme. Applicant(s): Spruit,M., & Szuhai,K. Remark: Project Raamplan Implementatie Artsopleiding (PRIMA) 2020 working group deliverable wrt Theme 5 on Big Data and AI. Researcher(s): Faiq,A. healthcampusdenhaag.nl/nl/project/ virtuele-patient-en-populatie-vipp-dataset/
2024-2025: EuroQoL-LLM, 1325 EUR (LUMC).: Applying Large Language Models to Identify EQ-5D Bolt-ons Based on Patient Text Data. Financer: EuroQol Group Seed grant: 1792-SG. Applicant: van den Akker-van Marle,E., Spruit,M., et al. Remark: Grant total: 42K EUR. Researcher(s): Heijdra Suasnabar,J. et al. euroqol.org/research-at-euroqol/ our-research-portfolio/funded-projects/
2023-2024: SENSYN, EUR 5K (LUMC).: Making sensitive data reusable through synthetic data generation, and implementation of FAIR principles in highly sensitive data areas. Financer(s): NWO Open Science Fund. Applicant(s): Liem,M., Spruit,M., et al. Remarks: NWO project OSF23.1.006; grant total: 50K EUR. Researcher(s): Haas,M. & Achterberg,J. www.nwo.nl/en/projects/osf231006
2020-2023: GEIGER, EUR 300K (ULEI, UU).: Geiger Cybersecurity Counter. A metric for assessing, monitoring, and forecasting risks and reducing these risks by improving SME security with well-curated SMESEC tools and an education program targeting practitioners-in-practice, facilitated by a cybersecurity knowledge graph. Financer(s): Horizon2020: SU-DS03-2019-2020: Digital Security and privacy for citizens and SMEs. Applicant(s): Fricker,S. et al. Remark: EU project 883588; grant total: 4.8M EUR. Researcher(s): Haastrecht,M. van, Sarhan,I., Shojaifar,A. 10.3030/883588
2020-2022: COVIDA, EUR 230K (UU).: Computing Visits Data for Dutch Natural Language Processing in Mental Healthcare. Financer(s): Utrecht-Eindhoven Alliance Fund. Applicant(s): Spruit,M., Scheepers,F., & Kaymak,U. et al. Remark: Grant total: 492K EUR. Researcher(s): Mosteiro,P., Rijcken,E. www.tue.nl/en/our-university/ about-the-university/university-alliances-networks/ challenging-future-generations/utrecht-eindhoven-alliance
2017-2020: OPTICA, EUR 22K (UU).: Optimising PharmacoTherapy In the multimorbid elderly in Primary CAre: a cluster randomised controlled trial. RCT to implement STRIPA 2.0 in Swiss daily GP practices. Financer(s): Research Plan NRP 74 Smarter Health Care Division IV, National Research Programmes (NRP), Switzerland. Applicant(s): Rodondi,N., Streit,S., Schwenkglenk,M., Trelle,S., Spruit,M., & Schilling,G.. Remark: Swiss National Science Foundation (SNF) project; grant total: 475K EUR. Researcher(s): Elloumi,L., Brinkhuis,E.
2017-2020: SMESEC: EUR 278K (UU).: Protecting Small and Medium-sized Enterprises digital technology through an innovative cyber-SECurity framework. Personalised maturity modelling for incremental organisational improvement in cybersecurity. Financer(s): H2020-DS-2016-2017: Secure societies - Protecting freedom and security of Europe and its citizens. Applicant(s): Diaz,R. et al. Remark: EU project 740787; grant total: 5.6M EUR. Researcher(s): Yigit Ozkan,B., Shojaifar,A. 10.3030/740787
2015-2020: OPERAM, EUR 250K (UU).: OPtimising thERapy to prevent Avoidable hospital admissions in the Multimorbid elderly. Software Tool for Optimising Medication to run a RCT to evaluate STRIPA 2.0. Financer(s): PHC 17-2014: Comparing the effectiveness of existing healthcare interventions in the elderly. Applicant(s): Rodondi,N. et al. Remark: EU project 634238; grant total: 6.6M EUR. Researcher(s): Meulendijk,M., Shen,Z. 10.3030/634238
2015-2020: FeDerATE, EUR 200K (UU).: Fair Data and context ArchiTEcture. Data stewardship in research reproducibility and data analytics in omics domains. Financer(s): Utrecht University IT Services (UU-ITS), Utrecht Bioinformatics Centre (UBC). Researcher(s): Lefebvre,A.
2019-2020: INTERESTM, EUR 5K (UU).: A text mining approach to interest development. ADS Seed project on text mining of interest development descriptions of adolescents: Financer(s): UU focus area Applied Data Science. Applicant(s): Akkerman,S., Spruit,M. Researcher(s): Meer,T. van der.
2018-2019: STRIMP, EUR 112K (UU).: Implementatie van de STRIP Assistent ter verbetering van de STRIP medicatiebeoordeling. Integrate the STRIP Assistant within Dutch daily primary care. Financer(s): ZonMW/Goed Gebruik Geneesmiddelen - Stimulering Toepassing In de Praktijk (GGG - STIP Ronde 3). Applicant(s): Spruit,M., Wit,N. de, et al. Remark: grant total: 350K EUR. Researcher(s): Elloumi,L., Brinkhuis,E. projecten.zonmw.nl/nl/project/ strimp-implementatie-van-de-strip-assistent -ter-verbetering-van-de-strip
2015-2019: PRAISE, EUR 200K (UU).: Psychiatry Research Analytics InfraStructurE. Big Data Psychiatry Breakthrough programme towards predicting psychiatric conditions such as schizofrenia and autism through patient fingerprinting techniques enabled by an interorganisational information integration architecture. Financer(s): UMC Utrecht/Psychiatry. Researcher(s): Menger,V.
2011-2019: CESCA, EUR 170K (UU): CEll SCreening Architecture & Analytics. Online platform for high content screening and cloud-based data analysis services for drug target discovery and validation, leads discovery and optimization, and the assessment of cellular toxicity. Financer(s): UMC Utrecht, UU-ICS. Researcher(s): Omta,W.
2014-2018: TAF21, EUR 200K (UU).: Text analytics for social science aspects of fisheries for the 21st century. Early Stage Researcher (ESR) 7 at Manchester University. Financer(s): MSCA-ITN-2014-ETN: Marie Sklodowska-Curie Innovative Training Networks (ITN-ETN). Applicant(s): Borit, M. et al. Remark: EU project 642080; grant total: 2.7M EUR. Researcher(s): Syed,S. 10.3030/642080
2010-2015: STRIPA, EUR 200K (UU).: STRIP Assistant. Systematic Tool to Reduce Inappropriate Prescribing (STRIP) Assistant is an online decision support platform integrated in GPISs to support general practitioners with optimising polypharmacy in elderly patients through periodical prescription reviews. Financer(s): Expertise centre Pharmacotherapy in Elderly (EPHOR), UU/ICS. Researcher(s):Meulendijk,M.
2010-2011: POMP, EUR 50K (UU).: Polypharmacy Optimisation Method Platform. Feasibility study on a knowledge platform to assist physicians, especially general practitioners, in optimising polypharmacy in elderly patients. Financer(s): Agentschap NL, Small Business Innovation Research programme (SBIR). Applicant(s): Spruit,M.
2015: CURP 1.1, EUR 5K (UU).: CURsus Planning app, Revision 1. Financer(s): Utrechts Stimuleringsfonds Onderwijs 2015. Applicant(s): Spruit,M. Researcher(s): Joosten,P. 10.1108/978-1-78973-627-420191010
2014: CURP, EUR 15K (UU).: Curriculum Planning app. Development of an interactive serious game to support collaborative curriculum course design for education management, staff and students. Financer(s): Utrechts Stimuleringsfonds Onderwijs. Applicant(s): Spruit,M. Researcher(s): Dompseler,H. van.

