Use of Federated Learning for validating and updating privacy-preserving decentralized multi-study prognostic models in Traumatic Brain Injury
Abstract
ABSTRACT: Developing modern clinical prediction models (CPMs) and advanced analytics requires large datasets, often necessitating data from different studies. Privacy regulations may hinder data sharing, especially across countries. Decentralized federated data infrastructures, where data remain in their original location and analyses are run only in a shared, secure environment, may address these challenges. We implemented a privacy-preserving federated learning (FL) infrastructure and evaluated and updated the IMPACT prognostic models for traumatic brain injury (TBI) using 2 studies. A multi-continental federated infrastructure was established between 2 large-scale studies (TRACK-TBI from the United States and CENTER-TBI from Europe and Israel). Three IMPACT prognostic models for post-TBI 6-month mortality and unfavorable outcomes were evaluated, followed by model updates through 2 FL approaches trained across the TRACK-TBI and CENTER-TBI studies. Internal validation, external cross-validation, and sub-study validations were performed. CPMs were evaluated for discrimination and calibration. The federated cohort included 1616 participants (TRACK-TBI: n=441, CENTER-TBI: n=1175). Both FL performed well, with comparable coefficient estimates, AUCs (area under the receiver operating characteristics curve) between 0.77-0.88, and calibrated probabilities. Compared to the original IMPACT and single-study models, both federated models presented similar discrimination (AUC), were…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 1,616 participants or records.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.