Research group in Biomedical Data Sciences

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 infrastructures

Disciplines: Biomedical and life sciences research, data management, data wrangling, data integration, machine learning, artificial intelligence

Prof. dr. ir. Liesbet M. Peeters

Multiple Sclerosis
Real-world data
Big data
Data science

+32 (479) 78 67 27


Scientific focus

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:

  • Trajectory analyses and coping with missingness
  • Increasing interpretability of decision-support systems
  • Automate feature extraction from images and time series data
  • Cope with high-dimensional and small real-world datasets (number of features > number of patients)
  • Pre-processing and quality assessment and enhancement of real-world multi-centric time series data
  • Pre-processing and quality assessment and enhancement of real-world multi-centric image data
  • Federated machine learning approaches with a specific focus on registry data
  • AI to speed-up health data infrastructures (data wrangling, data integration and data visualization)

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.

Team Members

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

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.

Sofie Aerts, PhD Student

Sofie recently graduated with a Master in Clinical Biomedical Sciences from Hasselt University (July 2022). During this Master’s degree, she gained experience in managing real-world data (RWD) at the Rehabilitation and MS Centre in Pelt and obtained insights into the current challenges related to research that implements RWD.

In September 2022, she officially joined the research group of Liesbet Peeters, who also represents the co-promotor of her PhD. Her PhD project focusses on the development of a prognostication tool (dashboard) for Multiple Sclerosis to guide treatment strategy. Sofie is used to collaborating closely with clinicians/healthcare providers in a hospital environment. She is thus eager to be part of the bridge between data scientists and the clinic within the research group's projects.

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.

Ilse Vermeulen, MSDA Staff member - focus on Strategy and Operations

Ilse is a Bio-engineer in Cell and Gene Biotechnology and she got her PhD in Medical Sciences in 2012 at the Free University of Brussels (VUB). During her PhD, she mainly worked on prediction models and epidemiological studies in the clinical biology of type I diabetes, which has led to a great deal of experience in data processing as well as in processing and writing results in the form of articles in peer-reviewed journals. The last few years, she has been working as a project manager at the University of Applied Sciences Leuven-Limburg (UCLL) in the centers of expertise "Sustainable Resources" and "Digital Solutions" being responsible for the respective focus lines "Environment & Health" and "Technology Enhanced Care".
Since April 2022, Ilse joined the research group of Liesbet Peeters as a Staff Member to support the MS Data Alliance. She will also get the lead on the follow-up of some other projects within the group, e.g. the ELIXIR-project.
Brenda Hernández Bulnes, MSDA Staff member - focus on International Communications
Brenda is a graduate from the Master's program Globalization and Development Studies of Maastricht University. She has experience in non-governmental organizations and international organizations. This year (2022) she joined the team with great enthusiasm to focus on communication strategies.

Noëlla Pierlet
Noëlla Pierlet got her master in Mathematics at KU Leuven. Later, she finished an ‘individual adapted program’ combining 2 courses ‘Aanvullende opleiding informatica’ and ‘Aanvullende opleiding Toegepaste Informatica’ at KU Leuven, in one year. At UHasselt, she did a ‘postgraduaat bedrijfskunde’.  She now works in Ziekenhuis Oost-Limburg as head of the data science team. Being a translator between data scientists and physicians is her day to day job. With more than 25 years of experience in medical IT, she definitely will be an added value to the team.

Key Publications

  • L. Peeters, T. Parciak, C. Walton, L. Geys, Y. Moreau, E. De Brouwer, et al.: COVID-19 in people with multiple sclerosis: A global data sharing initiative, MSJ, 2020.

  • L. Peeters, T. Parciak, D. Kalra, Y. Moreau, E. Kasilingam, P. van Galen, et al: Multiple Sclerosis Data Alliance - A global multi-stakeholder collaboration to scale-up real world data research, MSRD, 2020.

  • J. Yperman, T. Becker, D. Valkenborg, V. Popescu, N. Hellings, B. Van Wijmeersch, L. M. Peeters: Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis, BMC Neurology, 2020.

  • J. Yperman, T. Becker, D. Valkenborg, N. Hellings, M. Cambron, D. Dive, G. Laureys, V. Popescu, B. Van Wijmeersch, L. M. Peeters: Deciphering the Morphology of Motor Evoked Potentials, Frontiers in Neuroinformatics, 2020.

  • S. Simpson-Yap, E. De Brouwer, et al. L.M. Peeters: Association of Disease-Modifying Therapies with COVID-19 Severity in Multiple Sclerosis. Neurology , 2021

  • E. De Brouwer, et al. L.M. Peeters: Longitudinal machine learning modeling of multiple sclerosis patient trajectories improves predictions of disability progression. Computer Methods and Programs in Biomedicine, 2021
  • L.M. Peeters: FAIR data for next-generation management of multiple sclerosis. Multiple Sclerosis Journal, 2018

Media & Websites

Selected projects

  • ELIXIR Belgium (Human Data): “ELIXIR infrastructure for Data and Services to strengthen Life Sciences Research Flanders” – International Research Infrastructure (IRI) – Flemisch Research Foundation (FWO). The ELIXIR project itself requires little introduction as a major multinational European research infrastructure project in the life sciences/informatics community. Professor Peeters leads the use case multiple sclerosis within the cluster “human data”, where we are constructing a federated infrastructure to connect real-world datasets.

  • Flanders AI Research Program: The Flanders AI Research Program is part of the Impulse Program of the Flemish Ministry of Economy, Science and Innovation. Within this program, professor Peeters acts as the use case lead of the Use Case multiple sclerosis. To date, 14 principle investigators of 6 different research department across 4 universities are involved. The use case team consists of 16 PhD students and 4 PostDocs.

  • Remote Clinical Monitoring Center (RCMC): Recently, the Flemish Ministry of Economy, Science and Innovation approved the Remonte Clinical Monitoring Center Initiative. This initiative aims to develop and implement a modular data-integration, data-analytics and integrated healthcare service platform. Professor Peeters acts as the lead architect of this platform.

  • Multiple Sclerosis Data Alliance (MSDA): The MSDA is a global multi-stakeholder collaboration working to accelerate research insights for innovative care and treatment in people with multiple sclerosis. We do this by raising awareness about the importance of research using real-world MS data, enabling better discovery and access to real-world MS data and promoting trustworthy and transparent practices in the way real-world MS data is used. Stakeholders of over 60 countries across different continents (Europe, Northern America, Latin America, Middle East, Africa, Russia, …) are actively involved. The MSDA is sponsored by a consortium of life science industry partners. Professor Peeters is the founder and chair of this initiative.