A generalizable physiological model for detection of Delayed Cerebral Ischemia using Federated Learning.

ABSTRACT: Delayed cerebral ischemia (DCI) is a complication seen in patients with subarachnoid hemorrhage stroke. It is a major predictor of poor outcomes and is detected late. Machine learning models are shown to be useful for early detection, however training such models suffers from small sample sizes due to rarity of the condition. Here we propose a Federated Learning approach to train a DCI classifier across three institutions to overcome challenges of sharing data across hospitals. We developed a framework for federated feature selection and built a federated ensemble classifier. We compared the performance of FL model to that obtained by training separate models at each site. FL significantly improved performance at only two sites. We found that this was due to feature distribution difference

SUBMITTER: Elhussein A 

PROVIDER: S-EPMC10883332 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature