Bridging the Gaps: Imputation of Parkinson's Disease Clinical Assessments With Federated Learning

Routine clinical assessments for Parkinson's disease are essential instruments in both clinical practice and research, often used to identify disease sub-types and monitor the progression of disease severity. However, each clinic has limited access to information and the quality of these assessments is often degraded by the amount of missing information recorded at the time of each visit. The main objective of this study is to evaluate the performance of Federated Learning (FL) algorithms for imputing missing clinical data, enhancing the quality of decentralized Parkinson's disease assessments while maintaining data privacy. Specifically, we explore the impact of various aggregation strategies on the imputation of clinical data from 1,370 patients in the Parkinson Progression Marker Initiative (PPMI). Notably, the Cyclic Weight Transfer (CWT) algorithm stands out for its lower imputation errors. To validate this study, we conducted a downstream analysis using imputed data to predict symptoms progression. We observed that a FL-based approach yields superior model performance based on imputation errors, when compared to traditional learning strategies. These improvements can achieve 37.7% and 31.5% lower mean imputation errors with low and moderate degree of missing scores in the training data, respectively. In addition, we achieved better classification scores with Random Forest models trained with imputed data from FL-based approaches, compared to traditional statistical methods, with improvements of 0.5% in PR-AUC, 0.6% in ROC-AUC, and 1.3% in F-1 score. These results highlight FL as a robust and secure solution for decentralized clinical data management, offering improved performance while preserving patient privacy.