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

Contact:
+32 (479) 78 67 27
liesbet.peeters@uhasselt.be

Linkedin
Orcid
#DataSavesLives
#MSCOVID19

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 Geys has a background in biomedical sciences. She defended her PhD in 2017 at the University of Leuven and started working as clinical data manager afterwards. Since December 2019, she is working for the research group of Biomedical Data Sciences under supervision of Prof. Liesbet Peeters where she is currently employed as postdoctoral researcher.

Lotte has a bridgebuilding role between the worlds of biomedical sciences and data sciences and has expertise in governance and contract management when it comes to reuse of real-world health data for research.

contact: lotte.geys@uhasselt.be

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.

contact: ashkan.pirmani@uhasselt.be

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.

contact: Tina.parciak@uhasselt.be

Hamza Khan, PhD Student

Hamza Khan completed his Bachelor's in Dental Surgery before pursuing a Master's degree in Public Health (MPH) in 2019. During his internship at the International Organization for Migration (IOM - Regional Office for EU/EEA and NATO, Brussels), he gained insight into the importance of data and robust statistical methods/machine learning in healthcare research.

After his experience at IOM, Hamza pursued a Master's degree in Cognitive Science and Artificial Intelligence (MCSAI) at Tilburg University, The Netherlands, which he completed in August 2020. He has since embarked on an interdisciplinary PhD at BioMed UHasselt and Department of Precision Medicine at UMaastricht, where he is researching biomarkers for disease progression in multiple sclerosis, as well as other areas such as data anonymization.

contact: hamza.khan@uhasselt.be

Marcel Parciak, PhD Student

Marcel finished his applied computer science studies in 2017, earning a master's degree. Afterwards, he worked as a research associate at the medical data integration center of the university medical center Göttingen. During this time, he developed IT-infrastructures and worked as a health data engineer.

He joined the team in 2021 to develop AI methods and tools that accelerate the set-up of health data integration infrastructures, enabling clinicians and researchers to get the right data in the right format at the right place at the right time. Marcel's research interests include AI, data integration and health data science. He is an open-source enthusiast who aims to create open, collaborative, 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 is a Master's graduate in Clinical Biomedical Sciences from Hasselt University. During her Master's education, she gained experience managing real-world data (RWD) at the University MS

Centre Hasselt-Pelt and acquired insights into the current challenges related to it.

Since September 2022 Sofie joined the research group of Prof. Liesbet M. Peeters, who is also to co-promotor of her PhD. Sofie's PhD project is focused on developing a prognostication tool to assist neurologists in determining the prognosis and appropriate treatment strategy for people with Multiple Sclerosis. Her project involves both qualitative and quantitative research methods to develop a dashboard that can be implemented in the clinic. 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

Dr. ir. Ilse Vermeulen is a Bio-engineer in Cell and Gene Biotechnology with a PhD in Medical Sciences from the Free University of Brussels (VUB), which she obtained in 2012. During her doctoral studies, Ilse focused on prediction models and epidemiological studies in the clinical biology of type I diabetes, which provided her with extensive experience in data processing and writing research articles for peer-reviewed journals.

For the past few years, Ilse has been working as a project manager at the University of Applied Sciences Leuven-Limburg (UCLL), where she was responsible for the respective focus lines "Environment & Health" and "Technology Enhanced Care".

In April 2022, Ilse joined the Research Group of Biomedical Data Sciences of Liesbet Peeters as a Staff Member and Project Manager to support the MS Data Alliance. Additionally, she leads the follow-up of other projects within the group, e.g. EBRAINS. Ilse is a vigorous creator, project enabler, and adept at transforming real-world data into real-world evidence.

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 is the Head of the Data Science team at ‘Ziekenhuis Oost-Limburg’ in Genk. She has over 25 years of experience in medical IT: she started as a system administrator, changed to software developer, project lead, and finally a data scientist. One of her specialties is bridging the gap between physicians and engineers. She is a pro at translating complex medical jargon into language that technical people can understand and vice versa.

Noëlla firmly believes that qualitative, structured data is the foundation for improving patient care. Her passion for using data to drive change and enhance healthcare has led her to pursue a PhD within the biomedical data sciences research group where she researches how to improve data quality in real world data.

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

Website MS Data Alliance
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
Kunnen jouw medische gegevens iemands leven redden? (Universiteit van Vlaanderen)

Hoe maak je data in de zorg FAIR? - te beluisteren via:

Happy Aging Day 2019

Reportage - Kanaal Z

Global Data Sharing Initiative - Reflections - Nov 2020

Video - Global Data Sharing Initiative

Limburgse leidt wereldwijd onderzoek naar impact van corona op MS-patiënten

VSC Industry Road Tour | HPC meets Health | Liesbet Peeters

EHDEN podcast “A rising star in the RWD/RWE domain, Prof Liesbet Peeters, her journey in addressing frustrations in working with data, and answering key questions in Multiple Sclerosis”

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.

Research group in Biomedical Data Sciences