从潜在生物标志物到临床规则：基于嵌入引导的规则挖掘与基于归因的翻译用于可解释表格学习
From Latent Biomarkers to Clinical Rules: Embedding-Guided Rule Mining and Attribution-Based Translation for Interpretable Tabular Learning
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中文总结 AI 辅助
提出一种四步流水线，在FT-Transformer潜在空间挖掘规则并翻译为临床特征，在六个数据集上多数优于原始特征规则，但高性能潜在规则翻译后可能性能下降。
中文摘要 AI 辅助
临床决策支持工具在准确预测伴随可理解解释时最为有用。基于规则的模型提供了透明度，但直接从原始临床测量中推导出的规则可能遗漏由多个变量之间交互作用产生的模式。我们提出一个四步流水线，在FT-Transformer的潜在空间中挖掘决策规则，并将其翻译回可测量的临床特征。能够一致区分患者群体的嵌入维度被视为潜在生物标志物，使用小型决策树挖掘规则，并利用梯度输入显著性和CLS注意力归因翻译选定的规则。我们在六个公共临床和人口健康数据集上、四种嵌入维度下评估该框架。翻译后的规则在六个数据集中的五个上优于原始特征规则，平均AUROC提升范围从0.04到0.23。在心脏病数据集上，嵌入空间规则达到0.98 AUROC，但翻译将其降至0.72，表明高性能潜在规则并不总能由简单的原始特征条件表示。这些结果表明，潜在空间规则发现能够揭示预测模式，同时将其翻译为临床医生可评估的临床可测量特征。
英文摘要
Clinical decision support tools are most useful when accurate predictions are accompanied by understandable explanations. Rule-based models provide transparency, but rules derived directly from raw clinical measurements may miss patterns arising from interactions between multiple variables. We present a four-step pipeline that mines decision rules in the latent space of an FT-Transformer and translates them back into measurable clinical features. Embedding dimensions that consistently separate patient groups are treated as latent biomarkers, rules are mined using small decision trees, and selected rules are translated using gradient-input saliency and CLS attention attribution. We evaluate the framework on six public clinical and population health datasets at four embedding dimensions. Translated rules outperformed raw-feature rules in five of six datasets, with mean AUROC gains ranging from 0.04 to 0.23. On the heart disease dataset, embedding-space rules reached 0.98 AUROC, but translation reduced this to 0.72, showing that high-performing latent rules cannot always be represented by simple raw-feature conditions. These results show that latent-space rule discovery can uncover predictive patterns while translating them into clinically measurable features that can be evaluated by clinicians.
