Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling
Name
journal.pcbi.1009862.pdf
Description
Published version
Size
1.65 MB
Format
Adobe PDF
Checksum (MD5)
aa86d6a33e323d1aad20c46051b282a0
Author(s)
Diamant, Nathaniel
Reinertsen, Erik
Song, Steven
Aguirre, Aaron D
Stultz, Collin M
Batra, Puneet
Date Issued
2022
Journal
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Citation
Diamant, Nathaniel, Reinertsen, Erik, Song, Steven, Aguirre, Aaron D, Stultz, Collin M et al. 2022. "Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling." PLoS Computational Biology, 18 (2).
Version
Final published version
Abstract
Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate the effect of small sample size, we introduce a pre-training approach, Patient Contrastive Learning of Representations (PCLR), which creates latent representations of electrocardiograms (ECGs) from a large number of unlabeled examples using contrastive learning. The resulting representations are expressive, performant, and practical across a wide spectrum of clinical tasks. We develop PCLR using a large health care system with over 3.2 million 12-lead ECGs and demonstrate that training linear models on PCLR representations achieves a 51% performance increase, on average, over six training set sizes and four tasks (sex classification, age regression, and the detection of left ventricular hypertrophy and atrial fibrillation), relative to training neural network models from scratch. We also compared PCLR to three other ECG pre-training approaches (supervised pre-training, unsupervised pre-training with an autoencoder, and pre-training using a contrastive multi ECG-segment approach), and show significant performance benefits in three out of four tasks. We found an average performance benefit of 47% over the other models and an average of a 9% performance benefit compared to best model for each task. We release PCLR to enable others to extract ECG representations at https://github.com/broadinstitute/ml4h/tree/master/model_zoo/PCLR.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Harvard University--MIT Division of Health Sciences and Technology
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/JOURNAL.PCBI.1009862
