Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review.
Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive an...
| Publicado en: | American Journal of Chinese Medicine Vol. 50; no. 1; pp. 91 - 132 |
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| Autores principales: | , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
World Scientific Publishing Company
2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155358301&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155358301 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0192415X DYP jtl: American Journal of Chinese Medicine issn: 0192415X maglogo: N pubinfo: dt: 2022 vid: 50 iid: 1 pid: 2051 pub: World Scientific Publishing Company place: Hackensack, New Jersey artinfo: ui: 155358301 154211261 155358301 155358301 10.1142/S0192415X22500045 155358301 ppf: 91 ppct: 41 formats: tig: atl: Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review. aug: au: Chen, Haiyang He, Yu affil: School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China sug: subj: Machine Learning Classification Medicine, Chinese Traditional Human Systematic Review Funding Source Regression Methods Cluster Analysis Methods Support Vector Machine Logistic Regression Neural Networks (Computer) Decision Trees Random Forest Factor Analysis Methods Discriminant Analysis Methods Disease Diagnosis Syndrome Diagnosis Prescriptions, Drug Evaluation Research, Alternative Therapies Medicine, Herbal Drugs Pharmacodynamics Quality Control (Technology) Drugs Pharmacokinetics ab: Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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