We envision a world where every single person gets the treatment they deserve in a timely matter.
We are convinced that data saves lives. Therefore, we investigate new methods to handle and analyze Big Data in Health & Care.
What we do?
The University of Hasselt aims to foresee high-quality education and believes that the therefore needed foundation is solid academic research, which is also an important link in the innovation chain. Additionally, UHasselt aims to serve the community by being a civic university.
Since our research group belongs to the UHasselt, we have different roles to fulfill: we investigate, we serve and we educate. As you will see in the overview of our projects, these activities cannot be separated from each other and are most often interlinked. We do research, but in a civic way and try to educate students to the best of our abilities.
An overview of the activities and projects we are involved in:
We use the following three key questions to scope our research activities.
Only when a new research project or potential future activity leads to a “triple-yes” to these questions, it fits within our scope of interest
We are convinced that our health & care system of today is not good enough. One of the main challenges of today is that disease management is mainly focusing on insight gathered at population level. Our dream is that one day every single person gets the treatment they deserve in a timely manner. We believe Data Saves Lives and we urgently need to supercharge our health & care system with insights using Big Data. Therefore, we investigate new methods to handle and analyse Big Data. Our current research focuses mostly on the neurodegenerative auto-immune disorder “multiple sclerosis” (MS). We focus on developing new biomarkers for disease activity and –progression and on developing decision-support systems for relative treatment effectiveness.
For us, Real-World Data (RWD) is defined as data derived from a number of data sources that are associated with outcomes in a heterogeneous patient population representing real-world settings (e.g. follow-up data collected by a healthcare provider during a routine patient visit). There is great potential in using RWD. However handling and analysing RWD is challenging and time-consuming. More specifically, we currently focus on the following technical challenges:
We need to build bridges to connect the world of biomedical research and life sciences with the world of data science. However, the people within those worlds speak different languages. We excel in bringing these two worlds together, because we have the unique talent to act as translators.