Multi-modal Knowledge-aware Hierarchical Attention Network for Explainable Medical Question Answering

Online healthcare services can offer public ubiquitous access to the medical knowledge, especially with the emergence of medical question answering websites, where patients can get in touch with doctors without going to hospital. Explainability and accuracy are two main concerns for medical question answering. However, existing methods mainly focus on accuracy and cannot provide a good explanation for retrieved medical answers. This paper proposes a novel Multi-Modal Knowledge-aware Hierarchical Attention Network (MKHAN) to effectively exploit multi-modal knowledge graph (MKG) for explainable medical question answering. MKHAN can generate path representation by composing the structural, linguistics, and visual information of entities, and infer the underlying rationale of question-answer interactions by leveraging the sequential dependencies within a path from MKG. Furthermore, a novel hierarchical attention network is proposed to discriminate the salience of paths endowing our model with explainability. We build a large-scale multi-modal medical knowledge graph and two real-world medical question answering datasets, the experimental results demonstrate the superior performance on our approach compared with the state-of-the-art methods.