Methods for AI and data science
data science, artificial intelligence, methods research
Research aim
To develop and validate methods for data science, AI, statistics and causal inference and to improve medical research and patient care through impactful applications and collaborations.
About us
Innovations in data science and artificial intelligence (AI) are instrumental in advancing medical research and improving patient outcomes. Robust methods are essential to generate evidence on the efficacy of novel interventions, to tailor treatments to patient characteristics and to improve the efficiency and sustainability of healthcare. Ensuring the safety, fairness and reliability of these methods is paramount, and innovations should be properly validated before deployment.
The aim of our group is to develop novel methods for data science and AI and validate and apply these in clinical research through diverse collaborations. Our multidisciplinary group consists of (bio)statisticians, computer scientists, epidemiologists, ethicists, medical doctors, and physicists. We partner with various departments in UMC Utrecht, Utrecht University, other research and healthcare institutions, and industry partners. Our core research areas are:
• AI fairness and ethics (de Hond, Safarlou)
• Causal inference (van Amsterdam, Ryan, Choi, Penning-de Vries)
• High dimensional data, such as wearable sensors (e.g. smart watches), omics and biomarkers (el Bouhaddani, Lopez Rincon, Erler)
• Impact assessment and health-economic evaluation of AI innovations (Bartels, de Hond, van der Meulen)
• Innovative and adaptive methods for clinical trials (van Rosmalen)
• Natural language processing (van Leeuwenberg, Kuiper)
• Prediction modeling and machine learning (van Smeden, van Amsterdam)