Research collaborations | Funded research

2025-2026: When Stories Meet Algorithms (LIACS): 15K EUR.: Leiden University Global Fund (LUGF) Seed Fund. When Stories Meet Algorithms: Human–AI Collaboration for Culturally Embedded Qualitative Research. Applicants: N. Chavannes (LUMC), M. Spruit (LIACS), Y. Wu (Zhejiang University, Hangzhou). /leiden-university-global-fund/
2025-2026: ElanGGZ (LUMC).: Transdiagnostische versterking van de ELAN kennisinfrastructuur. ZonMW, Nationaal Plan Hoofdzaken: kennis en data beter delen SA 2024. PI: Giltay,Erik. Remark: Total grant total: 648K EUR. Deadline: 25/02/2025. url
2025: COACH (LIACS).: Conversational AI for Cancer reHabilitation. Develops a GenAI powered companion app for young people with cancer in collaboration with HealthyChronos. Financer: Kansen voor West fonds. Researcher(s): Y.M. Ng. /projecten-kansen-voor-west-iii/coach/
2025: Understanding Homicide in Indonesia (LIACS): Leiden University Global Fund (LUGF) Seed Fund. Harnessing Traditional and New Media Data for Insight. Collaboration with Universitas Gadjah Mada in Yogyakarta, Indonesia. Applicants: O. Bogolyubova, M. Liem (ISGA), M. Spruit. /leiden-university-global-fund/
2022-2026: PreProMMF (ULEI): Natural Language Processing in Mental Health: Detection, Prediction and Promotion with Multilingual, Multimodal and Federated Techniques. Sponsor: Arab Academy of Science, Technology & Maritime Transport (AAST). Financed as a 60% lecturer - 40% researcher contract. Researcher(s): Khalil,S.
2021-2025: Data2Bedside (LUMC): Reusing routinely collected data from regional GP offices in ELAN to create a clinical decision support tool to identify disease progression risk levels in Type Two Diabetes Mellitus (T2DM) patients. Sponsor: Kingdom of Saudi Arabia scholarship. Researcher(s): Alfaraj,S.
2021-2026: PHA (LUMC): Population Health Analytics. Maturity modelling for situational data infrastructure and scenario planning towards appropriate regional intelligence. Sponsor: Q-Consult Zorg. Researcher(s): Roorda,E.
2018-2024: PbD (UU): Privacy-by-Design. How organisations can demonstrate responsible data use in information systems through Privacy-by-Design. Sponsor: P&O Rijk. Researcher(s): Dijk,F. van
2020-2024: ATS (ULEI): A Telling Story. Mindreading with NLP. Sponsor: NWO; Applicant(s): Duijn, M. van. Researcher(s): Dijk,B. van
2022-2024: EDAsynth (ULEI): Emergency Department Admissions Forecasting with Generative AI. Sponsor: Universidad de Alcalá. Researcher(s): Álvarez-Chavez,H.
2017-2023: SpeechAS (UU): Real-time Speech and Text Analytic Systems for HR dialogue support. Sponsor: P&O Rijk. Researcher(s): Toledo,C. van
2018-2022: DEQUES (UU): Deep learning for Query based Summarisation: Deep neural networks for exploratory summarisation. Sponsor: Arab Academy of Science, Technology & Maritime Transport (AAST). Financed as a 60% lecturer - 40% researcher contract. Researcher(s): Sarhan.I.
2016-2021: BeHapp (UU): Psychiatric Ratings using Intermediate Stratified Markers: the BeHapp mobile health analytics app to classify neuropsychiatric diseases to accelerate the discovery and development of better treatments for patients. Sponsor: Innovative Medicine Initiative (IMI) project; Applicant(s): Kas,M. Researcher(s): Jagesar,R.
2016-2020: TAILS (UU): Text Analytics Innovations in Life Sciences: Natural Language Processing based innovations from both machine learning and computational linguistics perspectives to better understand their specific added values throughout the broad application domain of personalised medicine. Sponsor: Arab Academy of Science, Technology & Maritime Transport (AAST). Financed as a 60% lecturer - 40% researcher contract. Researcher(s): Tawfik,N.
2017-2019: PsyADS (UU): Applied data science in de psychiatrische praktijk. Strategic collaboration within the Compute Visits Data (COVIDA) consortium in this Implementation project. Financer(s): Kwaliteit van Zorg: Actieonderzoek Innovatieve Zorg. Applicant(s): Scheepers et al. Remark: grant total: 300K EUR.
2010-2018: DataSpace (UU): Data Space architecture in the judicial domain. External PhD research on a Data Space architecture at the crossroads of Data Warehousing, Privacy Preservation, Semantic Web, and Data Quality. Sponsor: Research and Documentation Centre (WODC), Ministry of Security and Justice. Financed as a 80% business - 20% research contract. Researcher(s): Dijk, J. van
2015-2016: BeHapp (UU): Using the smartphone to longitudinally monitor adolescent social behavior in real life. Financer(s): Utrecht University Strategic Theme Dynamics of Youth (DoY) 2015. Applicant(s): Kas,M. Remark: grant total: EUR 100K.
2013-2015: UBIL (UU): Unified business intelligence language for vendor and technology independent BI-chain modeling through a data lineage framework. Sponsor: CSB-System Benelux BV, Kadenza BV. Financed as a 80% business - 20% research contract. Researcher(s): Otten,S.
2013-2014: SNiF, EUR 50K (UU).: Social Network Forensics. Inter-ethnic relations and ethnic identity of Dutch adolescents in offline and online networks based on the Linguistic Engineering for Business Intelligence (LEBI) framework. Financer(s): UU DoY 2014. Applicant(s): Corten,R. et al. Seed fund for UU strategic theme Dynamics of Youth.
2009-2012: SAM (UU): How software product management (SPM) practices can be improved in a situational manner. Sponsor: Software quality group, Centric IT Solutions BV. Financed as a 60% business - 40% research contract. Researcher(s): Bekkers,W.

Below are the 269 publications which document our TDS research efforts throughout the years. Appropriately reflecting our dissemination strategy, we currently have published 123 journal articles, 94 conferences proceedings, 2 books, 27 book chapters, and 23 non-peer reviewed publications such as pre-prints, magazine articles, and technical reports.

Publications overview

Publications

Journal articles [ ^ ]

Under review

Muizelaar,H., Haas,M., van Aken,M., Vos,R., & Spruit,M. (under review). Quantifying the Predictive Power of Social Determinants of Health in Cardiometabolic Disease Progression Using XGBoost: A Retrospective Cohort Study. preprint
Roorda,E., Bruijnzeels,M., Struijs,J., & Spruit,M. (under review). Data-Driven Health Care Commissioning to Reduce Health Disparities Among Individuals with Diabetes Type 2 in the Netherlands. A Focus Group Analysis. preprint

2025 [ 2024 | 2023 | 2022 | 2021 | 2020-2018 | 2017-2014 | 2013-2006 | ^ ]

Alfaraj,S., Vos,R., Spruit,M., Groenwold,R., & Mook-Kanamori,D. (2025). Towards Personalized Diabetes Management: Identifying Stability for Efficient Care Using Primary Care Data. Journal of Medicine, Surgery, and Public Health, 100218. 10.1016/j.glmedi.2025.100218

Alfaraj,S., Vos,R., Spruit,M., Groenwold,R., & Mook-Kanamori,D. (2025). Insulin Initiation in Type 2 Diabetes: Unraveling the Sociodemographic and Biological Dynamics using Routinely Collected Primary Care Data. British Journal of General Practice, BJGP.2024.0693. 10.3399/bjgp.2024.0693

Van Dijk,B., Lefebvre,A., & Spruit,M. (2025). Welzijn.AI: A Conversational AI System for Monitoring Mental Well-being and a Use Case for Responsible AI Development. Maturitas, 108616. In: The future of healthy ageing. 10.1016/j.maturitas.2025.108616

