Deep Learning Based Syndrome Diagnosis of Chronic Gastritis

Joint Authors

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

Source

Computational and Mathematical Methods in Medicine

Issue

Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2014-03-05

Country of Publication

Egypt

No. of Pages

8

Main Subjects

Medicine

Abstract 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.

American Psychological Association (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

Modern Language Association (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

American Medical Association (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

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-509749