Traditional Chinese Medicine Experts Research Articles

Result and analysis of guidelines to diagnosis and treatment for distending pain in breasts during menstruation in traditional Chinese medicine

To establish guidelines to diagnosis and treatment for distending pain in breasts during menstruation in traditional Chinese medicine (TCM). With delphi method, survey questionnaires were sent to 40 gynecology experts in TCM and integrated traditional and western medicine, and recovery 35 experts were recoveried which, were analyzed by SPSS 11.5. The experts' activity and professional level was high. The expert's are from all over China and express the typical opinions of all experts in this field. The items in the frame of the guidelines to diagnosis and treatment for distending pain in breasts during menstruation in TCM are generally recognized, but different opinions from the card type, governance, in areas such as prescription standard also exist.

Latent class model based diagnostic system utilizing traditional Chinese medicine for patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) can affect nearly any organ system, and is frequently an evolving disease with varied manifestations. Traditional Chinese medicine (TCM) physicians have identified different SLE patterns that they have difficulty summarizing, but the latent class model helps solve this problem. This study applies the latent class model and disease pattern coding system (B-code) to design a TCM diagnostic expert system. This study gathered 2047 valid records and classified three clusters of main disease patterns. Compared with the experience of the TCM expert, the accuracy rate of the expert system reached 77.47%. The results show that this diagnostic system performed well in identifying the disease patterns of SLE and may be clinically useful for TCM physicians.

Application of Delphi Technique in Identification of Appropriate Screening Questions for Chronic Low Back Pain from Traditional Chinese Medicine Experts' Opinions

The goal of the present study was to obtain a standard list of traditional Chinese medicine (TCM) symptoms and signs for screening chronic low back pain (cLBP) from a group of experts and to assess agreement and consistency among their opinions on the items of a questionnaire. The study design involved three rounds of modified Delphi technique, and it was carried out by 13 experts in orthopedics, massage, and acupuncture working in four hospitals affiliated with Yunnan University of Traditional Chinese Medicine, China. The outcome was measured on the 5-score Likert-scale self-administered checklists. A review of eight textbooks identified 12 pain characteristics, 11 associated factors, and 25 physical and tongue diagnostic expressions as important factors in the TCM diagnosis of cLBP. These 48 diagnostic characteristics were rated by 13 experts as "not important" to "very important" on a scale of 1-5. After three rounds of rating, 13 characteristics were eliminated from the list, with the final numbers for each group being 8, 11, and 16, respectively. Seven items based on Western medicine were also added by the experts. The intra-class correlation (ICC) coefficient for agreement among the experts was 0.2 at the end. Intra-rater, between rounds, consecutive pair-wise median kappa values were 0.53 and 0.66. Analysis of variance using items appearing in all three rounds revealed significant effects of expert and group of symptoms and signs (p < 0.001) and nonsignificant differences among scores of the same expert in the three rounds (p = 0.97). Mean score of physical and tongue expressions was significantly (p < 0.001) lower than that of all other groups of symptoms and signs. Modern TCM experts have de-emphasized the items on physical and tongue expressions and have adopted instead those from Western medicine. Intra-expert agreement across items was low, and each expert tended to stick to her/his original opinions.

KISTCM: knowledge discovery system for traditional Chinese medicine

Objective: Traditional Chinese Medicine (TCM) provides an alternative method for achieving and maintaining good health. Due to the increasing prevalence of TCM and the large volume of TCM data accumulated though thousands of years, there is an urgent need to efficiently and effectively explore this information and its hidden rules with knowledge discovery in database (KDD) techniques. This paper describes the design and development of a knowledge discovery system for TCM as well as the newly proposed KDD techniques integrated in this system. Methods: A novel Knowledge dIscovery System for TCM (KISTCM) is developed by incorporating several data mining techniques, primarily including a medicine dependency relationship discovery algorithm, an efficacy dimension reduction algorithm based on neural networks, a method for exploring the relationships between formulae and syndromes using gene expression programming (GEP), and an approach for discovering the properties in terms of nature, taste and meridian based on the herbal dosage by employing the effect degree function to calculate the effect of each property. Results: Representative experimental cases are used to evaluate the system performance. Encouraging results are obtained, including rules previously unknown to algorithm designers and experiment runners. Experiments demonstrate that KISTCM has powerful knowledge discovery and data analysis capabilities, and is a useful tool for discovering the underlying rules in formulae. Our proposed techniques successfully discover hidden knowledge from TCM data, which is a new direction in knowledge discovery. From TCM experts' perspective, the accuracy of data analysis for KISTCM is an improvement, and these results compare favorably to other existing TCM data mining techniques. The system could be expected to be useful in the practice of TCM, e.g., assisting TCM physicians in prescribing formulae or automatically distinguishing between minister and assistant herbs in a formula.

Computational methods for Traditional Chinese Medicine: A survey

Traditional Chinese Medicine (TCM) has been actively researched through various approaches, including computational techniques. A review on basic elements of TCM is provided to illuminate various challenges and progresses in its study using computational methods. Information on various TCM formulations, in particular resources on databases of TCM formulations and their integration to Western medicine, are analyzed in several facets, such as TCM classifications, types of databases, and mining tools. Aspects of computational TCM diagnosis, namely inspection, auscultation, pulse analysis as well as TCM expert systems are reviewed in term of their benefits and drawbacks. Various approaches on exploring relationships among TCM components and finding genes/proteins relating to TCM symptom complex are also studied. This survey provides a summary on the advance of computational approaches for TCM and will be useful for future knowledge discovery in this area.

Arrhythmic Pulses Detection Using Lempel-Ziv Complexity Analysis

Computerized pulse analysis based on traditional Chinese medicine (TCM) is relatively new in the field of automatic physiological signal analysis and diagnosis. Considerable researches have been done on the automatic classification of pulse patterns according to their features of position and shape, but because arrhythmic pulses are difficult to identify, until now none has been done to automatically identify pulses by their rhythms. This paper proposes a novel approach to the detection of arrhythmic pulses using the Lempel-Ziv complexity analysis. Four parameters, one lemma, and two rules, which are the results of heuristic approach, are presented. This approach is applied on 140 clinic pulses for detecting seven pulse patterns, not only achieving a recognition accuracy of 97.1% as assessed by experts in TCM, but also correctly extracting the periodical unit of the intermittent pulse.

A self-learning expert system for diagnosis in traditional Chinese medicine

A novel self-learning expert system for diagnosis in Traditional Chinese medicine (TCM) was constructed by incorporating several data mining techniques, mainly including an improved hybrid Bayesian network learning algorithm, [email protected]?ve-Bayes classifiers with a novel score-based strategy for feature selection and a method for mining constrained association rules. The data-driven nature distinguished the system from those existing TCM expert systems based on if-then rules to address knowledge elicitation problem. Moreover, the learned knowledge was provided in multiple forms including causal diagram, association rule and reasoning rules derived from classifiers. Finally, five representative cases were diagnosed to evaluate the performance of the system and the encouraging results were obtained. The results show that the prototype system performs well in diagnosis of TCM, and could be expected to be useful in the practice of TCM.