  Machine Learning (ML) models typically require large-scale, balanced training
data to be robust, generalizable, and effective in the context of healthcare.
This has been a major issue for developing ML models for the
coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced,
particularly within electronic health records (EHR) research. Conventional
approaches in ML use cross-entropy loss (CEL) that often suffers from poor
margin classification. For the first time, we show that contrastive loss (CL)
improves the performance of CEL especially for imbalanced EHR data and the
related COVID-19 analyses. This study has been approved by the Institutional
Review Board at the Icahn School of Medicine at Mount Sinai. We use EHR data
from five hospitals within the Mount Sinai Health System (MSHS) to predict
mortality, intubation, and intensive care unit (ICU) transfer in hospitalized
COVID-19 patients over 24 and 48 hour time windows. We train two sequential
architectures (RNN and RETAIN) using two loss functions (CEL and CL). Models
are tested on full sample data set which contain all available data and
restricted data set to emulate higher class imbalance.CL models consistently
outperform CEL models with the restricted data set on these tasks with
differences ranging from 0.04 to 0.15 for AUPRC and 0.05 to 0.1 for AUROC. For
the restricted sample, only the CL model maintains proper clustering and is
able to identify important features, such as pulse oximetry. CL outperforms CEL
in instances of severe class imbalance, on three EHR outcomes with respect to
three performance metrics: predictive power, clustering, and feature
importance. We believe that the developed CL framework can be expanded and used
for EHR ML work in general.
