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 analyse Big Data in Health & Care
Key words: real-world data, big data, health and care, multidisciplinary care paths, data infrastructructures
Disciplines: Biomedical and life sciences research, data management, data wrangling, data integration, machine learning, artificial intelligence
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
Will this activity lead to insights that can disrupt the status quo of our health & care system?
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 matter. 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.
Do we need “real-world-insights” (and thus so called “Real-World Data)?
Real-World Data (RWD) are defined as data derived from a number of data sources that are associated with outcomes in a heterogeneous patient population representing real-world settings. 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:
Is it difficult for the data scientists and biomedical researchers to talk to each other?
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.
Lotte Geys, Post-doc/Project coordinator
Lotte got her PhD in Biomedical sciences in 2017 at the University of Leuven and started working as clinical data manager afterwards. In December 2019, she started working for the research group of Liesbet Peeters. She is Liesbet’s right-hand woman, and is involved in follow-up of nearly all the projects and activities of the group, including the MSDA activities.
Ashkan Pirmani, PhD Student
Ashkan got his master degree in industrial engineering in 2018 and after that did another master's in MBA. He has started his adventure as a joint Ph.D. student in mid-2019 with the close collaboration of STADUIS/ESAT lab of KU Leuven.
He is currently working on a federated learning framework to apply this framework in multiple sclerosis using real-world data. He believes in FAIR data. As "Accessibility" is one of these FAIR data principles, He aims to launch infrastructure and framework that learns from other data sources without sacrificing privacy in real-world data. He believes we already pass the centralized data silos era for big data research, and we have to come up with ideas to know how to share data, of course, by taking into account all the ethical and legal challenges.
His other research interest is machine learning, specifically artificial neural networks, BlockChain, and DevOps, with a keen interest in containerization.
Tina Parciak, PhD Student
Tina got her Master’s degree in Informatics with focus on Medical Informatics in 2013. Since 2014, she has been working at the Department of Medical Informatics of the University Medical Center Göttingen, focussing on IT infrastructure and data processing in the field of multiple sclerosis. Tina specialises on the topic of data harmonisation where she first was introduced to in the EUReMS project, where European MS registries collaborated to estimate the feasibility of joining the distributed and heterogeneous registry data for joint analyses. Since 2019 Tina is involved in the Multiple Sclerosis Data Alliance, where she has been responsible for the data harmonisation aspect within the FAIR spectrum. That partnership resulted in a joint PhD position with the BIOMED institute of University Hasselt, with Liesbet Peeters as promoter. In the PhD she focuses on the automatisation of data harmonisation for real-world MS data.
Hamza Khan, PhD Student
After working as a dental surgeon for one year, Hamza got his Master degree in Public Health (MPH) in 2019. Realising the importance of data in the healthcare domain and the need for acquaintance with robust statistical methods/machine learning during his internship at the migrant health division (MHD) of the International Organization for Migration (IOM - Regional Office for EU/EEA and NATO, Brussels), he enrolled in another master degree program at Tilburg University, The Netherlands. He graduated with a Master in Cognitive Science and Artificial Intelligence (MCSAI) in August of 2020 after which he started his interdisciplinary PhD at BioMed UHasselt and Department of Precision Medicine at UMaastricht.
The focus of his PhD is finding biomarkers for the diagnosis of multiple sclerosis by applying AI/Machine learning algorithms on evoked potential time series data and MRIs. The other areas of focus of his research include improvement of the quality of life and personalized treatment plan for MS patients.
Marcel Parciak, PhD Student
Marcel finished his applied computer science studies in 2017 earning a masters degree. Afterwards he worked as a research associate at the medical data integration center of the university medical center Göttingen. During this time his responsibilities involved development and set up of IT-infrastructures, data engineering and software engineering. He joined the team in 2021 to work on his PhD thesis. He
develops AI methods and tools that accelerate the set up of data integration infrastructures, enabling researchers to get the right data quicker. Marcel's research interests include AI, data integration and health data science. He is an open-source enthusiast that aims to create open,
collaborative and automated solutions.
Nasim Shabani graduated in Computer Science. She has been working as a full-stack software engineer since 2009, building high quality, scalable solutions for the web across a wide range of industries.
She is always excited about new technologies; among them Augmented Reality is a promising technology that excites her in both technical and application points of view.
She's been collaborating on the MS Data Alliance since 2021, working on the platform to allow data federation across service providers and end-users to browse metadata profiles of MS real-world data cohorts.
Cathérine Dekeyser, PhD Student
Thijs Becker, voluntary scientific employee
Jan Yperman, voluntary scientific employee
Jan got his master’s degree at KU Leuven in 2015, after which he started a PhD on applied machine learning in the group of prof. Christian Van den Broeck. During his PhD he has worked on various machine learning projects, ranging from automated traffic analysis to healthcare decision support systems. The main topic of the PhD involved the gathering and analysis of Motor Evoked Potentials (MEP) in MS using machine learning. He finished his PhD in computer science in 2021.
Edward De Brouwer, PhD Student (KULeuven)
Edward graduated in Electrical Engineering from KU Leuven and UCLouvain in 2014 and started his PhD at KU Leuven in 2017 under the supervision of Prof. Yves Moreau. His main interests lie in machine learning algorithms for dealing with clinical patient trajectories. He’s been collaborating on the MS initiative since the beginning of his PhD, working on predicting disability progression worsening using patient clinical history and in the COVID-19 initiative.
Website MS Data Alliance
Website MS Data Connect Consortium
Website Flanders AI ProgrammaMSWeb (Data engineer Liesbet Peeters over MS & Corona)
FLAIR (Toepassingsdomein Gezondheid)
COVID-19 onderzoek (Beter omgaan met gezondheidsdata)
Big data kunnen onze gezondheidszorg verbeteren
Intervention on the Open Science Webinar with Top UHasselt COVID-19 researchers
Het grote AI debat
AI voor iedereen
Fair Health data: noodzaak voor een betere gezondheidszorg