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...
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doaj-7299ab3c9f66427fbf6dd8cb6e76eadf2020-11-24T22:15:57ZengHindawi LimitedEvidence-Based Complementary and Alternative Medicine1741-427X1741-42882016-01-01201610.1155/2016/72739407273940To Set Up a Logistic Regression Prediction Model for Hepatotoxicity of Chinese Herbal Medicines Based on Traditional Chinese Medicine TheoryHongjie Liu0Tianhao Li1Lingxiu Chen2Sha Zhan3Meilan Pan4Zhiguo Ma5Chenghua Li6Zhe Zhang7Department of Traditional Chinese Medicine, Medical College, Jinan University, Guangzhou 510632, ChinaDepartment of Traditional Chinese Medicine, Medical College, Jinan University, Guangzhou 510632, ChinaDepartment of Traditional Chinese Medicine, Medical College, Jinan University, Guangzhou 510632, ChinaDepartment of Traditional Chinese Medicine, Medical College, Jinan University, Guangzhou 510632, ChinaDepartment of Traditional Chinese Medicine, Medical College, Jinan University, Guangzhou 510632, ChinaDepartment of Chinese Herbal Medicines, Pharmacy College, Jinan University, Guangzhou 510632, ChinaBiomaterial Laboratory of Guangdong Higher Education and Department of Biomedical Engineering, Jinan University, Guangzhou 510632, ChinaShandong University of Traditional Chinese Medicine, Jinan 250355, ChinaAims. 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.http://dx.doi.org/10.1155/2016/7273940 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hongjie Liu Tianhao Li Lingxiu Chen Sha Zhan Meilan Pan Zhiguo Ma Chenghua Li Zhe Zhang |
spellingShingle |
Hongjie Liu Tianhao Li Lingxiu Chen Sha Zhan Meilan Pan Zhiguo Ma Chenghua Li Zhe Zhang To Set Up a Logistic Regression Prediction Model for Hepatotoxicity of Chinese Herbal Medicines Based on Traditional Chinese Medicine Theory Evidence-Based Complementary and Alternative Medicine |
author_facet |
Hongjie Liu Tianhao Li Lingxiu Chen Sha Zhan Meilan Pan Zhiguo Ma Chenghua Li Zhe Zhang |
author_sort |
Hongjie Liu |
title |
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_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_sort |
to set up a logistic regression prediction model for hepatotoxicity of chinese herbal medicines based on traditional chinese medicine theory |
publisher |
Hindawi Limited |
series |
Evidence-Based Complementary and Alternative Medicine |
issn |
1741-427X 1741-4288 |
publishDate |
2016-01-01 |
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. |
url |
http://dx.doi.org/10.1155/2016/7273940 |
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