Correlation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network Prediction Model

Objective. To predict the major comorbidities of type 2 diabetes based on the distribution characteristics of syndromes, and to explore the relationship between TCM syndromes and comorbidities of type 2 diabetes. Methods. Based on the electronic medical record data of 3413 outpatient visits from 995...

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Autores principales:	Yifei Wang, Runshun Zhang, Min Pi, Julia Xu, Moyan Qiu, Tiancai Wen
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Other systems of medicine RZ201-999
Acceso en línea:	https://doaj.org/article/a19d9a4e297d42f29ec2fcfd6b48a039
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spelling	oai:doaj.org-article:a19d9a4e297d42f29ec2fcfd6b48a0392021-11-29T00:56:03ZCorrelation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network Prediction Model1741-428810.1155/2021/6095476https://doaj.org/article/a19d9a4e297d42f29ec2fcfd6b48a0392021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6095476https://doaj.org/toc/1741-4288Objective. To predict the major comorbidities of type 2 diabetes based on the distribution characteristics of syndromes, and to explore the relationship between TCM syndromes and comorbidities of type 2 diabetes. Methods. Based on the electronic medical record data of 3413 outpatient visits from 995 type 2 diabetes patients with comorbidities, descriptive statistical methods were used to analyze the basic characteristics of the population, the distribution characteristics of comorbidities, and TCM syndromes. A neural network model for the prediction of type 2 diabetic comorbidities based on TCM syndromes was constructed. Results. Patients with TCM syndrome of blood amassment in the lower jiao were diagnosed with renal insufficiency with 95% test sensitivity. The patients with spleen deficiency combined with ascending counterflow of stomach qi and cold-damp patterns were diagnosed with gastrointestinal lesions with 92% sensitivity. The patients with TCM syndrome group of spleen heat and exuberance of heart fire were diagnosed as type 2 diabetes complicated with hypertension with a sensitivity of 91%. In addition, the prediction accuracy of combined neuropathy, heart disease, liver disease, and lipid metabolism disorder reached 70∼90% in TCM syndrome groups. Conclusion. The fully connected neural network model study showed that syndrome characteristics are highly correlated with type 2 diabetes comorbidities. Syndrome location is commonly in the heart, spleen, stomach, lower jiao, meridians, etc., while syndrome pattern manifests in states of deficiency, heat, phlegm, and blood stasis. The different combinations of disease location and disease pattern reflect the syndrome characteristics of different comorbidities forming the characteristic syndrome group of each comorbidity. Major comorbidities could be predicted with a high degree of accuracy through TCM syndromes. Findings from this study may have further implementations to assist with the diagnosis, treatment, and prevention of diabetic comorbidities at an early stage.Yifei WangRunshun ZhangMin PiJulia XuMoyan QiuTiancai WenHindawi LimitedarticleOther systems of medicineRZ201-999ENEvidence-Based Complementary and Alternative Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Other systems of medicine RZ201-999
spellingShingle	Other systems of medicine RZ201-999 Yifei Wang Runshun Zhang Min Pi Julia Xu Moyan Qiu Tiancai Wen Correlation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network Prediction Model
description	Objective. To predict the major comorbidities of type 2 diabetes based on the distribution characteristics of syndromes, and to explore the relationship between TCM syndromes and comorbidities of type 2 diabetes. Methods. Based on the electronic medical record data of 3413 outpatient visits from 995 type 2 diabetes patients with comorbidities, descriptive statistical methods were used to analyze the basic characteristics of the population, the distribution characteristics of comorbidities, and TCM syndromes. A neural network model for the prediction of type 2 diabetic comorbidities based on TCM syndromes was constructed. Results. Patients with TCM syndrome of blood amassment in the lower jiao were diagnosed with renal insufficiency with 95% test sensitivity. The patients with spleen deficiency combined with ascending counterflow of stomach qi and cold-damp patterns were diagnosed with gastrointestinal lesions with 92% sensitivity. The patients with TCM syndrome group of spleen heat and exuberance of heart fire were diagnosed as type 2 diabetes complicated with hypertension with a sensitivity of 91%. In addition, the prediction accuracy of combined neuropathy, heart disease, liver disease, and lipid metabolism disorder reached 70∼90% in TCM syndrome groups. Conclusion. The fully connected neural network model study showed that syndrome characteristics are highly correlated with type 2 diabetes comorbidities. Syndrome location is commonly in the heart, spleen, stomach, lower jiao, meridians, etc., while syndrome pattern manifests in states of deficiency, heat, phlegm, and blood stasis. The different combinations of disease location and disease pattern reflect the syndrome characteristics of different comorbidities forming the characteristic syndrome group of each comorbidity. Major comorbidities could be predicted with a high degree of accuracy through TCM syndromes. Findings from this study may have further implementations to assist with the diagnosis, treatment, and prevention of diabetic comorbidities at an early stage.
format	article
author	Yifei Wang Runshun Zhang Min Pi Julia Xu Moyan Qiu Tiancai Wen
author_facet	Yifei Wang Runshun Zhang Min Pi Julia Xu Moyan Qiu Tiancai Wen
author_sort	Yifei Wang
title	Correlation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network Prediction Model
title_short	Correlation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network Prediction Model
title_full	Correlation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network Prediction Model
title_fullStr	Correlation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network Prediction Model
title_full_unstemmed	Correlation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network Prediction Model
title_sort	correlation between tcm syndromes and type 2 diabetic comorbidities based on fully connected neural network prediction model
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/a19d9a4e297d42f29ec2fcfd6b48a039
work_keys_str_mv	AT yifeiwang correlationbetweentcmsyndromesandtype2diabeticcomorbiditiesbasedonfullyconnectedneuralnetworkpredictionmodel AT runshunzhang correlationbetweentcmsyndromesandtype2diabeticcomorbiditiesbasedonfullyconnectedneuralnetworkpredictionmodel AT minpi correlationbetweentcmsyndromesandtype2diabeticcomorbiditiesbasedonfullyconnectedneuralnetworkpredictionmodel AT juliaxu correlationbetweentcmsyndromesandtype2diabeticcomorbiditiesbasedonfullyconnectedneuralnetworkpredictionmodel AT moyanqiu correlationbetweentcmsyndromesandtype2diabeticcomorbiditiesbasedonfullyconnectedneuralnetworkpredictionmodel AT tiancaiwen correlationbetweentcmsyndromesandtype2diabeticcomorbiditiesbasedonfullyconnectedneuralnetworkpredictionmodel
_version_	1718407724369182720

Correlation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network Prediction Model

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