Loading…
Federated Inverse Reinforcement Learning for Smart ICUs with Differential Privacy
Clinical decision-making models have been developed to support therapeutic interventions based on medical data from either a single hospital or multiple hospitals. However, models based on multi-hospital data require collaboration among hospitals to integrate local data, which can result in informat...
Saved in:
Published in: | IEEE internet of things journal 2023-11, Vol.10 (21), p.1-1 |
---|---|
Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | Clinical decision-making models have been developed to support therapeutic interventions based on medical data from either a single hospital or multiple hospitals. However, models based on multi-hospital data require collaboration among hospitals to integrate local data, which can result in information leakage and violate patient privacy. To address this challenge, we propose a novel approach that combines federated learning with inverse reinforcement learning to create an efficient medical decision-making support tool while preserving patient privacy. Our approach uses an inverse reinforcement learning algorithm with differential privacy to train a neural network-based agent on local data containing clinician trajectories, which learns a private treatment policy by observing patients' conditions. Additionally, we integrate federated learning into the proposed algorithm to learn a global optimal action policy collaboratively among various smart ICUs, overcoming data limitations at each hospital. We evaluate our approach using real-world medical data and demonstrate that it achieves superior performance in a distributed manner. |
---|---|
ISSN: | 2327-4662 2327-4662 |
DOI: | 10.1109/JIOT.2023.3281347 |