用降维方法揭示中医辨证中表里判断的核心作用。
Understanding Clinical Decision-Making in Traditional East Asian Medicine through Dimensionality Reduction: An Empirical Investigation
- 将中医八纲辨证中的表里作为降维关键，提取核心症状信息。
- 表里模式最抽象且通用，能高效连接症状与药方空间。
- 为中医智能诊断系统提供可量化的理论支持。
本研究通过降维视角重新解读传统东亚医学（TEAM）中的辨证过程，聚焦《伤寒论》中的八纲辨证（EPPI）体系，验证表里辨证在诊断与治疗选择中的必要性。研究提出三项假设：表里模式是否包含最多症状信息、是否代表最抽象通用的症状特征、是否有助于准确选方。采用抽象指数、交叉条件泛化性能及决策树回归等量化指标，结果表明表里模式具有最高抽象性与泛化能力，显著提升症状与中药处方之间的映射效率。该研究为理解中医认知机制提供了客观框架，推动传统医学与现代计算方法的融合，对人工智能辅助诊断工具的开发具有重要启示，有助于临床实践、教学与科研的深化。
原文摘要 · Abstract (English)
This study examines the clinical decision-making processes in Traditional East Asian Medicine (TEAM) by reinterpreting pattern identification (PI) through the lens of dimensionality reduction. Focusing on the Eight Principle Pattern Identification (EPPI) system and utilizing empirical data from the Shang-Han-Lun, we explore the necessity and significance of prioritizing the Exterior-Interior pattern in diagnosis and treatment selection. We test three hypotheses: whether the Ext-Int pattern contains the most information about patient symptoms, represents the most abstract and generalizable symptom information, and facilitates the selection of appropriate herbal prescriptions. Employing quantitative measures such as the abstraction index, cross-conditional generalization performance, and decision tree regression, our results demonstrate that the Exterior-Interior pattern represents the most abstract and generalizable symptom information, contributing to the efficient mapping between symptom and herbal prescription spaces. This research provides an objective framework for understanding the cognitive processes underlying TEAM, bridging traditional medical practices with modern computational approaches. The findings offer insights into the development of AI-driven diagnostic tools in TEAM and conventional medicine, with the potential to advance clinical practice, education, and research.
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