用于诊断预测的自解释超图神经网络
Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction
AI总结：
本文提出自解释超图神经网络模型SHy，通过将患者建模为超图并利用消息传递机制捕获高阶疾病交互与时间表型，提供个性化且可干预的解释，在真实数据集上性能优于现有模型。
摘要：
电子健康记录（EHRs）的迅猛增长使深度学习模型在预测性医疗领域表现卓越。然而，对于诊断预测等高风险应用，模型的可解释性依然至关重要。现有的具有内在可解释性的深度学习诊断预测模型通常为每次过往诊断或就诊分配注意力权重，导致解释缺乏灵活性与简洁性。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，并允许临床专家进行干预。通过将每位患者建模为独特的超图并采用消息传递机制，SHy捕获高阶疾病交互并提取独特的时间表型作为个性化解释。它还通过考虑原始诊断记录中关键的假阴性，解决了EHR数据的不完整性问题。在两个真实世界EHR数据集上的定性案例研究和广泛定量评估表明，SHy在预测性能和可解释性上均优于现有最先进模型。
英文摘要：
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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