FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
This addresses privacy and access issues in healthcare by enabling distributed model training, though it is incremental as it builds on existing federated learning methods.
The paper tackled the problem of training machine learning models on distributed electronic health records without moving data, using ICU data from 58 hospitals to predict patient mortality, and found that their Federated-Autonomous Deep Learning method outperforms traditional federated learning.
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.