Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction
This work addresses the need for interpretable AI in high-stakes healthcare applications like diagnosis prediction, offering a method to improve trust and allow expert interventions, though it is incremental in enhancing existing interpretability approaches.
The paper tackles the problem of interpretability in deep learning models for diagnosis prediction from electronic health records by introducing SHy, a self-explaining hypergraph neural network that provides personalized and concise explanations. It demonstrates superior predictive performance and interpretability over state-of-the-art models on two real-world EHR datasets.
The burgeoning volume of electronic health records (EHRs) has enabled deep learning models to excel in predictive healthcare. However, for high-stakes applications such as diagnosis prediction, model interpretability remains paramount. Existing deep learning diagnosis prediction models with intrinsic interpretability often assign attention weights to every past diagnosis or hospital visit, providing explanations lacking flexibility and succinctness. In this paper, we introduce SHy, a self-explaining hypergraph neural network model, designed to offer personalized, concise and faithful explanations that allow for interventions from clinical experts. By modeling each patient as a unique hypergraph and employing a message-passing mechanism, SHy captures higher-order disease interactions and extracts distinct temporal phenotypes as personalized explanations. It also addresses the incompleteness of the EHR data by accounting for essential false negatives in the original diagnosis record. A qualitative case study and extensive quantitative evaluations on two real-world EHR datasets demonstrate the superior predictive performance and interpretability of SHy over existing state-of-the-art models.