用超图网络生成可解释的疾病预测，让医生能看懂并干预。
Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction
- 将每位患者建模为独特超图，捕捉疾病间高阶关联。
- 在两个真实数据集上预测准确率超越现有最佳模型。
- 能解释缺失诊断信息，适合临床医生审阅与修正。
电子健康记录（EHR）的快速增长推动了深度学习在医疗预测中的应用。然而，在诊断预测等高风险场景中，模型可解释性至关重要。现有具备内在可解释性的模型通常对每次既往诊断或就诊分配注意力权重，导致解释缺乏灵活性和简洁性。本文提出SHy——一种自解释超图神经网络模型，旨在提供个性化、简洁且忠实的解释，便于临床专家干预。通过将每位患者建模为独特超图并采用消息传递机制，SHy捕捉更高阶的疾病相互作用，并提取独特的时序表型作为个性化解释。同时，该模型通过考虑原始诊断记录中的关键假阴性，缓解了EHR数据不完整性问题。在两个真实世界EHR数据集上的定性案例研究与广泛定量评估表明，SHy在预测性能和可解释性方面均优于现有最先进模型。
原文摘要 · Abstract (English)
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.
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