Abstract
Objective: The optimal acupoints for a particular disease can be determined by analysis of diagnosis patterns. The objective of this study was to reveal the association between such patterns and the acupoints prescribed in clinical practice using medical data extracted from case reports. Methods: This study evaluated online virtual diagnoses made by currently practicing Korean medical doctors (N = 80). The doctors were presented with 10 case reports published in Korean medical journals and were asked to diagnose the patients and prescribe acupoints accordingly. A network analysis and the term frequency-inverse document frequency (tf-idf) method were used to analyse and quantify the relationship between diagnosis patterns and prescribed acupoints. Results: The network analysis showed that ST36, LI4, LR3, and SP6 were the most frequently used acupoints across all diagnoses. The tf-idf values showed the acupoints used for specific diseases, such as BL40 for bladder disease and LU9 for lung disease. Conclusions: The associations between diagnosis patterns and prescribed acupoints were identified using an online virtual diagnosis modality. Network and text mining analyses revealed commonly applied and disease-specific acupoints in both qualitative and quantitative terms.
Highlights
Clinical reasoning with respect to acupuncture treatment has long been considered a ‘black box’, dependent on the clinical experience and medical knowledge of individual doctors, which can be neither systemised nor quantified
Acupoints may be prescribed according to certain diagnosis patterns, where such patterns sharing common signs or symptoms can be determined by reference to commonalities in their acupoint prescriptions
Characteristics of Diagnosis Patterns and Acupoints Based on Virtual Diagnoses
Summary
Clinical reasoning with respect to acupuncture treatment has long been considered a ‘black box’, dependent on the clinical experience and medical knowledge of individual doctors, which can be neither systemised nor quantified. The diversity in approaches is the result of the particular decision-making requirements for acupuncture treatment, which include assembling clinical data and identifying diagnosis patterns to ensure the use of appropriate acupoints [1]. Pattern identification, which is a traditional approach to diagnostic classification, can inform treatment decisions via synthesis and analysis of clinical information [2]. Different diseases (and indications) can be treated using different acupoints; individual acupoints can be applied to a wide range of indications [3,4]. Each acupoint along the different meridians showed different constellation patterns at various disease sites [5]. A review of the clinical literature on acupoint selection, and the underlying rules and patterns involved therein, showed that several different principles may apply in the treatment of a single disease [6]. Acupoints may be prescribed according to certain diagnosis patterns, where such patterns sharing common signs or symptoms can be determined by reference to commonalities in their acupoint prescriptions
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