  Electronic Health Record (EHR) data has been of tremendous utility in
Artificial Intelligence (AI) for healthcare such as predicting future clinical
events. These tasks, however, often come with many challenges when using
classical machine learning models due to a myriad of factors including class
imbalance and data heterogeneity (i.e., the complex intra-class variances). To
address some of these research gaps, this paper leverages the exciting
contrastive learning framework and proposes a novel contrastive regularized
clinical classification model. The contrastive loss is found to substantially
augment EHR-based prediction: it effectively characterizes the
similar/dissimilar patterns (by its "push-and-pull" form), meanwhile mitigating
the highly skewed class distribution by learning more balanced feature spaces
(as also echoed by recent findings). In particular, when naively exporting the
contrastive learning to the EHR data, one hurdle is in generating positive
samples, since EHR data is not as amendable to data augmentation as image data.
To this end, we have introduced two unique positive sampling strategies
specifically tailored for EHR data: a feature-based positive sampling that
exploits the feature space neighborhood structure to reinforce the feature
learning; and an attribute-based positive sampling that incorporates
pre-generated patient similarity metrics to define the sample proximity. Both
sampling approaches are designed with an awareness of unique high intra-class
variance in EHR data. Our overall framework yields highly competitive
experimental results in predicting the mortality risk on real-world COVID-19
EHR data with a total of 5,712 patients admitted to a large, urban health
system. Specifically, our method reaches a high AUROC prediction score of
0.959, which outperforms other baselines and alternatives: cross-entropy(0.873)
and focal loss(0.931).
