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Agent-based intelligent medical diagnosis system for patients

Issue title: Papers from the 3rd International Conference on Biomedical Engineering and Technology (iCBEB 2014), 25–28 September 2014, Beijing, China

Subtitle:

Guest editors: Edward J. Ciaccio

Article type: Research Article

Authors: Zhang, Yingfeng* | Liu, Sichao | Zhu, Zhenfei | Si, Shubin

Affiliations: Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi, China

Correspondence: [*] Corresponding author: Yingfeng Zhang, Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi, China. Tel.: +86 139911 35396; E-mail:[email protected]

Keywords: Intuitionistic fuzzy set theory, sensing technology, intelligent medical diagnosis, agent, medical knowledge

DOI: 10.3233/THC-150976

Journal: Technology and Health Care, vol. 23, no. s2, pp. S397-S410, 2015

Published: 2015
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Abstract

BACKGROUND: According to the analysis of the challenges faced by the current public health circumstances such as the sharp increase in elderly patients, limited medical personnel, resources and technology, the agent-based intelligent medical diagnosis system for patients (AIMDS) is proposed in this research.

OBJECTIVE: Based on advanced sensing technology and professional medical knowledge, the AIMDS can output the appropriate medical prescriptions and food prohibition when the physical signs and symptoms of the patient are inputted.

METHODS: Three core modules are designed include sensing module, intuition-based fuzzy set theory/medical diagnosis module, and medical knowledge module.

RESULTS: The result shows that the optimized prescription can reach the desired level, with great curative effect for patient disease, through a case study simulation.

CONCLUSION: The presented AIMDS can integrate sensor technique and intelligent medical diagnosis methods to make an accurate diagnosis, resulting in three-type of optimized descriptions for patient selection.

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