用时间特征交叉注意力机制，让医疗预测模型既准又透明。
No Black Box Anymore: Demystifying Clinical Predictive Modeling with Temporal-Feature Cross Attention Mechanism
- 引入时间-特征交叉注意力，捕捉临床指标随时间的动态交互。
- 在1422名慢性肾病患者上，准确率AUC达0.95，F1值0.69。
- 可识别关键时间窗口、特征重要性及跨时互动关系，适合临床决策支持。
尽管深度学习在临床预测中表现优异，但可解释性仍是重大挑战。受Transformer启发，我们提出时间-特征交叉注意力机制（TFCAM），一种新型深度学习框架，用于捕捉临床特征随时间的动态交互，同时提升预测准确率与可解释性。在包含1,422名慢性肾病患者的实验中，该模型预测终末期肾病进展的表现优于LSTM和RETAIN基线，达到0.95的AUROC和0.69的F1-score。TFCAM不仅性能优越，还提供多层次可解释性：识别关键时间阶段、排序特征重要性，并量化特征间跨时间的相互影响。该方法有效缓解了深度学习在医疗领域的“黑箱”问题，为临床医生提供透明的疾病进展洞察，同时保持前沿预测性能。
原文摘要 · Abstract (English)
Despite the outstanding performance of deep learning models in clinical prediction tasks, explainability remains a significant challenge. Inspired by transformer architectures, we introduce the Temporal-Feature Cross Attention Mechanism (TFCAM), a novel deep learning framework designed to capture dynamic interactions among clinical features across time, enhancing both predictive accuracy and interpretability. In an experiment with 1,422 patients with Chronic Kidney Disease, predicting progression to End-Stage Renal Disease, TFCAM outperformed LSTM and RETAIN baselines, achieving an AUROC of 0.95 and an F1-score of 0.69. Beyond performance gains, TFCAM provides multi-level explainability by identifying critical temporal periods, ranking feature importance, and quantifying how features influence each other across time before affecting predictions. Our approach addresses the "black box" limitations of deep learning in healthcare, offering clinicians transparent insights into disease progression mechanisms while maintaining state-of-the-art predictive performance.
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