  Deep learning models have achieved promising disease prediction performance
of the Electronic Health Records (EHR) of patients. However, most models
developed under the I.I.D. hypothesis fail to consider the agnostic
distribution shifts, diminishing the generalization ability of deep learning
models to Out-Of-Distribution (OOD) data. In this setting, spurious statistical
correlations that may change in different environments will be exploited, which
can cause sub-optimal performances of deep learning models. The unstable
correlation between procedures and diagnoses existed in the training
distribution can cause spurious correlation between historical EHR and future
diagnosis. To address this problem, we propose to use a causal representation
learning method called Causal Healthcare Embedding (CHE). CHE aims at
eliminating the spurious statistical relationship by removing the dependencies
between diagnoses and procedures. We introduce the Hilbert-Schmidt Independence
Criterion (HSIC) to measure the degree of independence between the embedded
diagnosis and procedure features. Based on causal view analyses, we perform the
sample weighting technique to get rid of such spurious relationship for the
stable learning of EHR across different environments. Moreover, our proposed
CHE method can be used as a flexible plug-and-play module that can enhance
existing deep learning models on EHR. Extensive experiments on two public
datasets and five state-of-the-art baselines unequivocally show that CHE can
improve the prediction accuracy of deep learning models on out-of-distribution
data by a large margin. In addition, the interpretability study shows that CHE
could successfully leverage causal structures to reflect a more reasonable
contribution of historical records for predictions.
