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Robustly identifying risk and protective factors for this complication across distinct cohorts could lead to improved disease management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>The goal was to identify key prognostic factors for motor fluctuations in PD by using machine learning and exploring their associations in the context of the prior literature.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We applied interpretable machine learning techniques for time\u2010to\u2010event analysis and prediction of motor fluctuations within 4\u2009years in three longitudinal PD cohorts. Prognostic models were cross\u2010validated to identify robust predictors, and the performance, stability, calibration, and utility for clinical decision\u2010making were assessed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Cross\u2010validation analyses suggest the effectiveness of the models in identifying significant baseline predictors. Movement Disorder Society\u2010Unified Parkinson's Disease Rating Scale parts I and II, freezing of gait, axial symptoms, rigidity, and pathogenic\n                      <jats:italic>GBA<\/jats:italic>\n                      and\n                      <jats:italic>LRRK2<\/jats:italic>\n                      variants were positively correlated with motor fluctuations. Conversely, motor fluctuations were inversely associated with tremors and late age of onset of PD. Cross\u2010cohort data integration provides more stable predictions, reducing cohort\u2010specific bias and enhancing robustness. Decision curve and calibration analysis confirms the models' practical utility and alignment of predictions with observed outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Interpretable machine learning models can effectively predict motor fluctuations in PD from baseline clinical data. Cross\u2010cohort data integration increases the stability of selected predictors. Calibration and decision curve analyses confirm the model's reliability and utility for practical clinical applications. \u00a9 2025 The Author(s).\n                      <jats:italic>Movement Disorders<\/jats:italic>\n                      published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mds.30223","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T07:09:11Z","timestamp":1747206551000},"page":"1604-1617","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Interpretable Machine Learning for Cross\u2010Cohort Prediction of Motor Fluctuations in Parkinson's Disease"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7566-1677","authenticated-orcid":false,"given":"Rebecca Ting 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