Knowledge-aware representation learning for diagnosis prediction

Diagnosis prediction exploits electronic health records (EHRs) to predict the future diagnoses of patients, further supporting clinical decision making and personalized treatments. However, a patient's EHR is an irregular sequence of visits that contains a large number of medical concepts. The disease progression patterns are closely related to the visits, as well as the contextual knowledge of each visit. The existing diagnosis prediction methods ignore the complex relationships between the visits and the contextual knowledge, and thus cannot achieve satisfactory performance. Therefore, we develop a knowledge-aware representation learning method to comprehensively model these complex relationships. Specifically, we first construct a medical knowledge graph to model the correlations between medical concepts in EHRs, and project the contextual knowledge into the pre-learned vectors. We then devise an enhanced gated recurrent unit (GRU) neural network to extract the long-term intra-relationships between visits, and design a novel knowledge attention module to capture the complex inter-relationships between the visits and the contextual knowledge. Armed with these, we provide a powerful and flexible framework to capture the long-term discriminative disease progression patterns for diagnosis prediction. Intensive experiments are conducted on two real-world EHR datasets. And the model achieves a competitive performance with Code-level Accuracy@20 of 0.7465 and Visit-level Precision@20 of 0.7547 on MIMIC-III, and Code-level Accuracy@20 of 0.9337 and Visit-level Precision@20 of 0.9427 on MIMIC-IV.