Patients-like-me：用于可解释临床预测的变分LM-GNN框架

中文摘要 AI 辅助

语言模型（LMs）可为电子健康记录（EHR）提供强大的文本表示，但它们孤立地编码患者序列，可解释性有限。图神经网络（GNN）通过纳入患者间关系并实现参考患者归因来补充LMs，但它们依赖高质量的患者表示。我们提出Patients-like-me（PLM），这是一个统一的LM-GNN框架，将局部患者语义与全局队列结构相结合。为了高效训练PLM，我们引入了一种变分期望最大化算法，该算法在监督变分目标下交替进行LM和GNN更新。在MIMIC-III和MIMIC-IV上的大量实验表明，PLM在所有情况下均优于最先进的方法，其改进可泛化到仅编码器和仅解码器的LM骨干网络。这些增益仅伴随适度的额外计算开销。PLM还通过检索有影响力的相似患者提供参考患者解释，而边掩码实验证实，排名最高的参考对模型预测的影响最大。

英文摘要

Language models (LMs) offer strong textual representations for electronic health records (EHRs), but they encode patient sequences in isolation and provide limited explainability. Graph neural networks (GNNs) complement LMs by incorporating inter-patient relationships and enabling reference-patient attribution, yet they rely on high-quality patient representations. We propose Patients-like-me (PLM), a unified LM--GNN framework that integrates local patient semantics with global cohort structure. To train PLM efficiently, we introduce a Variational Expectation-Maximization algorithm that alternates LM and GNN updates under a supervised variational objective. Extensive experiments on MIMIC-III and MIMIC-IV show that PLM consistently outperforms state-of-the-art methods, with improvements generalizing across encoder-only and decoder-only LM backbones. These gains are achieved with only modest additional computational overhead. PLM also provides reference-patient explanations by retrieving influential similar patients, while edge-masking experiments confirm that the highest-ranked references have the greatest impact on model predictions.

发表机构

• McGill University（麦吉尔大学）
• Université de Montréal（蒙特利尔大学）
• Georgia State University（佐治亚州立大学）
• Ohio University（俄亥俄大学）

机构由 AI 辅助整理，请以论文原文为准。