Federated Learning for Healthcare Informatics

Xu, Jie; Glicksberg, Benjamin S.; Su, Chang; Walker, Peter B.; Bian, Jiang; Wang, Fei

doi:10.1007/s41666-020-00082-4

Cited by 857 publications

(440 citation statements)

References 57 publications

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“…FL is agnostic to the type of the input data. It is capable of analyzing various medical data modalities, from free-text clinical reports to high-dimensional medical images ( Xu and Wang, 2019 ). Brisimi et al.…”

Section: Related Workmentioning

confidence: 99%

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

Yang

et al. 2021

Medical Image Analysis

209

130

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Section: Related Workmentioning

confidence: 99%

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

Yang

et al. 2021

Medical Image Analysis

209

130

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“…In light of patient privacy, federated learning has emerged as a promising strategy, particularly in the context of COVID-19 [ 14 ]. Federated learning allows for the decentralized refinement of independently built machine learning models via the iterative exchange of model parameters with a central aggregator, without sharing raw data.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Vaid¹,

Jaladanki²,

Xu³

et al. 2021

JMIR Med Inform

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150

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Background Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. Objective We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. Methods Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. Results The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. Conclusions The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

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“…Multimodal artificial intelligence (AI) and decentralised machine learning can find patterns in and across very large datasets 7 while also preserving individual privacythrough federated, secure ways-to analyse life data. 8 However, when health and related data are linked at the individual level, a high degree of public trust is needed to use these data effectively. Technologies that bring algorithms to data instead of moving large amounts of sensitive data around might prove useful in this context.…”

Section: Open Life Data To Support Healthy Longevity For Allmentioning

confidence: 99%