Heterogeneous information network based clustering for precision traditional Chinese medicine
Abstract 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 Chin...
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doaj-54e13c184a6c4596bc521f168a7285872020-12-20T12:35:10ZengBMCBMC Medical Informatics and Decision Making1472-69472019-12-0119S611210.1186/s12911-019-0963-0Heterogeneous information network based clustering for precision traditional Chinese medicineXintian Chen0Chunyang Ruan1Yanchun Zhang2Huijuan Chen3School of Computer Science, Fudan UniversitySchool of Computer Science, Fudan UniversityCollege of Engineering and Science, Victoria UniversitySchool of Basic Medical Science, Shanghai University of Traditional Chinese MedicineAbstract 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.https://doi.org/10.1186/s12911-019-0963-0TCMFormulaHeterogeneous Information networkClusteringRanking |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xintian Chen Chunyang Ruan Yanchun Zhang Huijuan Chen |
spellingShingle |
Xintian Chen Chunyang Ruan Yanchun Zhang Huijuan Chen Heterogeneous information network based clustering for precision traditional Chinese medicine BMC Medical Informatics and Decision Making TCM Formula Heterogeneous Information network Clustering Ranking |
author_facet |
Xintian Chen Chunyang Ruan Yanchun Zhang Huijuan Chen |
author_sort |
Xintian Chen |
title |
Heterogeneous information network based clustering for precision traditional Chinese medicine |
title_short |
Heterogeneous information network based clustering for precision traditional Chinese medicine |
title_full |
Heterogeneous information network based clustering for precision traditional Chinese medicine |
title_fullStr |
Heterogeneous information network based clustering for precision traditional Chinese medicine |
title_full_unstemmed |
Heterogeneous information network based clustering for precision traditional Chinese medicine |
title_sort |
heterogeneous information network based clustering for precision traditional chinese medicine |
publisher |
BMC |
series |
BMC Medical Informatics and Decision Making |
issn |
1472-6947 |
publishDate |
2019-12-01 |
description |
Abstract 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. |
topic |
TCM Formula Heterogeneous Information network Clustering Ranking |
url |
https://doi.org/10.1186/s12911-019-0963-0 |
work_keys_str_mv |
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1724376368832053248 |