Interpretable Machine Learning for Cross‐Cohort Prediction of Motor Fluctuations in Parkinson's Disease
Interpretable machine learning models can effectively predict motor fluctuations in PD from baseline clinical data. Cross-cohort data integration increases the stability of selected predictors. Calibration and decision curve analyses confirm the model's reliability and utility for practical clinical applications. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Authors
- Jean‐Christophe Corvol (ORCID: https://orcid.org/0000-0001-7325-0199)
- Rebecca Ting Jiin Loo (ORCID: https://orcid.org/0000-0002-7566-1677)
- Marie Vidailhet (ORCID: https://orcid.org/0000-0002-2409-9143)
- Lukas Pavelka (ORCID: https://orcid.org/0000-0002-7721-3317)
- Enrico Glaab (ORCID: https://orcid.org/0000-0003-3977-7469)
- Graziella Mangone (ORCID: https://orcid.org/0000-0002-2847-3067)
- Rejko Krueger (ORCID: https://orcid.org/0000-0003-4258-6241)
- Fouad Khoury (ORCID: https://orcid.org/0009-0009-8423-6865)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Centre Hospitalier de Luxembourg (LU)
- University of Luxembourg (LU)
- Sorbonne Université (FR)
- Luxembourg Institute of Health (LU)
- Institut du Cerveau (FR)
Publication Details
- Journal
- Movement Disorders
- Published
- 2025-05-14
- DOI
- https://doi.org/10.1002/mds.30223
- Citations
- 2
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 2.78
Funders
- Université du Luxembourg
- EU Joint Programme – Neurodegenerative Disease Research
- Agence Nationale de la Recherche
- Fonds National de la Recherche Luxembourg
- Association France Parkinson