Jip de Kok
Clinical Data Specialist
Maastricht University Medical Centre+ (MUMC+)
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Clinical Data Specialist
Maastricht University Medical Centre+ (MUMC+)
I am a Clinical Data Specialist at the Intensive Care department of Maastricht University Medical Centre+ (MUMC+), applying machine learning and data science to improve healthcare quality and equity in critical care. My research focuses on socioeconomic disparities in ICU outcomes, robust data infrastructure for clinical research, and advanced clustering methods for patient phenotyping. I am currently finalising my PhD and working as a Clinical Data Specialist on data-driven research to improve ICU care.
I graduated from the Maastricht Science Programme (BSc Liberal Arts and Sciences), where I studied computer science, mathematics, and cellular/molecular biology. I then worked as a data analyst at Etil, using Python for dashboarding, data analysis, and pipeline automation. I completed the Systems Biology Master at Maastricht University, learning to use mathematical modelling to understand and predict biological systems. Currently, I work as a Clinical Data Specialist at MUMC+, continuing to apply my skills to improve ICU care through research.
My PhD research investigates how socioeconomic status (SES) influences outcomes in the intensive care unit and how this affects fair benchmarking of hospitals. Using a nationwide Dutch ICU dataset linked to household-level SES information, I've demonstrated clear socioeconomic gradients in hospital mortality — even in a universal healthcare system. This work provides a strong foundation for incorporating SES into risk adjustment for performance metrics.
Robust data collection, preparation, and transparent reporting are prerequisites for high-quality ICU research and benchmarking. I've explored practical routes for sharing ICU data under GDPR, demonstrating that responsible data sharing is feasible through multiple approaches — from pseudonymised consent-based sharing to cloud-based non-downloadable access. My work on 'Table 0' documentation presents a best practice for reporting data preparation steps in clinical research.
I've explored deep embedded clustering (DEC) methods to identify clinically meaningful subgroups from heterogeneous ICU patient populations. By developing an adapted X-shaped DEC framework (X-DEC) that better handles mixed data types, I've shown that deep clustering can yield stable, clinically relevant patient phenotypes. I place particular emphasis on assessing the clinical relevance of identified clusters, and have evaluated cluster generalisability across different hospitals and populations. This work opens new possibilities for precision medicine and equity assessment in critical care.
Selected publications — full list on ResearchGate
Intensive Care Medicine Experimental, 13(1):17
Journal of Clinical Epidemiology, 179
Journal of Clinical Epidemiology, 170:111342
Journal of Clinical Epidemiology, 173
Thrombosis Research, 234:51–58
Intensive Care Medicine Experimental, 12(1):26
Annals of Noninvasive Electrocardiology, 29(5):e70001
De Intensivist, 32(4):196–197
Scientific Data, 10:404
Under review
Maastricht University Medical Centre+ (MUMC+)
Continuing work as a data scientist at the ICU department, improving quality of care through research and data-driven insights.
Maastricht University Medical Centre+ (MUMC+)
Applying machine learning and data science to investigate socioeconomic disparities in ICU outcomes, develop robust data infrastructure, and explore advanced clustering for patient phenotyping.
Maastricht University
Learned to use mathematical modelling to understand and predict behaviour of various biological systems, predominantly regarding human health.
Etil
Used Python for dashboarding, data analysis, and pipeline automation.
Scouts Association
Helping manage the scouts association as a board member, supporting the organisation and its activities.
Scouts
Led a group of scouts aged 9–11 for 10 years, organising activities, teaching outdoor skills, and mentoring young people.
Maastricht Science Programme
Studied computer science, mathematics, and cellular/molecular biology.
Stedelijk Gymnasium, Maastricht
Pre-university education (VWO) with a focus on biology, physics, and chemistry.
Selected projects from my academic journey
Development of an interactive virtual reality network for multi-omics data visualisation, compared to a pre-existing 2D network. Tested through a cross-over design study with participants evaluating both visualisation types using Likert scale surveys.
3D modelling and animation of the respiratory chain in mitochondria under paracetamol exposure, illustrating hepatotoxic effects on ATP synthesis. Individual components (phospholipids, ATP synthase, protons, co-enzymes) were modelled and animated to explain the electron transport chain and the effect of APAP.
Logo designs created during high school and university, including work for Phytome (European Commission-funded research on reducing carcinogenic components in red meat) and the Honours Programme of the Faculty of Health, Medicine and Life Sciences at Maastricht University.
Feel free to reach out for collaborations, questions, or just to say hello!