  With the successful adoption of machine learning on electronic health records
(EHRs), numerous computational models have been deployed to address a variety
of clinical problems. However, due to the heterogeneity of EHRs, models trained
on different patient groups suffer from poor generalizability. How to mitigate
domain shifts between the source patient group where the model is built upon
and the target one where the model will be deployed becomes a critical issue.
In this paper, we propose a data augmentation method to facilitate domain
adaptation, which leverages knowledge from the source patient group when
training model on the target one. Specifically, adversarially generated samples
are used during domain adaptation to fill the generalization gap between the
two patient groups. The proposed method is evaluated by a case study on
different predictive modeling tasks on MIMIC-III EHR dataset. Results confirm
the effectiveness of our method and the generality on different tasks.
