  Predicting the risk of in-hospital mortality from electronic health records
(EHRs) has received considerable attention. Such predictions will provide early
warning of a patient's health condition to healthcare professionals so that
timely interventions can be taken. This prediction task is challenging since
EHR data are intrinsically irregular, with not only many missing values but
also varying time intervals between medical records. Existing approaches focus
on exploiting the variable correlations in patient medical records to impute
missing values and establishing time-decay mechanisms to deal with such
irregularity. This paper presents a novel contrastive learning-based
imputation-prediction network for predicting in-hospital mortality risks using
EHR data. Our approach introduces graph analysis-based patient stratification
modeling in the imputation process to group similar patients. This allows
information of similar patients only to be used, in addition to personal
contextual information, for missing value imputation. Moreover, our approach
can integrate contrastive learning into the proposed network architecture to
enhance patient representation learning and predictive performance on the
classification task. Experiments on two real-world EHR datasets show that our
approach outperforms the state-of-the-art approaches in both imputation and
prediction tasks.
