Intelligent decision support system for the diagnosis of acute myocardial infarction

Bai Qing Sun; Ying Jun Feng; Qi Shu Pan; Chang Sheng Zhang; Gui Ying Hou; Zhen Zhong Guan; Jing Xu; Ling Fan Yu

Intelligent decision support system for the diagnosis of acute myocardial infarction

Bai Qing Sun^*
, Ying Jun Feng
, Qi Shu Pan
, Chang Sheng Zhang
, Gui Ying Hou
, Zhen Zhong Guan
, Jing Xu
, Ling Fan Yu

^*Corresponding author for this work

School of Management, Harbin Institute of Technology
Harbin Medical University

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

The thesis firstly analyzes the actuality of intelligent medical diagnostic field. To reduce the mis-diagnosis ratio, an intelligent decision support system for the diagnosis of acute myocardial infarction is built. For the single parameter dynamic searching method is used to train neural network, which proves to work well than BP algorithm, the training speed and classifying precision of neural network are increased. Tested by the diagnostic cases in clinical, the sensitivity, specificity and accuracy of the diagnosis system are higher than those of the physicians. It is significant to improve the accuracy to diagnose acute myocardial infarction.

Original language	English
Pages (from-to)	141-144
Number of pages	4
Journal	Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice
Volume	26
Issue number	10
State	Published - Oct 2006
Externally published	Yes

Keywords

Acute myocardial infarction
Artificial neural network
Decision support system
Intelligent diagnosis system

Cite this

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title = "Intelligent decision support system for the diagnosis of acute myocardial infarction",

abstract = "The thesis firstly analyzes the actuality of intelligent medical diagnostic field. To reduce the mis-diagnosis ratio, an intelligent decision support system for the diagnosis of acute myocardial infarction is built. For the single parameter dynamic searching method is used to train neural network, which proves to work well than BP algorithm, the training speed and classifying precision of neural network are increased. Tested by the diagnostic cases in clinical, the sensitivity, specificity and accuracy of the diagnosis system are higher than those of the physicians. It is significant to improve the accuracy to diagnose acute myocardial infarction.",

keywords = "Acute myocardial infarction, Artificial neural network, Decision support system, Intelligent diagnosis system",

author = "Sun, \{Bai Qing\} and Feng, \{Ying Jun\} and Pan, \{Qi Shu\} and Zhang, \{Chang Sheng\} and Hou, \{Gui Ying\} and Guan, \{Zhen Zhong\} and Jing Xu and Yu, \{Ling Fan\}",

year = "2006",

month = oct,

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journal = "Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice",

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TY - JOUR

T1 - Intelligent decision support system for the diagnosis of acute myocardial infarction

AU - Sun, Bai Qing

AU - Feng, Ying Jun

AU - Pan, Qi Shu

AU - Zhang, Chang Sheng

AU - Hou, Gui Ying

AU - Guan, Zhen Zhong

AU - Xu, Jing

AU - Yu, Ling Fan

PY - 2006/10

Y1 - 2006/10

N2 - The thesis firstly analyzes the actuality of intelligent medical diagnostic field. To reduce the mis-diagnosis ratio, an intelligent decision support system for the diagnosis of acute myocardial infarction is built. For the single parameter dynamic searching method is used to train neural network, which proves to work well than BP algorithm, the training speed and classifying precision of neural network are increased. Tested by the diagnostic cases in clinical, the sensitivity, specificity and accuracy of the diagnosis system are higher than those of the physicians. It is significant to improve the accuracy to diagnose acute myocardial infarction.

AB - The thesis firstly analyzes the actuality of intelligent medical diagnostic field. To reduce the mis-diagnosis ratio, an intelligent decision support system for the diagnosis of acute myocardial infarction is built. For the single parameter dynamic searching method is used to train neural network, which proves to work well than BP algorithm, the training speed and classifying precision of neural network are increased. Tested by the diagnostic cases in clinical, the sensitivity, specificity and accuracy of the diagnosis system are higher than those of the physicians. It is significant to improve the accuracy to diagnose acute myocardial infarction.

KW - Acute myocardial infarction

KW - Artificial neural network

KW - Decision support system

KW - Intelligent diagnosis system

UR - https://www.scopus.com/pages/publications/33845353133

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SN - 1000-6788

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SP - 141

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JO - Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice

JF - Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice

IS - 10

ER -

Intelligent decision support system for the diagnosis of acute myocardial infarction

Abstract

Keywords

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