Achterberg,J., Haas,M., Van Dijk,B., & Spruit,M. (2025). Fidelity-agnostic synthetic data generation improves utility while retaining privacy. Patterns, 101287. 10.1016/j.patter.2025.101287

Mosteiro,P., Wang,R., Scheepers,F;, & Spruit,M. (2025). De-identification Methodologies in Dutch Medical Texts: A Replication Study of Deduce and Deidentify. Electronics, 14(8), 1636. Digital Security and Privacy Protection: Trends and Applications, 2nd Edition. 10.3390/electronics14081636

Rijcken,E., Zervanou,K., Mosteiro,P., Scheepers,F., Spruit,M., & Kaymak,U. (2025). Machine Learning vs. Rule-Based Methods for Document Classification of Electronic Health Records within Mental Health Care - A Systematic Literature Review. Natural Language Processing Journal, 10, 100129. 10.1016/j.nlp.2025.100129

Sibbald, L., van den Heuvel, M.I., Haas, M.R., van Lissa, C.J., van Bakel, H.J.A., Jongerling, J., Hulsbosch, L.P., Muskens, L., Boekhorst, M.G.B.M., & Schwabe, I. (2025). Identifying prenatal risk factors of postpartum depression with machine learning. Scientific Reports, 15(1), 34610. 10.1038/s41598-025-18204-6

2024 [ 2025 | 2023 | 2022 | 2021 | 2020-2018 | 2017-2014 | 2013-2006 | ^ ]

Alfaraj,S., Kist,J., Groenwold,R., Spruit,M., Mook-Kanamori,D., & Vos,R. (2024). External validation of SCORE2-Diabetes in the Netherlands across various Socioeconomic levels in native-Dutch and non-Dutch populations. European Journal of Preventive Cardiology, zwae354. 10.1093/eurjpc/zwae354
Roorda,E., Bruijnzeels,M., Struijs,J., & Spruit,M. (2024). Business Intelligence Systems for Population Health Management: A Scoping Review. JAMIA Open, 7(4), ooae122. 10.1093/jamiaopen/ooae122
Drougkas,G., Bakker,E., & Spruit,M. (2024). Multimodal Machine Learning for Language and Speech Markers Identification in Mental Health. BMC Medical Informatics and Decision Making, 24, 354. 10.1186/s12911-024-02772-0
Álvarez-Chaves,H., Spruit,M., & R-Moreno,M. (2024). Improving ED admissions forecasting by using generative AI: An approach based on DGAN. Computer Methods and Programs in Biomedicine, 256, 108363. 10.1016/j.cmpb.2024.108363
Achterberg,J., Haas,M., & Spruit,M. (2024). On the evaluation of synthetic longitudinal electronic health records. BMC Medical Research Methodology, 24, 181. 10.1186/s12874-024-02304-4
Haastrecht,M. van, Haas,M., Brinkhuis,M., & Spruit,M. (2024). Understanding Validity Criteria in Technology-Enhanced Learning: A Systematic Literature Review. Computers & Education, 220, 105128. 10.1016/j.compedu.2024.105128
Rijcken,E., Zervanou,K., Mosteiro,P., Scheepers,F., Spruit,M., & Kaymak,U. (2024). Topic Specificity: a Descriptive Metric for Algorithm Selection and Finding the Right Number of Topics. Natural Language Processing Journal, 8, 100082. 10.1016/j.nlp.2024.100082
Muizelaar,H., Haas,M., van Dortmont,K., van der Putten,P., & Spruit,M. (2024). Extracting Patient Lifestyle Characteristics from Dutch Clinical Text with BERT Models. BMC Medical Informatics and Decision Making, 24, 151. 10.1186/s12911-024-02557-5
Khalil, S., Tawfik,N., & Spruit,M. (2024). Federated learning for privacy-preserving depression detection with multilingual language models in social media posts. Patterns, 5, 100990. 10.1016/j.patter.2024.100990
Khalil, S., Tawfik,N., & Spruit,M. (2024). Exploring the Potential of Federated Learning in Mental Health Research: A Systematic Literature Review. Applied Intelligence, 54, 1619-1636. 10.1007/s10489-023-05095-1
Jungo,K., Salari,P., Meier,R., Bagattini,M., Spruit,M., Rodondi,N., Streit,S., & Schwenkglenks,M. (2024). Cost-effectiveness of a medication review intervention for general practitioners and their multimorbid older patients with polypharmacy: Analysis of data from the OPTICA trial. Socio-Economic Planning Sciences, 92, 101837. 10.1016/j.seps.2024.101837
Jungo,K., Deml,M., Schalbetter,F., Moor,J., Feller,M., Lüthold,R., Huibers,J., Sallevelt,B., Meulendijk,M., Spruit,M., Schwenkglenks,M., Rodondi,N., & Streit,S. (2024). A mixed methods analysis of the medication review intervention centered around the use of the Systematic Tool to Reduce Inappropriate Prescribing Assistant (STRIPA) in Swiss primary care practices. BMC Health Services Research, 24, article number 350. 10.1186/s12913-024-10773-y
Kist, J.M., Vos, H.M.M., Vos, R.C., Mairuhu, A.T.A., Struijs, J.N., Vermeiren, R.R.J.M., van Peet, P.G., van Os, H.J.A., Ardesch, F.H., Beishuizen, E.D., Sijpkens, Y.W.J., de Waal, M.W.M., Haas, M.R., Groenwold, R.H.H., Numans, M.E., & Mook-Kanamori, D. (2024). Data Resource Profile: Extramural Leiden University Medical Center Academic Network (ELAN). International Journal of Epidemiology, 53(4). 10.1093/ije/dyae099
Haas, M.R. & Sibbald, L. (2024). Measuring data drift with the unstable population indicator. Data Science, 2024(1). 10.3233/DS-240059

2023 [ 2025 | 2024 | 2022 | 2021 | 2020-2018 | 2017-2014 | 2013-2006 | ^ ]

Jungo,K., 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). Optimising prescribing in older adults with multimorbidity and polypharmacy in primary care (OPTICA): cluster randomised clinical trial. BMJ, 381, e074054. 10.1136/bmj-2022-074054
Lefebvre,A., & Spruit,M. (2023). Laboratory forensics for open science readiness: an investigative approach to research data management. Information Systems Frontiers, 25, 381-399. 10.1007/s10796-021-10165-1
Ferguson,R., Khosravi,H., Kovanovic,V., Viberg,O., Aggarwal,A., Brinkhuis,M., Shum,S., Chen,L., Drachsler,H., Guerrero,V., Hanses,M., Hayward,C., Hicks,B., Jivet,I., Kitto,K., Kizilcec,R., Lodge,J., Manly,C., Matz,R., Meaney,M., Ochoa,X., Schuetze,B., Spruit,M., van Haastrecht,H., van Leeuwen,A., van Rijn,L., Tsai,Y., Weidlich,J., Williamson,K., & Yan,V. (2023). Aligning the Goals of Learning Analytics with its Research Scholarship: An Open Peer Commentary Approach. Journal of Learning Analytics, 10(2), 10-50. 10.18608/jla.2023.8197
Haastrecht,M., Brinkhuis,M., Wools,S., & Spruit,M. (2023). VAST: a practical validation framework for e-assessment solutions. Information Systems and e-Business Management, 21, 603-627. 10.1007/s10257-023-00641-3
Yigit Ozkan,B., & Spruit,M. (2023). Adaptable Security Maturity Assessment and Standardization for Digital SMEs. Journal of Computer Information Systems, 63(4), 965-987. 10.1080/08874417.2022.2119442
Ardesch,F., Meulendijk,M., Kist,J., Vos,R., Vos,H., Kiefte-de Jong,J., Spruit,M., Bruijnzeels,M., Bussemaker,J., Numans,M., & Struijs,J. (2023). A data-driven population health management approach: The extramural LUMC academic network data infrastructure. Health Policy, 132, 104769. 10.1016/j.healthpol.2023.104769
van Toledo, C., Schraagen, M., van Dijk, F., Brinkhuis, M., & Spruit, M. (2023). Readability Metrics for Machine Translation in Dutch: Google vs. Azure & IBM. Applied Sciences, 13(7), 4444. 10.3390/app13074444
van Dijk,F., Gadellaa,J., Spruit,M., van Toledo,C., Brinkkemper,S., & Brinkhuis,M. (2023). Uncovering the Structures of Privacy Research using Bibliometric Network Analysis and Topic Modelling. Organizational Cybersecurity Journal: Practice, Process and People, 3(2), 81-99. 10.1108/ocj-11-2021-0034
Haas, M.R., Caprani, C., & van Beurden, B. (2023). Bayesian Generative Modelling of Student Results in Course Networks. Journal of Learning Analytics, 10(3). 10.18608/jla.2023.7957

2022 [ 2025 | 2024 | 2023 | 2021 | 2020-2018 | 2017-2014 | 2013-2006 | ^ ]

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. 10.1016/j.eswa.2022.116720
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. 10.3390/app12042179
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. http://dx.doi.org/10.2196/31063
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. 10.3390/info13050237
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. 10.3390/info13110513
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, Section Data Mining and Management, 846930. 10.3389/fdata.2022.846930

2021 [ 2025 | 2024 | 2023 | 2022 | 2020-2018 | 2017-2014 | 2013-2006 | ^ ]

Blum,M., Sallevelt,B., Spinewine,A., O'Mahony,D., Moutzouri,E., Feller,M., Baumgartner,C., Roumet,M., Jungo,K., Schwab,N., Bretagne,L., Beglinger,S., Aubert,C., Wilting,I., Thevelin,S., Murphy,K., Huibers,C., Drenth-van Maanen,C., Boland,B., Crowley,E., Eichenberger,A., Meulendijk,M., Jennings,E., Adam,L., Roos,M., Gleeson,L., Shen,Z., Marien,S., Meinders,A., Baretella,O., Netzer,S., Montmollin,M., Fournier,A., Mouzon,A., O'Mahony,C., Aujesky,D., Mavridis,D., Byrne,S., Jansen,P., 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): Cluster Randomised Controlled Trial. BMJ, 374(n1585). 10.1136/bmj.n1585
Sarhan,I., & Spruit,M. (2021). Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph. Knowledge-Based Systems, 233(107524). 10.1016/j.knosys.2021.107524
Sallevelt,B., Huibers,C., Heij,J., Egberts,T., Puijenbroek,E. van, Shen,Z., Spruit,M., Jungo,K., Rodondi,N., Dalleur,O., Spinewine,A. Jennings,E., O'Mahony,D., Wilting,I., & Knol,W. (2021). Frequency and Acceptance of Clinical Decision Support System-Generated STOPP/START Signals for Hospitalised Older Patients with Polypharmacy and Multimorbidity. Drugs & Aging, 39, 59-73. 10.1007/s40266-021-00904-z
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. 10.3390/app11062663
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. 10.3390/app11156909
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). 10.1186/s12875-021-01488-8
Haastrecht,M. van, Gulpur,G., Tzismadia,G., Kab,R., Priboi,C., David,D., Racataian,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. 10.3390/electronics10232913
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. 10.2991/jaims.d.210225.001
Felix,S., Bagheri,A., Ramjankhan,F., Spruit,M., Oberski,D., Jonge,N. de, Laake, L. van, Suyker,W., & Asselbergs,F. (2021). A Data Mining-based Cross-Industry Process for Predicting Major Bleeding in Mechanical Circulatory Support. European Heart Journal - Digital Health, 2(4). 10.1093/ehjdh/ztab082
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. 10.3389/frma.2021.685591
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. 10.3390/jcp1040034
Yigit Ozkan,B., van Lingen,S., & Spruit,M. (2021). The Cybersecurity Focus Area Maturity (CYSFAM) Model. Journal of Cybersecurity and Privacy, 1, 119-140. 10.3390/jcp1010007
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. 10.3390/fi13100243

2020-2018 [ 2025 | 2024 | 2023 | 2022 | 2021 | 2017-2014 | 2013-2006 | ^ ]

Tawfik,N., & Spruit,M. (2020). Evaluating Sentence Representations for Biomedical Text: Methods and Experimental Results. Journal of Biomedical Informatics, 104(April), 103396. 10.1016/j.jbi.2020.103396
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. 10.3390/su12093631
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. 10.1177/2472555220919345
Ooms,R., & Spruit,M. (2020). Self-Service Data Science in Healthcare with Automated Machine Learning. Applied Sciences, 10(9), Medical Artificial Intelligence, 2992. 10.3390/app10092992
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. 10.3390/app10175758
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. 10.3390/app10082845
Crowley,E., Sallevelt,B., Huibers,C., Murphy,K., Spruit,M., Shen,Z., Boland,B., Spinewine,A., Dalleur,O., Moutzouri,E., Lowe,A., Feller,M., Schwab,N., Adam,L., Wilting,I., Knol,W., Rodondi,N., Byrne,S., & O'Mahony,D. (2020). Intervention protocol: OPtimising thERapy to prevent avoidable hospital Admission in the Multi-morbid elderly (OPERAM): a structured medication review with support of a computerised decision support system. BMC Health Services Research, 20(220). 10.1186/s12913-020-5056-3
Lefebvre,A., Bakhtiari,B., & Spruit,M. (2020). Exploring Research Data Management Planning Challenges in Practice. IT - Information Technology, 62(1), 29-37. 10.1515/itit-2019-0029
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. 10.1089/sysm.2019.0007
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. 10.4018/IJBIR.20200701.oa1
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). 10.5121/ijnlc.2020.9602
Syed,S., Aodha,L., Scougal,C., & Spruit,M. (2019). Mapping the global network of fisheries science collaboration. Fish and Fisheries, 20(5), 830-856. 10.1111/faf.12379
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(1). 10.1108/JIC-05-2019-0128
Jungo,K., Rozsnyai,Z., Mantelli,S., Floriani,C., Lowe,A., Lindemann,F., Schwab,N., Meier,R. Elloumi,L., Huibers,C., Sallevelt,B., Meulendijk,M., Reeve,E., Feller,M., Schneider,C., Bhend,H., Burki,P., Trelle,S., Spruit,M., Schwenkglenks,M., Rodondi,N., & Streit,S. (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. 10.1136/bmjopen-2019-031080
Adam,L., Moutzouri,E., Baumgartner,C., Lowe,A., Feller,M., M'Rabet-Bensalah,K., Schwab,N., Hossmann,S., Schneider,C., Jegerlehner,S., Floriani,C., Limacher,A., Jungo,K., Huibers,C., Streit,S., Schwenkglenks,M., Spruit,M., Van Dorland,A., Donzé1,J., Kearney,P., Jüni,P., Aujesky,D., Jansen,P., Boland,B., Dalleur,O., Byrne,S., Knol,W., Spinewine1,A., O'Mahony,D., Trelle,S., & Rodondi,N. (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. 10.1136/bmjopen-2018-026769
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. https://doi.org/10.3390/app9010150
Shen,Z., Krimpen,H. van, & Spruit,M. (2019). A lightweight API-based approach for building flexible clinical NLP systems. Journal of Healthcare Engineering, 11, Article ID 3435609. 10.1155/2019/3435609
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. 10.1001/jamanetworkopen.2019.6709
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. 10.4018/IJBIR.2019010105
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. 10.4018/IJSR.20190701.oa1
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), 643-653. 10.1016/j.tele.2018.04.002
Syed,S., Borit,M., & Spruit,M. (2018). Narrow lenses for capturing fisheries complexity: A topic analysis of fisheries science from 1990 to 2016. Fish and Fisheries 19(4), 643-661. 10.1111/faf.12280
Homberg,M. van den, Monné,R., & Spruit,M. (2018). Bridging the information gap of disaster responders by optimizing data selection using cost and quality. Computer & Geosciences, 120, 60-72. 10.1016/j.cageo.2018.06.002
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. 10.3390/app8060981
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), 727-736. 10.1016/j.tele.2017.08.002
Seddik Tawfik,N., & Spruit,M. (2018). The SNPCurator: Literature mining of SNP disease association. Database: The Journal of Biological Databases and Curation, 2018, bay020. 10.1093/database/bay020
Pieket Weeserik,B., & Spruit,M. (2018). Improving Operational Risk Management using Business Performance Management technologies. Sustainability, 10(3), 640. 10.3390/su10030640
Syed,S., & Spruit,M. (2018). Exploring Symmetrical and Asymmetrical Dirichlet Priors for Latent Dirichlet Allocation. International Journal of Semantic Computing, 12(3), 1-25. 10.1142/S1793351X18400184

2017-2014 [ 2025 | 2025 | 2024 | 2023 | 2022 | 2021 | 2020-2018 | 2013-2006 | ^ ]

Buijs,M., & Spruit,M. (2017). Asynchronous social search as a single point of access to information. Library Hi Tech, 35(4), 656-671. 10.1108/LHT-01-2017-0007
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. 10.1089/adt.2017.794
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. 10.1049/iet-sen.2016.0301

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. 10.1007/s10916-015-0423-z
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. 10.1016/j.chb.2016.01.024
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. 10.1089/adt.2016.726
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, 11, 9089321. 10.1155/2016/9089321
Mijnhardt,F., Baars,T., & Spruit,M. (2016). Organizational Characteristics Influencing SME Information Security Maturity. Journal of Computer Information Systems, 56(2), 106-115. 10.1080/08874417.2016.1117369
Baars,T., Mijnhardt,F., Vlaanderen,K., & Spruit,M. (2016). An Analytics Approach to Adaptive Maturity Models using Organizational Characteristics. Decision Analytics, 3(5). 10.1186/s40165-016-0022-1
Stroe,A., Spruit,M., Koelemeijer,S., & Beltman,B. (2016). PMOMM: The Project Management Office Maturity Model. International Journal of Knowledge Society Research, 7(3), 47-61. 10.4018/IJKSR.2016070104
Spruit,M., & Pietzka,K. (2015). MD3M: The Master Data Management Maturity Model. Computers in Human Behavior, 51(B), 1068-1076. 10.1016/j.chb.2014.09.030
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. 10.1007/s40266-015-0270-0
Spruit,M., & Sacu,C. (2015). DWCMM: The Data Warehouse Capability Maturity Model. Journal of Universal Computer Science, 21(11), 1508-1534. 10.3217/jucs-021-11-1508
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. 10.4018/IJSDS.2015070101
Spruit,M., & Adriana,T. (2015). Quantifying education quality in secondary schools. International Journal of Knowledge Society Research, 6(1), 55-87.

10.4018/IJKSR.2015010104

Otten,S., Spruit,M., & Helms,R. (2015). Towards decision analytics in product portfolio management. Decision Analytics, 2(4). 10.1186/s40165-015-0013-7
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. 10.4018/IJBIR.2015010102
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. 10.1504/IJIQ.2015.071672
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, Special Issue: ICTs for Human Capital, 698-707. 10.1016/j.chb.2013.07.038
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. 10.4018/ijbir.2014070105
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. 10.4018/IJBIR.2014100103
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. 10.1186/2193-8636-1-4
Pachidi,S., Spruit,M., & Weerd,I. van der (2014). Understanding Users' Behavior with Software Operation Data Mining. Computers in Human Behavior, 30, Special Issue: ICTs for Human Capital, 583-594. 10.1016/j.chb.2013.07.049
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. 10.4018/ijmdem.2014010103

2013-2006 [ 2025 | 2024 | 2023 | 2022 | 2021 | 2020-2018 | 2017-2014 | ^ ]

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. 10.1177/1460458212472333
Verkooij,K., & Spruit,M. (2013). Mobile Business Intelligence: Key considerations for implementation projects. Journal of Computer Information Systems, 54(1), 23-33. 10.1080/08874417.2013.11645668
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. 10.4018/IJHISI
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. 10.4018/jitsa.2013010103
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, Published 6/28/2012, 1662-1678. 10.3217/jucs-018-12-1662
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. 10.4018/jsita.2012010103
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. 10.4018/jisp.2012100105
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. 10.4018/jisp.2012010102
Omta,W., Egan,D., Spruit,M., & Brinkkemper,S. (2012). Information Architecture in High Throughput Screening. Procedia Technology, 5, 696-705. 10.1016/j.protcy.2012.09.077
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. 10.4018/jbir.2012040104
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.

www.mirlabs.org/ijcisim/ regular_papers_2012/Paper34.pdf

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.

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. academic-publishing.org/ index.php/ejkm/article/view/915
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. 10.1504/IJECRM.2011.039797
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. www.mirlabs.org/jias/ secured/Volume5-Issue1/Bruijn.pdf
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. 10.4018/jbir.2010070104
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. www.steiner-verlag.de/en/ Low-Saxon-Dialects-across- borders-Niedersaechsische- Dialekte-ueber-Grenzen-hinweg/9783515093729
Spruit,M., Heeringa,W., & Nerbonne,J. (2009). Associations among linguistic levels. Lingua, 119(11), The forests behind the trees, 1624-1642. 10.1016/j.lingua.2009.02.001
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. www.tntl.nl/index.php/ tntl/article/view/150
Spruit,M. (2006). Measuring syntactic variation in Dutch dialects. Literary and Linguistic Computing, 21(4), Progress in Dialectometry: Toward Explanation, 493-506. 10.1093/llc/fql043

Conference proceedings [ ^ ]

2025 [ 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013-2005 | ^ ]

Khalil, S., Tawfik,N., & Spruit,M. (In press). TVFed-P: Tversky-based Federated Learning with Personalized Loss Parameterization for Medical Imbalanced Data. 3rd Workshop on Advancements in Federated Learning Technologies (WAFL) at ECML-PKDD 2025, 15 Sept 2025, Porto. [preprint]

Van Dijk,B., Kuiper,T., Aoulad si Ahmed,S., Mooijaart,S., Duin,J., Lefebvre,A., Johnson,J., & Spruit,M. (2025). Out of the Box, into the Clinic? Evaluating State-of-the-Art ASR for Clinical Applications for Older Adults, Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP) (pp. 72-78), Conference on Empirical Methods for Natural Language Processing (EMNLP 2025), 9 Nov 2025, Suzhou, China. 10.18653/v1/2025.hcinlp-1.7

Klaassen,W., Van Dijk,B., & Spruit,M. (2025). A Review of Challenges in Speech-based Conversational AI for Elderly Care. 35th Medical Informatics Europe (MIE) Conference 2025, Intelligent health systems - From technology to data and knowledge, 327 (pp. 858-862). 19-21 May 2025, Glasgow, Scotland. 10.3233/SHTI250481

Leito,R., Lefebvre,A., Van Dijk,B., & Spruit,M. (2025). A natural and unobtrusive conversation using a RASA-driven chatbot for monitoring the wellbeing of elderlies. 35th Medical Informatics Europe (MIE) Conference 2025, Intelligent health systems - From technology to data and knowledge, 327 (pp. 979-983). 19-21 May 2025, Glasgow, Scotland. 10.3233/SHTI250518

Achterberg,J., Van Dijk,B., Islam,S., Waseem,M., Gallos,P., Epiphaniou,G., Maple,C., Haas,M., & Spruit,M. (2025). The Data Sharing Paradox of Synthetic Data in Healthcare. 35th Medical Informatics Europe (MIE) Conference 2025, Intelligent health systems - From technology to data and knowledge, 327 (pp. 582-586). 19-21 May 2025, Glasgow, Scotland. 10.3233/SHTI250404

