<ul><li>This study focuses on predicting post-stroke rigidity using graph-based explainable AI.</li><li>Graph-based models like Graphormer and Graph Attention Network outperform traditional approaches.</li><li>Key predictors such as NIH Stroke Scale and APR-DRG mortality risk scores are identified.</li><li>Graph-based XAI has the potential to guide early identification and personalized rehabilitation strategies.</li></ul>

Beyond Feature Importance: Feature Interactions in Predicting Post-Stroke Rigidity with Graph Explainable AI

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