Federated Learning for Clinical Event Classification Using Vital Signs Data
Ruzaliev Rakhmiddin, Kang Yoon Lee
Abstract
Accurate and timely diagnosis is a pillar of effective healthcare. However, the challenge lies in gathering extensive training data while maintaining patient privacy. This study introduces a novel approach using federated learning (FL) and a cross-device multimodal model for clinical event classification based on vital signs data. Our architecture employs FL to train several machine learning models including random forest, AdaBoost, and SGD ensemble models on vital signs data. The data were sourced from a diverse clientele at a Boston hospital (MIMIC-IV dataset). The FL structure trains directly on each client’s device, ensuring no transfer of sensitive data and preserving patient privacy. The study demonstrates that FL offers a powerful tool for privacy-preserving clinical event classification, with our approach achieving an impressive accuracy of 98.9%. These findings highlight the significant potential of FL and cross-device ensemble technology in healthcare applications, especially in the context of handling large volumes of sensitive patient data.