2024 [ 2025 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013-2005 | ^ ]

Van Dijk,B., Ul Islam,S., Achterberg,J., Muhammad Waseem,H., Gallos,P., Epiphaniou,G., Maple,C., Haas,M., & Spruit,M. (2024). A Novel Taxonomy for Navigating and Classifying Synthetic Data in Healthcare Applications. In Stoicu-Tivadar et al. (eds), Studies in Health Technology and Informatics, 321, Collaboration across Disciplines for the Health of People, Animals and Ecosystems. EFMI Special Topic Conference (STC 2024) (pp. 259-263), 27-29 Nov 2024, Timisoara, Romania. 10.3233/SHTI241104
Lefebvre,A., de Schipper,L., Haas,M., & Spruit,M. (2024). Empowering Translational Health Data Science Capabilities in Population Health Management A Case of Building a Data Competence Center. In van de Wetering et al. (Eds.): I3E 2024, 23rd IFIP Conference e-Business, e-Services, and e-Society (I3E 2024), Lecture Notes in Computer Science, 14907. 11-13 September 2024, Heerlen, Netherlands. 10.1007/978-3-031-72234-9_33
Gallos,P., Matragkas,N., Ul Islam,S., Epiphaniou,G., Hansen,S., Harrison,S., Van Dijk,B., Haas,M., Pappous,G., Brouwer,S., Torlontano,F., Farooq Abbasi,S., Pournik,O., Churm,J., Mantas,J., Luis Parra-Calderón,C., Petkousis,D., Weber,P., Dzingina,B., Mraidha,C., Maple,C., Achterberg,J., Spruit,M., Saratsioti,E., Moustaghfir,Y., & Arvanitis,T. (2024). INSAFEDARE Project: Innovative Applications of Assessment and Assurance of Data and Synthetic Data for Regulatory Decision Support. Studies in health technology and informatics, 316, 1193-1197. 34th Medical Informatics Europe Conference (MIE 2024), 25-29 Aug 2024, Athens, Greece.
Haastrecht,M., Brinkhuis,M., & Spruit,M. (2024). Federated Learning Analytics: Investigating the Privacy-Performance Trade-Off in Machine Learning for Educational Analytics. In: Olney et al. (eds), Artificial Intelligence in Education (AIED 2024), Lecture Notes in Computer Science, 14830 (pp. 62-74). 8-12 July 2024, Recife, Brazil. 10.1007/978-3-031-64299-9_5
Van Dijk,B., Duijn,M. van, Kloostra,L., Spruit,M., & Beekhuizen,B. (2024). Using a Language Model to Unravel Semantic Development in Children's Use of a Dutch Perception Verb. 8th Workshop on Cognitive Aspects of the Lexicon (CogALex@ LREC-COLING 2024) (pp. 98-106). 20 May 2024, Torino, Italy. 2024 - Dijk Duijn Kloostra Spruit Beekhuizen.pdf
Wang,R., Verberne,S., & Spruit,M. (2024). Attend All Options at Once: Full Context Input for Multi-choice Reading Comprehension. In European Conference on Information Retrieval (ECIR 2024) (pp. 387-402). 24-28 March 2024, Glasgow, Scotland. Cham: Springer. 10.1007/978-3-031-56027-9_24
Kouwenhoven, T., Peeperkorn, M., van Dijk, B., & Verhoef, T. (2024, August). The curious case of representational alignment: Unravelling visio-linguistic tasks in emergent communication. In Kuribayashi, T., Rambelli, G., Takmaz, E., & Oseki, Y.(eds.) Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (pp. 57-71). Association for Computational Linguistics. 10.18653/v1/2024.cmcl-1.5

2023 [ 2025 | 2024 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013-2005 | ^ ]

Van Dijk,B., Kouwenhoven,T., Spruit,M., & Duijn, M. van (2023). Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding. Conference on Empirical Methods in Natural Language Processing (EMNLP 2023) (pp. 12641-12654). ACL. December 6-10, Singapore. aclanthology.org/2023.emnlp-main.779/
Van Dijk,B., Duijn, M., Verberne,S., & Spruit,M. (2023). ChiSCor: A Corpus of Freely-Told Fantasy Stories by Dutch Children for Computational Linguistics and Cognitive Science. The SIGNLL Conference on Computational Natural Language Learning (CoNNL 2023) (pp. 352-363). ACL. December 6-7, Singapore. (best paper award) aclanthology.org/2023.conll-1.23/
Duijn,M., Van Dijk,B., Kouwenhoven,T., de Valk,W., Spruit,M., & Putten,P. van der (2023). Theory of Mind in Large Language Models vs. Children: Examining Non-Literal Language Comprehension and Recursive Intentionality. The SIGNLL Conference on Computational Natural Language Learning (CoNNL 2023) (pp. 389-402). ACL. December 6-7, Singapore. aclanthology.org/2023.conll-1.25/
Van Dijk,B., Spruit,M., Duijn, M. (2023). Theory of Mind in Freely-Told Children's Narratives: A Classification Approach. Findings of the Association for Computational Linguistics (ACL 2023) (pp. 12979-12993). ACL. 9-14 July, Toronto, Canada. aclanthology.org/2023.findings-acl.822/
Haastrecht,M., Brinkhuis,M., Peichl,J., Remmele,B., & Spruit,M. (2023). Embracing Trustworthiness and Authenticity in the Validation of Learning Analytics Systems. 13th International Conference on Learning Analytics and Knowledge (LAK 2023) (pp. 552-558). ACM. Arlington, Texas, USA. 10.1145/3576050.3576060
Rijcken,E., Scheepers,S., Zervanou,K., Spruit,M., Mosteiro,P., Kaymak,U. (2023). Towards Interpreting Topic Models with ChatGPT. The 20th World Congress of the International Fuzzy Systems Association (IFSA 2023). Paper 20. Daegu, Korea, 20-24 Aug 2023. research.tue.nl/files/300364784/ IFSA_InterpretingTopicModelsWithChatGPT.pdf
Rijcken,E., Zervanou,K., Spruit,M., Scheepers,S., Kaymak,U. (2023). Effect of calculating Pointwise Mutual Information using a Fuzzy Sliding Window in Topic Modeling. 2023 IEEE International Conference on Fuzzy Systems (FUZZ 2023). Songdo Incheon, Korea, 13-17 Aug 2023. 10.1109/FUZZ52849.2023.10309675

2022 [ 2025 | 2024 | 2023 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013-2005 | ^ ]

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. aclanthology.org/2022.semeval-1.35
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. aclanthology.org/2022.wnu-1.8
Rijcken,E., Mosteiro,P., Zervanou,K., Spruit,M., Scheepers,F., & Kaymak,U. (2022). FuzzyTM: a Software Package for Fuzzy Topic Modeling. IEEE International Conference on Fuzzy Systems 2022 (pp. 1-8). IEEE WCCI 2022: FUZZ-IEEE, 18-23 July, Padua, Italy: IEEE. 10.1109/FUZZ-IEEE55066.2022.9882661
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. 10.1109/SMC53654.2022.9945594

2021 [ 2025 | 2024 | 2023 | 2022 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013-2005 | ^ ]

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. ECIS 2021, Marrakech, Morocco. aisel.aisnet.org/ecis2021_rp/84/
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. dl.acm.org/doi/10.1145/3465481.3469199
Rijcken,E., Scheepers, Mosteiro,P., Zervanou,K., Spruit,M., & Kaymak,U.,F. (2021). A Comparative Study of Fuzzy Topic Models and LDA in terms of Interpretability. IEEE Symposium Series on Computational Intelligence. SSCI 2021, Dec 5-7, Orlando, Florida. 10.1109/SSCI50451.2021.9660139
Spruit,M., & Vries,N. de (2021). Self-Service Data Science for Adverse Event Prediction in Electronic Healthcare Records. In Visvizi,A., Lytras,M., & Aljohani,N. (Eds.), Springer Proceedings in Complexity, Research and Innovation Forum 2020: Disruptive Technologies in Times of Change (pp. 517-535). RII 2020, April 17-19, Athens, Greece: Springer. 10.1007/978-3-030-62066-0_39

