FedDBM: Federated Digital Biomarker for Detecting Parkinson’s Disease Progress
Bibliographic record
Abstract
Exploring a Digital Bio-Marker (DBM) is challenging for detecting the progress of Parkinson’s Disease (PD) since the unclear disease mechanism. Traditional clinical trials first formulate a DBM hypothesis and then proceed to verify them by control study, the process of which is often limited to clinicians’ expertise and experience. Machine Learning with big data provides an opportunity to automatically discover DBMs, while recent privacy policies, such as GDPR, have created extra obstacles for collecting patient information and conducting multicenter trials. To address this issue, we propose a novel DBM discovery paradigm with federated learning, called FedDBM, which forms a closed loop consisting of model training and post hoc explanation. FedDBM employs a federated split learning to preserve patients’ privacy in a multicenter clinical trial which attempts to build a model that maps signal data to PD progress. Then, a Federated Shapley Additive exPlanations method (Fed-SHAP) is proposed to find those features of vital importance in the well-trained model, known as DBM. The proposed FedDBM was evaluated on four PD typical motor symptoms and the extensive experimental results demonstrated that FedDBM showed comparable performance with SOTA federated learning methods, and the explored DBMs were proved to be more sensitive than current clinical metrics.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".