Liesbet Peeters
Prof. Liesbet M. Peeters is the chair of the MS Data Alliance and an Assistant Professor at Data Science Institute and Biomedical Research Institute of Hasselt University (Belgium).
In 2013, she finished her PhD in Bio-Engineering Sciences at the KULeuven in Belgium. Prof. Peeters firmly believes that data saves lives, and she envisions a world where every person receives timely and appropriate medical treatment. Her current research focuses on the use of Big Data in Health and Care with a specific focus and expertise in multiple sclerosis (MS). She investigates and implements new methodologies to overcome the technical and sociological challenges that arise with scaling-up real-world data.
Publications
Fair data for next-generation management of multiple sclerosis
Multidisciplinary data infrastructures in multiple sclerosis: Why they are needed and can be done!
Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis
Deciphering the Morphology of Motor Evoked Potentials
COVID-19 in people with multiple sclerosis: A global data sharing initiative
Multiple Sclerosis Data Alliance - A global multi-stakeholder collaboration to scale-up real world data research
Associations of DMT therapies with COVID-19 severity in multiple sclerosis.
Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression
Changes on the Health Care of People with Multiple Sclerosis from Latin America during the COVID-19 Pandemic
Treg-resistant cytotoxic CD4+ T cells dictate T helper cells in their vicinity: TH17 skewing and modulation of proliferation
Motor evoked potentials for multiple sclerosis: A 2 multiyear follow-up dataset.
The MSDA Catalogue: enabling web-based discovery of metadata from real-world multiple sclerosis data sources
Severity of COVID19 infection among patients with multiple sclerosis treated with interferon-β
Updated results of the COVID-19 in MS Global Data Sharing Initiative: anti-CD20 and other risk factors associated with COVID-19 severity
Safety and effectiveness of cladribine tablets for multiple sclerosis: Results from a single-center real-world cohort
Patient-level dataset to study the effect of COVID-19 in people with Multiple Sclerosis
The journey of data within a Global Data Sharing Initiative: A federated three-layer data analysis pipeline to scale up multiple sclerosis research
Introducing a core dataset for real-world data in multiple sclerosis - recommendations from a global task force
Strategic Oversight Across Real-World Health Data Initiatives in a Complex Health Data Space: A call for collective responsibility