Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction
Journal:
arXiv
Published Date:
Feb 15, 2025
Abstract
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.
