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

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.

Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/0101200

References in corpus (4)

• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Federated Learning: Strategies for Improving Communication Efficiency
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain

Cited by in corpus (12)

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• FedML: A Research Library and Benchmark for Federated Machine Learning
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• Privacy-preserving Artificial Intelligence Techniques in Biomedicine
• A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
• Personalized Federated Deep Learning for Pain Estimation From Face Images
• Federated Learning for Smart Healthcare: A Survey
• LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
• Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
• Open Set Medical Diagnosis
• Two-stage Federated Phenotyping and Patient Representation Learning
• Failure Prediction in Production Line Based on Federated Learning: An Empirical Study