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To Set Up a Logistic Regression Prediction Model for Hepatotoxicity of Chinese Herbal Medicines Based on Traditional Chinese Medicine Theory
Aims. To establish a logistic regression (LR) prediction model for hepatotoxicity of Chinese herbal medicines (HMs) based on traditional Chinese medicine (TCM) theory and to provide a statistical basis for predicting hepatotoxicity of HMs. Methods. The correlations of hepatotoxic and nonhepatotoxic...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5021893/ https://www.ncbi.nlm.nih.gov/pubmed/27656240 http://dx.doi.org/10.1155/2016/7273940 |
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author | Liu, Hongjie Li, Tianhao Chen, Lingxiu Zhan, Sha Pan, Meilan Ma, Zhiguo Li, Chenghua Zhang, Zhe |
author_facet | Liu, Hongjie Li, Tianhao Chen, Lingxiu Zhan, Sha Pan, Meilan Ma, Zhiguo Li, Chenghua Zhang, Zhe |
author_sort | Liu, Hongjie |
collection | PubMed |
description | Aims. To establish a logistic regression (LR) prediction model for hepatotoxicity of Chinese herbal medicines (HMs) based on traditional Chinese medicine (TCM) theory and to provide a statistical basis for predicting hepatotoxicity of HMs. Methods. The correlations of hepatotoxic and nonhepatotoxic Chinese HMs with four properties, five flavors, and channel tropism were analyzed with chi-square test for two-way unordered categorical data. LR prediction model was established and the accuracy of the prediction by this model was evaluated. Results. The hepatotoxic and nonhepatotoxic Chinese HMs were related with four properties (p < 0.05), and the coefficient was 0.178 (p < 0.05); also they were related with five flavors (p < 0.05), and the coefficient was 0.145 (p < 0.05); they were not related with channel tropism (p > 0.05). There were totally 12 variables from four properties and five flavors for the LR. Four variables, warm and neutral of the four properties and pungent and salty of five flavors, were selected to establish the LR prediction model, with the cutoff value being 0.204. Conclusions. Warm and neutral of the four properties and pungent and salty of five flavors were the variables to affect the hepatotoxicity. Based on such results, the established LR prediction model had some predictive power for hepatotoxicity of Chinese HMs. |
format | Online Article Text |
id | pubmed-5021893 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-50218932016-09-21 To Set Up a Logistic Regression Prediction Model for Hepatotoxicity of Chinese Herbal Medicines Based on Traditional Chinese Medicine Theory Liu, Hongjie Li, Tianhao Chen, Lingxiu Zhan, Sha Pan, Meilan Ma, Zhiguo Li, Chenghua Zhang, Zhe Evid Based Complement Alternat Med Research Article Aims. To establish a logistic regression (LR) prediction model for hepatotoxicity of Chinese herbal medicines (HMs) based on traditional Chinese medicine (TCM) theory and to provide a statistical basis for predicting hepatotoxicity of HMs. Methods. The correlations of hepatotoxic and nonhepatotoxic Chinese HMs with four properties, five flavors, and channel tropism were analyzed with chi-square test for two-way unordered categorical data. LR prediction model was established and the accuracy of the prediction by this model was evaluated. Results. The hepatotoxic and nonhepatotoxic Chinese HMs were related with four properties (p < 0.05), and the coefficient was 0.178 (p < 0.05); also they were related with five flavors (p < 0.05), and the coefficient was 0.145 (p < 0.05); they were not related with channel tropism (p > 0.05). There were totally 12 variables from four properties and five flavors for the LR. Four variables, warm and neutral of the four properties and pungent and salty of five flavors, were selected to establish the LR prediction model, with the cutoff value being 0.204. Conclusions. Warm and neutral of the four properties and pungent and salty of five flavors were the variables to affect the hepatotoxicity. Based on such results, the established LR prediction model had some predictive power for hepatotoxicity of Chinese HMs. Hindawi Publishing Corporation 2016 2016-08-29 /pmc/articles/PMC5021893/ /pubmed/27656240 http://dx.doi.org/10.1155/2016/7273940 Text en Copyright © 2016 Hongjie Liu et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Liu, Hongjie Li, Tianhao Chen, Lingxiu Zhan, Sha Pan, Meilan Ma, Zhiguo Li, Chenghua Zhang, Zhe To Set Up a Logistic Regression Prediction Model for Hepatotoxicity of Chinese Herbal Medicines Based on Traditional Chinese Medicine Theory |
title | To Set Up a Logistic Regression Prediction Model for Hepatotoxicity of Chinese Herbal Medicines Based on Traditional Chinese Medicine Theory |
title_full | To Set Up a Logistic Regression Prediction Model for Hepatotoxicity of Chinese Herbal Medicines Based on Traditional Chinese Medicine Theory |
title_fullStr | To Set Up a Logistic Regression Prediction Model for Hepatotoxicity of Chinese Herbal Medicines Based on Traditional Chinese Medicine Theory |
title_full_unstemmed | To Set Up a Logistic Regression Prediction Model for Hepatotoxicity of Chinese Herbal Medicines Based on Traditional Chinese Medicine Theory |
title_short | To Set Up a Logistic Regression Prediction Model for Hepatotoxicity of Chinese Herbal Medicines Based on Traditional Chinese Medicine Theory |
title_sort | to set up a logistic regression prediction model for hepatotoxicity of chinese herbal medicines based on traditional chinese medicine theory |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5021893/ https://www.ncbi.nlm.nih.gov/pubmed/27656240 http://dx.doi.org/10.1155/2016/7273940 |
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