Heterogeneous information network based clustering for precision traditional Chinese medicine.
Background: Traditional Chinese medicine (TCM) is a highly important complement to modern medicine and is widely practiced in China and in many other countries. The work of Chinese medicine is subject to the two factors of the inheritance and development of clinical experience of famous Chinese medi...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 19; pp. 1 - 13 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
12/19/2019 Supplement 6
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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=140452920&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140452920 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 12/19/2019 Supplement 6 vid: 19 pid: 24147 pub: BioMed Central artinfo: ui: 140452920 140452920 NLM31856802 140452920 10.1186/s12911-019-0963-0 NLM31856802 140452920 ppf: 1 ppct: 12 formats: tig: atl: Heterogeneous information network based clustering for precision traditional Chinese medicine. aug: au: Chen, Xintian Ruan, Chunyang Zhang, Yanchun Chen, Huijuan affil: School of Computer Science, Fudan University, Shanghai, China sug: subj: Medicine, Chinese Traditional Statistics and Numerical Data Information Services Standards Cluster Analysis Reference Books Human Data Mining China Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales ab: Background: Traditional Chinese medicine (TCM) is a highly important complement to modern medicine and is widely practiced in China and in many other countries. The work of Chinese medicine is subject to the two factors of the inheritance and development of clinical experience of famous Chinese medicine practitioners and the difficulty in improving the service capacity of basic Chinese medicine practitioners. Heterogeneous information networks (HINs) are a kind of graphical model for integrating and modeling real-world information. Through HINs, we can integrate and model the large-scale heterogeneous TCM data into structured graph data and use this as a basis for analysis.Methods: Mining categorizations from TCM data is an important task for precision medicine. In this paper, we propose a novel structured learning model to solve the problem of formula regularity, a pivotal task in prescription optimization. We integrate clustering with ranking in a heterogeneous information network.Results: The results from experiments on the Pharmacopoeia of the People's Republic of China (ChP) demonstrate the effectiveness and accuracy of the proposed model for discovering useful categorizations of formulas.Conclusions: We use heterogeneous information networks to model TCM data and propose a TCM-HIN. Combining the heterogeneous graph with the probability graph, we proposed the TCM-Clus algorithm, which combines clustering with ranking and classifies traditional Chinese medicine prescriptions. The results of the categorizations can help Chinese medicine practitioners to make clinical decision. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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