2020 [ 2025 | 2024 | 2023 | 2022 | 2021 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013-2005 | ^ ]

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. 10.5220/0009169507240734
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. 10.1007/978-3-030-57404-8_13
Toledo,C. van, Dijk,F. van, & Spruit,M. (2020). Evaluating Dutch Named Entity Recognition and De-identification Methods in the Human Resources Domain. In Wyld,D. et al. (Ed.), Proceedings of the International Conference on NLP Techniques and Applications (pp. 239-249). NLPTA 2020, 28-29 Nov 2020, London, United Kingdom: AIRCC Publishing Corporation.10.5121/csit.2020.101520
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. 10.1007/978-3-030-61951-0_1

2019 [ 2025 | 2024 | 2023 | 2022 | 2021 | 2020 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013-2005 | ^ ]

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. 10.1007/978-3-030-30809-4_30
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. 10.1007/978-3-030-30809-4_32
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).

10.18653/v1/W19-5053

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. 10.1007/978-3-030-23281-8_28
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. 10.1007/978-3-030-23281-8_28
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. 10.1007/978-3-030-21642-9_31
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. https://doi.org/10.5220/0007343900410050
Lefebvre, A., & Spruit,M. (2019). Designing Laboratory Forensics. In Pappas,I. Mikalef,P., Dwivedi,Y., Jaccheri,L., Krogstie,J., Mäntymäki,M. (Eds.), Lecture Notes in Computer Science 11701, Digital Transformation for a Sustainable Society in the 21st Century, I3E 2019, Trondheim, Norway. https://doi.org/10.1007/978-3-030-29374-1_20
Lefebvre,A., & Spruit,M. (2019). A Socio-Technical Perspective on Reproducibility Challenges in Research Data Management. Mediterranean Conference on Information Systems 2019 Proceedings, 10. Napels, Italy. aisel.aisnet.org/mcis2019/10
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. 10.1145/3342827.3342843
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. 10.5220/0007378702940301
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. 10.1007/978-3-030-23281-8_31
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. 10.1007/978-3-030-12085-6_5

2018 [ 2025 | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2017 | 2016 | 2015 | 2014 | 2013-2005 | ^ ]

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. aisel.aisnet.org/ecis2018_rp/35
Syed,S., & Spruit,M. (2018). Selecting Priors for Latent Dirichlet Allocation. 12th IEEE International Conference on Semantic Computing (pp. 194-202). Laguna Hills, California, USA. 10.1109/ICSC.2018.00035
Seddik 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. 10.1007/978-3-319-96136-1_12
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 Preproceedings (pp. 223-234). BNAIC, November 8-9, 2018, 's-Hertogenbosch, Netherlands: Springer CSAI / JADS. research.tue.nl/en/ publications/30th-benelux-conference-on- artificial-intelligence-bnaic-2018-pre
Yigit Ozkan,B., & Spruit,M. (2018). Assessing and Improving Cybersecurity Maturity for SMEs: Standardization aspects. 1st SMESEC Workshop. 1st SMESEC Workshop, September 14, 2018. 10.48550/arXiv.2007.01751
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). Funchal, Portugal. 10.5220/0006550103390348
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). Funchal, Portugal. 10.5220/0006541603000307

2017 [ 2025 | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2016 | 2015 | 2014 | 2013-2005 | ^ ]

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. https://doi.org/10.5220/0006499602060214
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. 10.1109/DSAA.2017.61
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. 10.1007/978-3-319-59758-4_38
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. 10.1007/978-3-319-62911-7_6
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. 10.5220/0006056701910198

2016 [ 2025 | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2015 | 2014 | 2013-2005 | ^ ]

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. 10.5220/0006081604000406
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. 10.5220/0006036901890196
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. 234-239). KDIR 2016, November 11-13, 2016, Porto, Portugal: ScitePress. www.scitepress.org/ ProceedingsDetails.aspx?ID=IKmvnOTP0ag=
Shen,Z., Meulendijk,M., & Spruit,M. (2016). A federated information architecture for multinational clinical trials: STRIPA revisited. 24th European Conference on Information Systems (ECIS). Prototypes. 2. Istanbul, Turkey. aisel.aisnet.org/ecis2016_prototypes/2
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. In: Hostettler, S., Besson, S., Bolay, J. (eds), Technologies for Development, UNESCO 2016. 10.1007/978-3-319-91068-0_18

2015 [ 2025 | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2014 | 2013-2005 | ^ ]

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. aisel.aisnet.org/ecis2015_rip/29
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. 10.1007/978-3-319-25840-9_8
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. 10.5220/0005597602490258
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. 10.5220/0005631604560462
Spruit,M., Visee,Y., & Jong,E. de (2015). DIA: het Docent-ICT Adoptie raamwerk - Verbinden van onderwijsvormen en onderwijstechnieken via onderwijstaken. Onderwijs Research Dagen 2015, Leiden. www.vorsite.nl/nl/content/ eerdere-onderwijs-research-dagen
Haasbroek,J., & Spruit,M. (2015). De ideale docent anno 2015: Docentgedrag en studenttevredenheid binnen het universitaire bachelor onderwijs. Onderwijs Research Dagen 2015, Leiden. www.vorsite.nl/nl/content/ eerdere-onderwijs-research-dagen

2014 [ 2025 | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2013-2005 | ^ ]

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. 10.5220/0005076400710077
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. aisel.aisnet.org/ecis2014/proceedings/track09/4
Spruit,M., & Roeling,M. (2014). ISFAM: the Information Security Focus Area Maturity model. 22nd European Conference on Information Systems. Tel Aviv, Israel. aisel.aisnet.org/ecis2014/proceedings/track14/6

2013-2005 [ 2025 | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | ^ ]

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. 10.1007/978-3-642-41030-7_55
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. www.ifiptc11.org
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. dblp.org/db/conf/wsks
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. dblp.org/db/conf/wsks
Polman,T., & Spruit,M. (2013). 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. 10.1007/978-3-642-35879-1_56
Helms,R., Booij,E., & Spruit,M. (2012). Reaching out: Involving users in innovation tasks through social media. 20th European Conference on Information Systems (pp. Paper 193). ECIS 2012, June 10-13, 2012, Barcelona. aisel.aisnet.org/ecis2012/193/
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, International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2011) (pp. 324-334). Springer. 10.1007/978-3-642-29752-6_24
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. dblp.org/db/conf/wsks
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. 10.5220/0003157700610069
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. 10.5220/0003661704520456
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. academic-publishing.org/ index.php/ejkm/article/view/915
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. 10.1109/IWSPM.2010.5623871
Bekkers,W., Spruit,M., Weerd,I. van de, Vliet,R. van, & Mahieu,A. (2010). A Situational Assessment Method For Software Product Management. 18th European Conference on Information Systems. Pretoria, South Africa. http://aisel.aisnet.org/ecis2010/22/
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. 10.1007/978-3-642-15666-3_1
Kormaris,G., & Spruit,M. (2010). Bridging the Gap between Web 2.0 Technologies and Social Computing Principles. Communications in Computer and Information Science 87, Networked Digital Technologies - Second International Conference (pp. 430-443). NDT 2010, July 7-9, 2010, Prague, Czech Republic. 10.1007/978-3-642-14292-5_44
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. 10.5220/0002967402880293
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. www.mirlabs.org/ijcisim/ regular_papers_2012/Paper34.pdf
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. 10.4018/978-1-61692-020-3.ch011
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. 10.4018/jisp.2012100105
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. 10.4018/978-1-61520-623-0.ch009
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 available. PAKM 2008, November 21-23, 2008, Yokohama, Japan: Springer. 10.1007/978-3-540-89447-6_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.

