融合语言模型与图神经网络，提升临床预测可解释性。
Patients-like-me: A Variational LM--GNN Framework for Explainable Clinical Prediction
- 用变分期望最大化算法联合训练语言模型与图神经网络
- 在MIMIC-III/IV数据集上超越现有方法，性能提升稳定
- 可找出相似患者作为解释依据，适合医疗决策辅助场景
语言模型（LM）能有效捕捉电子健康记录（EHR）的文本特征，但将患者序列独立编码，缺乏可解释性。图神经网络（GNN）通过建模患者间关系增强可解释性，但依赖高质量的患者表示。本文提出Patients-like-me（PLM）框架，融合局部患者语义与全局队列结构。为高效训练，引入变分期望最大化算法，在监督变分目标下交替更新LM与GNN。在MIMIC-III和MIMIC-IV上的实验表明，PLM持续优于当前最优方法，且在仅使用编码器或解码器型语言模型时均有效。额外计算开销小。同时，通过检索关键相似患者提供参考解释，边缘掩码实验验证排名最高的参考患者对预测影响最大。
原文摘要 · Abstract (English)
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.
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