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Construction and Evaluation of Neural Network Correlation Model between Syndrome Elements and Physical and Chemical Indexes of Unstable Angina Pectoris Complicated with Anxiety

OBJECTIVE: Syndrome elements are regarded as the smallest unit of syndrome differentiation, which is characterized by indivisibility and random combination. Therefore, it can well fit the goal of syndrome differentiation and unity. METHODS: Clinical physicochemical indicators are important reference...

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Autores principales: Chen, Xiaoyang, Wang, Yifei, Zhang, Li, Xu, Xuegong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420622/
https://www.ncbi.nlm.nih.gov/pubmed/36045947
http://dx.doi.org/10.1155/2022/6217186
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author Chen, Xiaoyang
Wang, Yifei
Zhang, Li
Xu, Xuegong
author_facet Chen, Xiaoyang
Wang, Yifei
Zhang, Li
Xu, Xuegong
author_sort Chen, Xiaoyang
collection PubMed
description OBJECTIVE: Syndrome elements are regarded as the smallest unit of syndrome differentiation, which is characterized by indivisibility and random combination. Therefore, it can well fit the goal of syndrome differentiation and unity. METHODS: Clinical physicochemical indicators are important references for disease diagnosis, but they are often not used too much in the process of TCM syndrome differentiation. In the era of intelligence, communicating TCM syndrome differentiation at the macro level with physiological and pathological processes at the micro level (i.e., these clinical physicochemical indicators) is an effective tool to realize intelligent medicine. Taking the collected relevant clinical physical and chemical indexes as the research object, on the basis of routine t-test and nonparametric test, logistic regression model is used to mine the main syndrome elements, and neural network multilayer perceptron is used to predict the feature model. RESULTS: Compared with non-blood stasis patients, there were significant differences in HGB, PLT, Pt, PTA, Na(+), TG, LDL, BNP, LVEDd, and EF in blood stasis patients. Taking blood stasis as the dependent variable and the above physical and chemical indexes with statistical significance (P < 0.05) as independent variables. Compared with non-qi depression patients, there were significant differences in atpp, TG, TC, LDL, LVESD, and FS in qi depression patients (P < 0.05). Taking Yin deficiency as dependent variable and the above physical and chemical indexes (Hgb, APTT, CKMB, LVEDd, and LVPW) with statistical significance (P < 0.05) as independent variables, binary logistic regression analysis was carried out. CONCLUSION: The combination pattern of physical and chemical indexes obtained from the neural network model provides a clinical reference basis for identifying the syndrome elements of unstable angina pectoris complicated with anxiety, such as blood stasis, qi depression, Qi deficiency, yin deficiency, phlegm turbidity, and qi stagnation.
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spelling pubmed-94206222022-08-30 Construction and Evaluation of Neural Network Correlation Model between Syndrome Elements and Physical and Chemical Indexes of Unstable Angina Pectoris Complicated with Anxiety Chen, Xiaoyang Wang, Yifei Zhang, Li Xu, Xuegong Comput Math Methods Med Research Article OBJECTIVE: Syndrome elements are regarded as the smallest unit of syndrome differentiation, which is characterized by indivisibility and random combination. Therefore, it can well fit the goal of syndrome differentiation and unity. METHODS: Clinical physicochemical indicators are important references for disease diagnosis, but they are often not used too much in the process of TCM syndrome differentiation. In the era of intelligence, communicating TCM syndrome differentiation at the macro level with physiological and pathological processes at the micro level (i.e., these clinical physicochemical indicators) is an effective tool to realize intelligent medicine. Taking the collected relevant clinical physical and chemical indexes as the research object, on the basis of routine t-test and nonparametric test, logistic regression model is used to mine the main syndrome elements, and neural network multilayer perceptron is used to predict the feature model. RESULTS: Compared with non-blood stasis patients, there were significant differences in HGB, PLT, Pt, PTA, Na(+), TG, LDL, BNP, LVEDd, and EF in blood stasis patients. Taking blood stasis as the dependent variable and the above physical and chemical indexes with statistical significance (P < 0.05) as independent variables. Compared with non-qi depression patients, there were significant differences in atpp, TG, TC, LDL, LVESD, and FS in qi depression patients (P < 0.05). Taking Yin deficiency as dependent variable and the above physical and chemical indexes (Hgb, APTT, CKMB, LVEDd, and LVPW) with statistical significance (P < 0.05) as independent variables, binary logistic regression analysis was carried out. CONCLUSION: The combination pattern of physical and chemical indexes obtained from the neural network model provides a clinical reference basis for identifying the syndrome elements of unstable angina pectoris complicated with anxiety, such as blood stasis, qi depression, Qi deficiency, yin deficiency, phlegm turbidity, and qi stagnation. Hindawi 2022-08-21 /pmc/articles/PMC9420622/ /pubmed/36045947 http://dx.doi.org/10.1155/2022/6217186 Text en Copyright © 2022 Xiaoyang Chen 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
Chen, Xiaoyang
Wang, Yifei
Zhang, Li
Xu, Xuegong
Construction and Evaluation of Neural Network Correlation Model between Syndrome Elements and Physical and Chemical Indexes of Unstable Angina Pectoris Complicated with Anxiety
title Construction and Evaluation of Neural Network Correlation Model between Syndrome Elements and Physical and Chemical Indexes of Unstable Angina Pectoris Complicated with Anxiety
title_full Construction and Evaluation of Neural Network Correlation Model between Syndrome Elements and Physical and Chemical Indexes of Unstable Angina Pectoris Complicated with Anxiety
title_fullStr Construction and Evaluation of Neural Network Correlation Model between Syndrome Elements and Physical and Chemical Indexes of Unstable Angina Pectoris Complicated with Anxiety
title_full_unstemmed Construction and Evaluation of Neural Network Correlation Model between Syndrome Elements and Physical and Chemical Indexes of Unstable Angina Pectoris Complicated with Anxiety
title_short Construction and Evaluation of Neural Network Correlation Model between Syndrome Elements and Physical and Chemical Indexes of Unstable Angina Pectoris Complicated with Anxiety
title_sort construction and evaluation of neural network correlation model between syndrome elements and physical and chemical indexes of unstable angina pectoris complicated with anxiety
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420622/
https://www.ncbi.nlm.nih.gov/pubmed/36045947
http://dx.doi.org/10.1155/2022/6217186
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