Group of Liesbet Peeters

Prof. Liesbet Peeters leads the Research Group in Biomedical Data Sciences and is affiliated to the Biomedical Research Institute (BIOMED) and Data Science Institute of UHasselt. Her team’s research is driven by the mission that one day every single person gets the treatment they deserve in a timely manner. One of the main challenges of today’s healthcare is that disease management is mainly focusing on insights gathered at population level. Prof. Peeters strives to achieve a “next-generation of management” by supercharging with insights gained from Big Data, following the idea that “Data Saves Lives”. Therefore, the group investigates new methods to handle and analyse Big Data, with a specific focus on the chronic disease multiple sclerosis (MS) and on 3 main clinical challenges:

• Identification of new biomarkers for disease activity and – progression
• Develop decision-support systems for relative treatment effectiveness in a real-world setting
• Develop tools and methodologies to support scaling-up real-world MS data research

dr. Lotte Geys has a doctoral degree in Biomedical Sciences (KU Leuven, June 2017) and started, after 2,5 years of work experience in the industry as clinical data manager, in December 2019 in the Research Group in Biomedical Data Sciences. Lotte’s role in the group is to coordinate and supervise most of the projects and activities as well as support the kick-off of new projects.

Dr. Axel Faes is Scientific Coordinator of the Flanders AI Research Program, Use Case Real World Evidence (FAIR UC RWE)

My research focuses on "Accelerating Open Data Integration of Real-World Health Data Silos".

The complexity of multiple sclerosis (MS) necessitates interventions from various healthcare professionals: general practitioners, neurologists, pharmacists and physiotherapists, just to name a few. These professionals may work on different sites with different IT systems supporting their routine work. Nevertheless, detailed, accurate and up-to-date information flows between these professionals are crucial for appropriate healthcare. These sites consequently generate high volumes of data in heterogeneous data formats that are continuously updated and modified. Health data engineers have to integrate such real-world datasets to unlock their full potential for healthcare professionals and health data scientists. Despite the similarities between health data integration and open data integration, which is a field that is studied intensively in the domain of computer science, I find that approaches and tools developed in the open integration domain are not applied to the health data domain. The sensitive nature of health data results in locked-down health data silos. Large corpora used to develop and test applications from the open data integration setting do not apply to the health data domain. Therefore, the tooling used for health data integration remains the same as for enterprise integration, even though health data’s volume, variety, velocity and veracity render these tools ineffective. This ineffectiveness leaves health data scientists with tedious, complex and time-consuming data integration tasks.