Evidence-based decision support for the clinical practice of acupuncture: Data mining approaches

Meng, Changrong; Zhang, Hong-Lai; Zeng, Lingfeng; Li, Ziping; Huang, Jimmy Xiangji; Liang, Zhaohui

doi:10.1109/bibm.2013.6732669

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…These patterns and examples structure the premise of prescient models that empower experts to deliver new perceptions from existing information. Prescription part is moreover improved with the help of this method [19].…”

Section: Introductionmentioning

confidence: 99%

Medicine prediction based on doctor’s degree: a data mining approach

Arman

Sarker

Shakir

et al. 2022

IJEECS

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<span>The effective use of information mining in profoundly unmistakable fields like e-business, promoting and retail has prompted its application in different enterprises. There is an absence of powerful investigation devices to find concealed connections and patterns in information. This examination paper expects to give a review of ebb and flow systems of learning revelation in databases utilizing information mining strategies that are being used in today’s therapeutic research especially in medicine prediction. Correlation, Chi-square and Euclidean distance feature selections are used to select features and showing the comparison of the result between K-Nearest neighbors, Naïve Bayes, decision tree, artificial neural network. The result uncovers that decision tree beats and sometime Bayesian grouping is having comparative precision as of choice tree. The analysis of performance can be done in such as doctor’s degrees may vary the diseases medicine.</span>

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Section: Introductionmentioning

confidence: 99%

Medicine prediction based on doctor’s degree: a data mining approach

Arman

Sarker

Shakir

et al. 2022

IJEECS

View full text Add to dashboard Cite

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“…Association rules mining (ARM) [14], another common topic, identifies the significant patterns of acupoints combinations and extract the rules underlying those patterns. In addition, ARM was often used to draw the association rule from acupoints combinations [15]. Network analysis, originated from social sciences to mine the essence of social phenomena [16], has also been introduced to explore the relationship between acupoints and symptoms in TCM.…”

Section: Introductionmentioning

confidence: 99%

Selection of Acupoints Combination Based on Pseudo Mutual Information Maximization

Liu

et al. 2020

IEEE Access

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Acupuncture has been used as an alternative and complementary therapy, however the selection of acupoints combination for a specific disease (or symptoms) highly depends on the experience of acupuncturists. The current study aimed to develop a reliable and quantifiable method for selection of acupoints combinations, which may improve treatment efficiency and reproducibility among acupuncturists. In the current study, a number of acupuncture prescriptions for treatment of acute or chronic gastritis had been collected and organized from public databases and literatures. Three types of associations, acupoints-symptoms associations (ASA), acupointsacupoints associations (AAA), and symptoms-symptoms associations (SSA) were defined to delineate their connections. The network constructed by AAA was used to study the synergistic effects among acupoints combination whereas SSA network was used to examine coexistence of symptoms. On the other hand, ASA characterized the empirical association between acupoints and symptoms in the prescription database which may be considered as the mathematical representation of acupoints selection for a particular prescription. Then a novel method namely mutual information screening (MIS) was proposed for screening acupoints combinations based on pseudo mutual information maximization. The selected acupoints combinations of MIS method were verified to be in accordance with the rules of acupoints compatibility based on the theory of acupuncture. Validation of MIS through conditional entropy and significance test of the clinical records suggested the method to be reliable and robust. Furthermore, a ten-fold cross validation were carried out to evaluate the performance of MIS compared with artificial neural network (ANN)-based method. The F1-score of MIS was found to be 0.67, higher than that of the ANN-based method (0.58). The current results provided a reliable and practical tool for symptom-specific acupoints selection, which may help to develop a scientific basis for clinical acupuncture therapy.

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