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The Translational Data Science & AI Laboratory

The Translational Data Science & AI Lab (a.k.a. TDS Lab)'s mission is to connect practical problems in healthcare practices to fundamental challenges in data science and to subsequently address both simultaneously. This is our encompassing Translational Data Science (TDS) research theme, which bridges the best of both worlds. We pursue a better fundamental understanding of the world around us through data science & AI innovations by being societally inspired, demand-driven and solution-oriented. See our About page for more information.

TDS Team

Current research grants (22)

2024-2025: 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.
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
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
2024: 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: 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. www.nwo.nl/en/projects/kich1gz0321007
2021-2024: 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/

Research topics wordcloud

Latest journal articles (114)

  1. Alfaraj,S., Kist,J., Groenwold,R., Spruit,M., Mook-Kanamori,D., & Vos,R. (In press). External validation of SCORE2-Diabetes in the Netherlands across various Socioeconomic levels in native-Dutch and non-Dutch populations. European Journal of Preventive Cardiology. 10.1093/eurjpc/zwae354
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
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