Deep Learning Based Syndrome Diagnosis of Chronic Gastritis

المؤلفون المشاركون

Wang, Yi-Qin
Zheng, Wu
Lu, Xiong
Yan, Jian-Jun
Liu, Guo-Ping
Zhong, Tao
Qian, Peng

المصدر

Computational and Mathematical Methods in Medicine

العدد

المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-8، 8ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2014-03-05

دولة النشر

مصر

عدد الصفحات

8

التخصصات الرئيسية

الطب البشري

الملخص EN

In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain.

However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome.

So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM.

The results showed that deep learning could improve the accuracy of syndrome recognition.

Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Liu, Guo-Ping& Yan, Jian-Jun& Wang, Yi-Qin& Zheng, Wu& Zhong, Tao& Lu, Xiong…[et al.]. 2014. Deep Learning Based Syndrome Diagnosis of Chronic Gastritis. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-509749

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Liu, Guo-Ping…[et al.]. Deep Learning Based Syndrome Diagnosis of Chronic Gastritis. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-509749

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Liu, Guo-Ping& Yan, Jian-Jun& Wang, Yi-Qin& Zheng, Wu& Zhong, Tao& Lu, Xiong…[et al.]. Deep Learning Based Syndrome Diagnosis of Chronic Gastritis. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-509749

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-509749