10.1007/978-3-540-87781-3_41

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. www.clinjournal.org/ CLIN_proceedings/XVII/spruit.pdf
Spruit,M. (2005). Classifying Dutch dialects using a syntactic measure: The perceptual Daan and Blok dialect map revisited. In dir (Ed.), Linguistics in the Netherlands 2005 (pp. 179-190). Amsterdam: John Benjamins. dare.uva.nl/document/37601

Books [ ^ ]

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, Leiden: Leiden University. 10.5281/zenodo.7665857
Spruit,M. (2008). Quantitative perspectives on syntactic variation in Dutch dialects. LOT Dissertation Series 174, Doctoral disseration, University of Amsterdam, The Netherlands, Utrecht: LOT. hdl.handle.net/11245/1.299758

Book chapters [ ^ ]

2024 [ 2020 | 2018 | 2017-2015 | 2013-1995 | ^ ]

Spruit,M., & Ferati,D. (2024). Text Mining Business Policy Documents: Applied Data Science in Finance. Research Anthology on Business Law, Policy, and Social Responsibility, (pp. 1525-1545). 10.4018/979-8-3693-2045-7.ch077
Mosteiro,P., Kuiper,J., Masthoff,J., Scheepers,F., & Spruit,M. (2024). Bias Discovery in Machine Learning Models for Mental Health. In Reprint: Advances in Explainable Artificial Intelligence. 10.3390/books978-3-7258-0284-5

2020 [ 2024 | 2018 | 2017-2015 | 2013-1995 | ^ ]

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. 10.1016/B978-0-12-819043-2.00004-6
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. 10.1108/978-1-78973-627-420191010
Spruit,M., & Adriana,T. (2020). Business Intelligence in Secondary Education: Data-Driven Innovation by Quality Measurement. In I. Management Association (Ed.), Research Anthology on Preparing School Administrators to Lead Quality Education Programs (pp. 565-597). IGI Global. 10.4018/978-1-7998-3438-0.ch026
Yigit Ozkan,B., & Spruit,M. (2020). Cybersecurity standardisation for SMEs: The stakeholders' perspectives and a research agenda. Research Anthology on Artificial Intelligence Applications in Security (pp. 1252-1278). IGI Global. 10.4018/978-1-7998-7705-9.ch056

2018 [ 2024 | 2020 | 2017-2015 | 2013-1995 | ^ ]

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. 10.4018/978-1-5225-2607-0.ch010
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. 10.4018/978-1-5225-4191-2.ch004
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). Springer. 10.1007/978-3-319-91068-0_18

2017-2015 [ 2024 | 2020 | 2018 | 2013-1995 | ^ ]

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. novapublishers.com/shop/security-risks- assessment-management-and-current-challenges/
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. 10.4018/978-1-4666-9840-6.ch009
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. 10.4018/978-1-5225-0248-7.ch005
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. 42029). Nova Publishers. novapublishers.com/shop/ business-intelligence-strategies-and-ethics/

2013-1995 [ 2024 | 2020 | 2018 | 2017-2015 | ^ ]

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. 10.4018/978-1-4666-2125-1.ch002
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. 10.4018/978-1-4666-3990-4.ch056
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. books.google.nl/books?id=SmURBAAAQBAJ&pg=PA22
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. 10.4018/978-1-61350-195-5.ch002
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. 10.4018/978-1-61350-498-7.ch018
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. 10.4018/978-1-4666-1972-2.ch003
Vleugel,A., Spruit,M., & Daal,A. van (2012). Historical data analysis through data mining from an outsourcing perspective: the three-phases method. In Herschel,R. (Ed.), Organizational Applications of Business Intelligence Management: Emerging Trends (pp. 236-260). IGI Global. 10.4018/978-1-4666-0279-3.ch017
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. 10.4018/978-1-61692-877-3.ch010
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. 10.1007/978-1-4419-7904-9_8
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). 10.4018/978-1-61692-020-3.ch011
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. 10.4018/978-1-61520-623-0.ch009
Brinkkemper,S., Batenburg,R., Versendaal,J., Wijngaert,L. van de, Helms,R., Bos,R., Jansen,S., Spruit,M., Ravesteijn,P., Huizer,E., Weerd,I. van de, Baaren,E., Plomp,M., & Schuur,H. van der (2009). On the Challenge of Creating an Attractive Research Master Program: Graduate Education Avant-la-Lettre. In Bodlaender,H., Duivesteijn,W., & Nijenhuis,C. (Eds.), Fascination for Computation: 25 jaar informatica (pp. 217-240). www.academia.edu/download/ 33578941/bbvlhbjsrhwbps2009jubi.pdf
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. 10.1007/978-3-540-87781-3_41
Spruit,M. (1995). FILTER prototype. In Scholtes,J. (Ed.), Artificial neural networks for information retrieval in a libraries context (pp. 213-251). op.europa.eu/s/zGax

Miscellaneous [ ^ ]

2024-2021 [ 2019 | 2017-2014 | 2013-2006 | ^ ]

Liem,M., Haas,M., Spruit,M., Krüsselmann,K., & Achterberg,J. (2024). Making Sensitive Data Open and Fair Through Synthetic Data Generation - A Guidebook. Zenodo. 10.5281/zenodo.13752141
Haastrecht,M., Brinkhuis,M., & Spruit,M. (2023). VAST Guideline, version 2. End-user companion guide to (Haastrecht, Brinkhuis, Wools & Spruit, 2023). osf.io/4ygf7/
Menger,V., Spruit,M., & Scheepers,F. (2021). Kennisontwikkeling in de klinische psychiatrie: leren van elektronische patiëntendossiers. Tijdschrift voor Psychiatrie, 63(4), 294-300. www.tijdschriftvoorpsychiatrie.nl/ issues/563/articles/12595
ETSI (2021). CYBER; Cybersecurity for SMEs; Part 1: Cybersecurity Standardization Essentials. ETSI TR 103 787-1. B Yigit Ozkan & M Spruit (eds.). https://www.etsi.org/deliver/etsi_tr/ 103700_103799/10378701/01.01.01_60/ tr_10378701v010101p.pdf
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. 10.5281/zenodo.4555589

2019 [ 2024-2021 | 2017-2014 | 2013-2006 | ^ ]

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. 2019 - Renes Spruit.pdf
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. 2019 - Haan Spruit Zoet.pdf
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. 2019 - Janssen Spruit.pdf
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. 2019 - Spruit Lingen Ozkan.pdf
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.2019 - Levebfre Berendsen Spruit.pdf
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. 2019 - Spruit Linden.pdf

2017-2014 [ 2024-2021 | 2019 | 2013-2006 | ^ ]

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. 2017b - Meulendijk Spruit Brinkkemper.pdf
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. 2016 - Shen et al - IMDD.pdf
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. 2014 - Spruit Pietzka.pdf
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. 2014 - Boer Spruit.pdf
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. 2014 - Reijmer Spruit.pdf

2013-2006 [ 2024-2021 | 2019 | 2017-2014 | ^ ]

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. 10.4018/ijbir.2014070105
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. 2013 - Wester Spruit.pdf
Stroe,A., Koelemeijer,S, & Spruit,M. (2013). Een PMO is meer dan een administratiekantoor. Controllers Magazine. Management Accounting & Control. 2013 - Stroe Koelemeijer Spruit.pdf
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. 2010b - Sacu Spruit.pdf
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. 2010 - Sacu Spruit Habers.pdf
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. 2010 - Bekkers Spruit Weerd Vliet Mahieu.pdf
Spruit,M. (2006). Tellen met Taal. Het meten van variatie in zinsbouw in Nederlandse dialecten. Respons: Mededelingen van het Meertens Instituut, 8, 12-16. 2006b - Spruit.pdf

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