Type of the Paper Article

Article
Federated Learning for Clinical Event Classification Using Vital Signs Data
Ruzaliev Rakhmiddin¹, KangYoon Lee¹*

Citation: To be added by editorial staff during production. Academic Editor: Firstname Lastname Received: date Revised: date Accepted: date Published: date Copyright: © 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

¹ Department of Computer Engineering, Gachon University, SongNamSi, 13120 South. Korea
* Correspondence: KangYoon Lee (e-mail: [e-mail])
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 clinical 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.