Health AI Central
Peer-Reviewed Publication
medRxiv2026July 9, 2026Journal Article

Early Prediction of Parkinson's Disease Progression by Integrating Research Cohort and Real-World Data Using Knowledge-Anchored Graph Learning.

Zuoyu Yan1,2, Haoyang Li1,2, Zhe Huang1,2, Manqi Zhou1,2,3, Wei-Ting Wang1,2, Shujun Jiang1,2, Mengying Zhang1,2, Enrique Martinez-Nunez4, Roberta Marongiu5,6,7, Harini Sarva8, Michael S Okun4, Jiayu Zhou9, Chang Su1,2, Fei Wang1,2
1Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, United States.
2Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.
3Department of Computational Biology, Cornell University, Ithaca, NY 14853, United States.
4Fixel Institute for Neurological Diseases, Department of Neurology, University of Florida, Gainesville, FL, United States.
5Department of Neurological Surgery, Weill Cornell Medicine, New York, NY, United States.
6Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, United States.
7Department of Genetic Medicine, Weill Cornell Medicine, New York, NY, United States.
8Parkinson's Disease and Movement Disorders Institute, Department of Neurology, Weill Cornell Medicine, New York, NY, United States.
9School of Information, University of Michigan, Ann Arbor, MI, United States.
PubMed PMC DOI PDF

Abstract

Parkinson's disease (PD) progression is highly heterogeneous. Deeply phenotyped longitudinal research cohorts have enabled characterization of PD progression trajectories. Early prediction of these progression patterns can help us better understand patient disease conditions and manage appropriately. However, the sample sizes of these cohorts are typically too small to build robust early predictor…

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