  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.
