Understanding Clinical Decision-Making in Traditional East Asian Medicine through Dimensionality Reduction: An Empirical Investigation

• URL: http://arxiv.org/abs/2409.19531v1
• Date: Sun, 29 Sep 2024 03:28:19 GMT
• Title: Understanding Clinical Decision-Making in Traditional East Asian Medicine through Dimensionality Reduction: An Empirical Investigation
• Authors: Hyojin Bae, Bongsu Kang, Chang-Eop Kim,
• Abstract summary: This study examines the clinical decision-making processes in Traditional East Asian Medicine (TEAM) We focus on the Eight Principle Pattern Identification (EPPI) system and utilize empirical data from the Shang-Han-Lun. 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.
• Score: 5.120567378386615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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