TCMPR: TCM Prescription Recommendation Based on Subnetwork Term Mapping and Deep Learning.
Traditional Chinese medicine (TCM) has played an indispensable role in clinical diagnosis and treatment. Based on a patient's symptom phenotypes, computation-based prescription recommendation methods can recommend personalized TCM prescription using machine learning and artificial intelligence techn...
| Publicado en: | BioMed Research International pp. 1 - 13 |
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| Autores principales: | , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/17/2022
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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=155315546&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155315546 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/17/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 155315546 155315546 155315546 10.1155/2022/4845726 155315546 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: TCMPR: TCM Prescription Recommendation Based on Subnetwork Term Mapping and Deep Learning. aug: au: Dong, Xin Zheng, Yi Shu, Zixin Chang, Kai Xia, Jianan Zhu, Qiang Zhong, Kunyu Wang, Xinyan Yang, Kuo Zhou, Xuezhong affil: Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China sug: subj: Medicine, Chinese Traditional Machine Learning Artificial Intelligence Drugs, Prescription Human Drug Efficacy Phenotype Data Mining ab: Traditional Chinese medicine (TCM) has played an indispensable role in clinical diagnosis and treatment. Based on a patient's symptom phenotypes, computation-based prescription recommendation methods can recommend personalized TCM prescription using machine learning and artificial intelligence technologies. However, owing to the complexity and individuation of a patient's clinical phenotypes, current prescription recommendation methods cannot obtain good performance. Meanwhile, it is very difficult to conduct effective representation for unrecorded symptom terms in an existing knowledge base. In this study, we proposed a subnetwork-based symptom term mapping method (SSTM) and constructed a SSTM-based TCM prescription recommendation method (termed TCMPR). Our SSTM can extract the subnetwork structure between symptoms from a knowledge network to effectively represent the embedding features of clinical symptom terms (especially the unrecorded terms). The experimental results showed that our method performs better than state-of-the-art methods. In addition, the comprehensive experiments of TCMPR with different hyperparameters (i.e., feature embedding, feature dimension, subnetwork filter threshold, and feature fusion) demonstrate that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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