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

By Dianbo Liu, Timothy Miller, Raheel Sayeed, Kenneth D. Mandl. 2018

68 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Information Management

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Citations in the corpus

FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “…computer vision [77, 78], natural language processing [79, 80, 81, 82, 83], ﬁnance [74, 35, 84], transportation [85, 86, 87, 88, 89, 90, 91, 92, 93, 94], digital health [95, 96, 97, 98, 99, 100, 101], recommendation [102, 103, 104, 105, 106, 107], robotics [108, 109], and smart cities [110, 111].”
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond (2021, background): “FL is applied in [10] to address these issues.”
Federated Learning for Smart Healthcare: A Survey (2021, methodology): “A federated HER management solution is also considered in [83], where 58 different hospitals are employed to collaborate the prediction of patient mortality without”
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, background): “The ﬁrst study to present the privacy issues of server use in healthcare is [160].”
Two-stage Federated Phenotyping and Patient Representation Learning (2019, methodology): “The strategy of federated learning has been recently adopted in the medical field in structured databased machine learning tasks (Liu et al., 2018a; Huang et al., 2018; Liu et al., 2018b).”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, background): “The authors in [111] also build a federated NN training framework that allows each hospital participates in learning part of the model using its EHRs data source.”
Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, background): “[25] develop a distributed two-layer neural network for mortality prediction based on EHR data, in which the bottom layer is private to each participant and the top layer is public and trained using the aggregation of anonymous parameters from local models.”