用图模型挖掘中风后僵直预测中的特征交互，提升早期识别能力
Beyond Feature Importance: Feature Interactions in Predicting Post-Stroke Rigidity with Graph Explainable AI
- 构建图神经网络捕捉临床特征间复杂交互关系
- 在51.9万病历上实现0.75的AUROC，优于传统模型
- 揭示了量表与风险评分间的隐藏关联，适合临床决策参考
本研究针对中风后僵直的早期预测难题，提出基于图结构的可解释人工智能方法。中风后僵直表现为肌张力增高和肌肉僵硬，严重影响患者行动能力和生活质量。尽管发病率高，但早期预测手段仍有限，延误干预时机。研究分析了来自医疗费用与利用项目（HCUP）数据集的51.9万例中风住院记录，其中43%的患者出现僵直症状。对比了逻辑回归、XGBoost、Transformer等传统模型与Graphormer、图注意力网络（GAT）等图模型。图模型能天然建模特征交互，并具备内在或事后可解释性。结果显示，图模型表现更优（AUROC达0.75），识别出美国国立卫生研究院中风量表（NIHSS）和APR-DRG死亡风险评分等关键预测因子，并发现传统模型忽略的特征交互。该研究为图基可解释人工智能在中风预后的应用提供新范式，有望推动早期识别与个性化康复策略制定。
原文摘要 · Abstract (English)
This study addresses the challenge of predicting post-stroke rigidity by emphasizing feature interactions through graph-based explainable AI. Post-stroke rigidity, characterized by increased muscle tone and stiffness, significantly affects survivors' mobility and quality of life. Despite its prevalence, early prediction remains limited, delaying intervention. We analyze 519K stroke hospitalization records from the Healthcare Cost and Utilization Project dataset, where 43% of patients exhibited rigidity. We compare traditional approaches such as Logistic Regression, XGBoost, and Transformer with graph-based models like Graphormer and Graph Attention Network. These graph models inherently capture feature interactions and incorporate intrinsic or post-hoc explainability. Our results show that graph-based methods outperform others (AUROC 0.75), identifying key predictors such as NIH Stroke Scale and APR-DRG mortality risk scores. They also uncover interactions missed by conventional models. This research provides a novel application of graph-based XAI in stroke prognosis, with potential to guide early identification and personalized rehabilitation strategies.
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