Personalized Body Constitution Inquiry Based on Machine Learning

Joint Authors

Ren, Yan
Li, Yanghui
Wen, Guihua
Lu, Yantong
Wang, Ziying
Wang, Changjun
Zhang, Yuan
Fan, Baochao

Source

Journal of Healthcare Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-11-12

Country of Publication

Egypt

No. of Pages

11

Main Topic

Public Health
Medicine

Abstract EN

Background.

Body constitution (BC) is the abstract concept indicating the state of a person’s health in Traditional Chinese Medicine (TCM).

The doctor identifies the body constitution of the patient through inspection and inquiry.

Previous research simulates doctors to identify BC types according to a patient’s objective physical indicators.

However, the lack of subjective feeling information can reduce the accuracy of the machine to imitate the doctor’s diagnosis.

The Constitution in Chinese Medicine Questionnaire (CCMQ) is used to collect subjective information but suffers from low acquisition efficiency.

Methods.

This paper presents a personalized body constitution inquiry method based on a machine learning technique.

It employs a random generator, a feature extractor, and a classifier to simulate the doctor inquiry and generate a personalized questionnaire.

Specifically, the feature extractor evaluates and sorts the question of the constitution in the CCMQ based on the recognition results of the tongue coating image of patients.

The sorted questions and relevant BC label are inputted into the classifier; the best questions are screened out for patients.

Results.

The experimental results show that our method can select personalized questions from the CCMQ for the patients, significantly reducing the time and the number of questions to answer.

It also improves the accuracy of recognizing BC.

Compared with the CCMQ, patients had 68.3% fewer questions to answer and the time occupied by answering is reduced by 80.3%.

Conclusions.

The proposed method can simulate the doctor's inquiry and pick out personalized questions for patients.

It can act as auxiliary diagnosis tools to collect subjective patient feelings and help make further judgments on the patient’s BC types.

American Psychological Association (APA)

Fan, Baochao& Li, Yanghui& Wen, Guihua& Ren, Yan& Lu, Yantong& Wang, Ziying…[et al.]. 2020. Personalized Body Constitution Inquiry Based on Machine Learning. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1186494

Modern Language Association (MLA)

Fan, Baochao…[et al.]. Personalized Body Constitution Inquiry Based on Machine Learning. Journal of Healthcare Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1186494

American Medical Association (AMA)

Fan, Baochao& Li, Yanghui& Wen, Guihua& Ren, Yan& Lu, Yantong& Wang, Ziying…[et al.]. Personalized Body Constitution Inquiry Based on Machine Learning. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1186494

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1186494