FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record

TL;DR

This paper introduces FADL, a federated learning approach for healthcare data that trains models across multiple hospitals without data sharing, improving patient mortality prediction accuracy.

Contribution

The paper proposes FADL, a novel hybrid federated learning method that balances global and local model training for distributed electronic health record data.

Findings

FADL outperforms traditional federated learning in mortality prediction.

Balancing global and local training improves model performance.

Distributed training preserves data privacy and enhances accuracy.

Abstract

Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to security, privacy, regulatory, and operational issues. We show, using ICU data from 58 different hospitals, that machine learning models to predict patient mortality can be trained efficiently without moving health data out of their silos using a distributed machine learning strategy. We propose a new method, called Federated-Autonomous Deep Learning (FADL) that trains part of the model using all data sources in a distributed manner and other parts using data from specific data sources. We observed that FADL outperforms traditional federated learning strategy and conclude that balance between global and local training is an important factor to consider when design distributed machine learning methods , especially in healthcare.