Data Science Research Programme
The Faculty of Medicine
LUMC is a modern university medical center for research, education and patient care with a high quality profile and a strong scientific orientation.
Its unique research practice, ranging from pure fundamental medical research to applied clinical research, places LUMC among the world top.This enables LUMC to offer patient care and education that is in line with the latest international insights and standards – and helps it to improve medicine and healthcare both internally and externally.
Data Science Research Projects
AI in Neuroscience: Development of Methods to make Personalized Predictions for Migraine and Stroke from E-Health Sensor Data
eHealth is revolutionizing the healthcare system. Giving patients the opportunity to carry their own diagnostics tools not only improves care through more timely measurements, but also reduces costs of (unnecessary) hospital visits. A keys aspect in this is the data interpretation. This project is a collaboration between the Department of Neurology at the LUMC and the Explanatory Data Analysis group at the LIACS where we focus on making eHealth-based, personalized predictions for migraine attacks or stroke risk with the use of machine learning and AI.
Mortality and HyperImage: Visual analytics techniques for biomarker discovery in massive 3D-omics datasets
Over the past decade, several novel types of spatially resolved "omics imaging" data have become available.
Study of utilisation of combined hormonal contraceptives in Europe
The aim of the study is to assess whether the publication of the review by EMA and the legally binding decision to update the product information in January 2014, led to a change in CHC prescription patterns in Europe and, if so, if there was a subsequent change in the incidence rate of VTE.
Meta‐modelling: development of methods and tools for the exchange, analysis and communication of anonymised patient data
This PhD project is funded by Sanquin, a non-profit organization, mostly known for collecting blood products from donors and supplying them to patients in the Netherlands.
The interpretation of physical activity wearable data and its relation with metabolic and brain health in older adults
Although average human life expectancy has doubled in most developed countries in the last 200 years, 16-20% of life is spent in late-life disability and disease. Sedentary time has a particularly strong effect on mortality risk later in life. Older people spend, on average, almost 10 waking hours in an immobile posture. We wish to identify sedentary lifestyle and target its effects.