A privacy-preserving platform oriented medical healthcare and its application in identifying patients with candidemia.

ABSTRACT: Federated learning (FL) has emerged as a significant method for developing machine learning models across multiple devices without centralized data collection. Candidemia, a critical but rare disease in ICUs, poses challenges in early detection and treatment. The goal of this study is to develop a privacy-preserving federated learning framework for predicting candidemia in ICU patients. This approach aims to enhance the accuracy of antifungal drug prescriptions and patient outcomes. This study involved the creation of four predictive FL models for candidemia using data from ICU patients across three hospitals in China. The models were designed to prioritize patient privacy while aggregating learnings across different sites. A unique ensemble feature selection strategy was implemented, comb

SUBMITTER: Yuan S 

PROVIDER: S-EPMC11227531 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature