Personalized Federated Graph Learning On Non-IID Electronic Health Records

Author

Tao Tang
Zhuoyang Han
Zhen Cai
Shuo Yu
Xiaokang Zhou
Taiwo Oseni
Sajal K. Das, Missouri University of Science and TechnologyFollow

Abstract

Understanding The Latent Disease Patterns Embedded In Electronic Health Records (EHRs) Is Crucial For Making Precise And Proactive Healthcare Decisions. Federated Graph Learning-Based Methods Are Commonly Employed To Extract Complex Disease Patterns From The Distributed EHRs Without Sharing The Client-Side Raw Data. However, The Intrinsic Characteristics Of The Distributed EHRs Are Typically Non-Independent And Identically Distributed (Non-IID), Significantly Bringing Challenges Related To Data Imbalance And Leading To A Notable Decrease In The Effectiveness Of Making Healthcare Decisions Derived From The Global Model. To Address These Challenges, We Introduce A Novel Personalized Federated Learning Framework Named PEARL, Which Is Designed For Disease Prediction On Non-IID EHRs. Specifically, PEARL Incorporates Disease Diagnostic Code Attention And Admission Record Attention To Extract Patient Embeddings From All EHRs. Then, PEARL Integrates Self-Supervised Learning Into A Federated Learning Framework To Train A Global Model For Hierarchical Disease Prediction. To Improve The Performance Of The Client Model, We Further Introduce A Fine-Tuning Scheme To Personalize The Global Model Using Local EHRs. During The Global Model Updating Process, A Differential Privacy (DP) Scheme Is Implemented, Providing A High-Level Privacy Guarantee. Extensive Experiments Conducted On The Real-World MIMIC-III Dataset Validate The Effectiveness Of PEARL, Demonstrating Competitive Results When Compared With Baselines.

Recommended Citation

T. Tang et al., "Personalized Federated Graph Learning On Non-IID Electronic Health Records," IEEE Transactions on Neural Networks and Learning Systems, Institute of Electrical and Electronics Engineers, Jan 2024.

The definitive version is available at https://doi.org/10.1109/TNNLS.2024.3370297

Department(s)

Computer Science

Publication Status

Early Access

Keywords and Phrases

Adaptation models; Data models; Disease prediction; Diseases; electronic health record (EHR); Federated learning; graph neural network (GNN); non-independent and identically distributed (Non-IID) data; personalized federated learning; Predictive models; Task analysis; Training

International Standard Serial Number (ISSN)

2162-2388; 2162-237X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2024