Dataset Information

Association Between Acupoint Selection, Target Symptoms, and Traditional Chinese Medicine Diagnosis in Real-Time Clinical Practice in a Comprehensive Cancer Center.

ABSTRACT: Background: Acupuncture intervention in actual practice is rarely reported and may be different from that applied in acupuncture research. Objectives: To review acupuncture practice in an integrative medicine clinic and characterize the association between targeted symptoms, traditional Chinese medicine (TCM) diagnosis, and acupoint selection. Methods: We reviewed outpatient acupuncture records from March 2016 to April 2018. Statistical analyses were applied to characterize referral symptoms and associated TCM diagnosis as well as acupoint selection. Results: The final analysis included 5393 acupuncture records (1264 patients). Twelve TCM diagnosis components were identified in the referral symptoms of pain, neuropathy, xerostomia, and hot flashes. Pain was associated with 78 different TCM diagnoses (combinations of TCM diagnosis components). Total of 217 different acupoints were used in the acupuncture treatments (1739) for neuropathy. The acupoint yintang was used in 73.8% of the visits for neuropathy, yet only in 26.5% (P < .001) of the treatments when patients had a TCM diagnosis of qi deficiency, qi stagnation, and blood stagnation. Similarly, both consistencies and variations were seen in acupoint selection with each targeted symptom and its associated TCM diagnoses. Conclusions: TCM diagnosis was not homogeneous among acupuncture treatments for a single referral symptom. In contrast to most of the research on acupuncture for symptom control, there were considerable variations in acupoint selection among treatments for the same symptom in a clinical setting. Future research is needed to examine the clinical relevance of a fixed intervention structure in acupuncture research and the value of individualized acupuncture treatment.

SUBMITTER: Liu W

PROVIDER: S-EPMC7287401 | biostudies-literature | 2020 Jan-Dec

REPOSITORIES: biostudies-literature

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Association Between Acupoint Selection, Target Symptoms, and Traditional Chinese Medicine Diagnosis in Real-Time Clinical Practice in a Comprehensive Cancer Center.

Liu Wenli W Qdaisat Aiham A Lopez Gabriel G Narayanan Santhosshi S Underwood Susan S Spano Michael M Reddy Akhila A Guo Ying Y Yeung Sai-Ching SC Bruera Eduardo E Cohen Lorenzo L

Integrative cancer therapies 20200101

Background: Acupuncture intervention in actual practice is rarely reported and may be different from that applied in acupuncture research. Objectives: To review acupuncture practice in an integrative medicine clinic and characterize the association between targeted symptoms, traditional Chinese medicine (TCM) diagnosis, and acupoint selection. Methods: We reviewed outpatient acupuncture records from March 2016 to April 2018. Statistical analyses were applied to characterize ...[more]

PMID: 32508209

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Project description:BackgroundIn Traditional Chinese Medicine (TCM), the lip diagnosis is an important diagnostic method which has a long history and is applied widely. The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body. However, the traditional diagnostic approach is mainly based on observation by doctor's nude eyes, which is non-quantitative and subjective. The non-quantitative approach largely depends on the doctor's experience and influences accurate the diagnosis and treatment in TCM. Developing new quantification methods to identify the exact syndrome based on the lip diagnosis of TCM becomes urgent and important. In this paper, we design a computer-assisted classification model to provide an automatic and quantitative approach for the diagnosis of TCM based on the lip images.MethodsA computer-assisted classification method is designed and applied for syndrome diagnosis based on the lip images. Our purpose is to classify the lip images into four groups: deep-red, red, purple and pale. The proposed scheme consists of four steps including the lip image preprocessing, image feature extraction, feature selection and classification. The extracted 84 features contain the lip color space component, texture and moment features. Feature subset selection is performed by using SVM-RFE (Support Vector Machine with recursive feature elimination), mRMR (minimum Redundancy Maximum Relevance) and IG (information gain). Classification model is constructed based on the collected lip image features using multi-class SVM and Weighted multi-class SVM (WSVM). In addition, we compare SVM with k-nearest neighbor (kNN) algorithm, Multiple Asymmetric Partial Least Squares Classifier (MAPLSC) and Naïve Bayes for the diagnosis performance comparison. All displayed faces image have obtained consent from the participants.ResultsA total of 257 lip images are collected for the modeling of lip diagnosis in TCM. The feature selection method SVM-RFE selects 9 important features which are composed of 5 color component features, 3 texture features and 1 moment feature. SVM, MAPLSC, Naïve Bayes, kNN showed better classification results based on the 9 selected features than the results obtained from all the 84 features. The total classification accuracy of the five methods is 84%, 81%, 79% and 81%, 77%, respectively. So SVM achieves the best classification accuracy. The classification accuracy of SVM is 81%, 71%, 89% and 86% on Deep-red, Pale Purple, Red and lip image models, respectively. While with the feature selection algorithm mRMR and IG, the total classification accuracy of WSVM achieves the best classification accuracy. Therefore, the results show that the system can achieve best classification accuracy combined with SVM classifiers and SVM-REF feature selection algorithm.ConclusionsA diagnostic system is proposed, which firstly segments the lip from the original facial image based on the Chan-Vese level set model and Otsu method, then extracts three kinds of features (color space features, Haralick co-occurrence features and Zernike moment features) on the lip image. Meanwhile, SVM-REF is adopted to select the optimal features. Finally, SVM is applied to classify the four classes. Besides, we also compare different feature selection algorithms and classifiers to verify our system. So the developed automatic and quantitative diagnosis system of TCM is effective to distinguish four lip image classes: Deep-red, Purple, Red and Pale. This study puts forward a new method and idea for the quantitative examination on lip diagnosis of TCM, as well as provides a template for objective diagnosis in TCM.

Dataset Information

Association Between Acupoint Selection, Target Symptoms, and Traditional Chinese Medicine Diagnosis in Real-Time Clinical Practice in a Comprehensive Cancer Center.

Publications

Association Between Acupoint Selection, Target Symptoms, and Traditional Chinese Medicine Diagnosis in Real-Time Clinical Practice in a Comprehensive Cancer Center.

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