Integrating Multimodal Electronic Health Records for Diagnosis Prediction

Abstract

Diagnosis prediction aims to predict the patient's future diagnosis based on their Electronic Health Records (EHRs). Most existing works adopt recurrent neural networks (RNNs) to model the sequential EHR data. However, they mainly utilize medical codes and ignore other useful information such as patients' clinical features and demographics. We proposed a new model called MDP to augment the prediction performance by integrating the multimodal clinical data. MDP learns the clinical feature representation by adjusting the weights of clinical features based on a patient's current health condition and demographics. Also, the clinical feature representation, diagnosis codes representation and the demographic embedding are integrated to perform the prediction task. Experiments on a real-world dataset demonstrate that MDP outperforms the state-of-the-art methods.