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Deep-learning-based clinical decision support using structured electronichealth records (EHR) has been an active research area for predicting risks ofmortality and diseases. Meanwhile, large amounts of narrative clinical notesprovide complementary information, but are often not integrated into predictivemodels. In this paper, we provide a novel multimodal transformer to fuseclinical notes and structured EHR data for better prediction of in-hospitalmortality. To improve interpretability, we propose an integrated gradients (IG)method to select important words in clinical notes and discover the criticalstructured EHR features with Shapley values. These important words and clinicalfeatures are visualized to assist with interpretation of the predictionoutcomes. We also investigate the significance of domain adaptive pretrainingand task adaptive fine-tuning on the Clinical BERT, which is used to learn therepresentations of clinical notes. Experiments demonstrated that our modeloutperforms other methods (AUCPR: 0.538, AUCROC: 0.877, F1:0.490).

A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction