Rakhmiddin, R.; Lee, K. Federated Learning for Clinical Event Classification Using Vital Signs Data. Multimodal Technol. Interact.2023, 7, 67.
Rakhmiddin, R.; Lee, K. Federated Learning for Clinical Event Classification Using Vital Signs Data. Multimodal Technol. Interact. 2023, 7, 67.
Rakhmiddin, R.; Lee, K. Federated Learning for Clinical Event Classification Using Vital Signs Data. Multimodal Technol. Interact.2023, 7, 67.
Rakhmiddin, R.; Lee, K. Federated Learning for Clinical Event Classification Using Vital Signs Data. Multimodal Technol. Interact. 2023, 7, 67.
Abstract
Effective healthcare relies on accurate and timely diagnosis; however, obtaining large amounts of training data while maintaining patient privacy remains challenging. This study introduces a novel approach utilizing federated learning (FL) and a cross-device multi-modal model for clin-ical event classification using vital signs data. Our architecture leverages FL to train machine learning models, including Random Forest, AdaBoost, and SGD ensemble model, on vital signs data from a diverse clientele at a Boston hospital (MIMIC-IV dataset). The FL structure preserves patient privacy by training directly on each client's device without transferring sensitive data. The study demonstrates the potential of FL in privacy-preserving clinical event classification, achieving an impressive accuracy of 98.9%. These findings underscore the significance of FL and cross-device ensemble technology in healthcare applications, enabling the analysis of large amounts of sensitive patient data while safeguarding privacy.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
