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arXiv · 2104.04569

Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling

Nathaniel Diamant·Erik Reinertsen·Steven Song·Aaron Aguirre·Collin Stultz·Puneet Batra
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Abstract

Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate this effect of small sample size, we introduce a pre-training approach, Patient Contrastive Learning of Representations (PCLR), which creates latent representations of ECGs from a large number of unlabeled examples. 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 substantial improvements across multiple new tasks when there are fewer than 5,000 labels. We release our model to extract ECG representations at https://github.com/broadinstitute/ml4h/tree/master/model_zoo/PCLR.

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Nathaniel Diamant, Erik Reinertsen, Steven Song, Aaron Aguirre, Collin Stultz, Puneet Batra. 2021-04-09. Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling. https://doi.org/10.1371/journal.pcbi.1009862

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