Stage-Aware Hierarchical Attentive Relational Network for Diagnosis Prediction

Recently, Electronic Health Records (EHR) have become valuable for enhancing medical decision making, as well as online disease detection and monitoring. Meanwhile, deep learning-based methods have achieved great success in health risk prediction and diagnosis prediction based on EHR. Nevertheless, deep learning-based models usually require high volumes of data due to the vast amount of parameters. In addition, a considerable proportion of medical codes appear rarely in the EHR data which poses huge difficulties for clinical applications. Hence, some works propose to adopt medical ontologies to enhance the prediction performance and provide interpretable prediction results. However, these medical ontologies are often small-scale and coarse-grained, most of diagnoses and medical concepts are not included, lacking many diagnoses and medical concepts, let alone various relationships between these concepts. To overcome this limitation, we propose to incorporate existing large-scale medical knowledge graphs (KGs) into diagnosis prediction and devise a Stage-aware Hierarchical Attentive Relational Network, named HAR. Specifically, for each visit, a personalized sub-KG is extracted from the existing medical KG, on which HAR conducts relation-specific message passing and hierarchical message aggregation to refine representations of nodes that correspond to medical codes in visits. HAR takes the specific stage of a patient's disease progression into consideration, which participates in the computation of relation-level and node-level attention. Extensive experiments on two public datasets demonstrate the effectiveness of HAR in improving both the visit-level precision and code-level accuracy of the diagnosis prediction task.