Bridging the Gaps: Imputation of Parkinson’s Disease Clinical Assessments With Federated Learning
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".