Explainable machine learning identifies predictors for postural instability and cognitive impairment in early Parkinson’s Disease
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Parkinson’s disease (PD) exhibits considerable variability in symptom onset, severity, and clinical trajectories, making prediction of individual disease progression a complex challenge. Leveraging large multimodal datasets from two independent cohorts of early PD patients, we examined predictive features for progression to mild cognitive impairment (MCI) and postural instability (PI) using Explainable Boosting Machines (EBM) - an interpretable machine learning (ML) approach. We found comparable predictive performances of EBM models using a minimal clinico-demographic dataset compared to an extended multimodal dataset including volumetric brain imaging and fluid biomarkers. Baseline age and Montréal Cognitive Assessment total score were identified as most important predictive features for both MCI and PI using the minimal dataset, while additional motor, cognitive and imaging features were identified using the extended dataset. Our study highlights the potential of ML predictions of cognitive and motor endpoints of PD progression at the individual level bringing personalized clinical decision-making within reach.
Source: medRxiv
Authors: Benjamin Sailer, Benjamin Roeben, Christoph Kiefer, Lars-Christian Achauer, Roswitha Kemmner, Isabel Wurster, Stefanie Lerche, Benjamin Bender, Tobias Lindig, Claudia Schulte, Christian Deuschle, Christian Erhardt, Thomas Gasser, Kathrin Brockmann, Oliver Kohlbacher, Stephanie Biergans, Inga Liepelt-Scarfone
Language: English
Subfield: Clinical Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Bayer, Sanofi, Biogen, Teva Pharmaceutical Industries, Celgene, Jazz Pharmaceuticals, Neurocrine Biosciences, Verily Life Sciences, Voyager Therapeutics, European Commission, Deutsche Forschungsgemeinschaft, Eberhard Karls Universität Tübingen, Bundesministerium für Bildung und Forschung, Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, Eisai, Servier, H. Lundbeck A/S, Sun Pharma, Genentech, Allergan, Avid Radiopharmaceuticals, UCB Pharma
OpenAlex ID: W7220389231