Intelligent diagnosis system for jaundice based on dynamic uncertain causality graph

Nan Deng; Shichao Gengy; Shaorui HaoQin ZhangLanjuan Liz

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Intelligent diagnosis system for jaundice based on dynamic uncertain causality graph

机译：Intelligent diagnosis system for jaundice based on dynamic uncertain causality graph

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摘要

The healthcare system in China still has some defects such as the imbalance of medical resources. With the development of computer science, medical diagnostics digitisation has become possible. In this paper, a medical diagnosis system for jaundice based on dynamic uncertain causality graph (DUCG) is proposed. After a brief introduction to DUCG, a knowledge base of the DUCG for jaundice diagnosis is built. It can represent the experienced knowledge of human experts explicitly with graphical symbols. During the construction of the knowledge base, how to classify the medical diagnosis data in a structural and standard manner is important. In this paper, we propose to classify these data as four categories: general symptoms, medical signs, results of laboratory tests and results of imaging examinations. The first two form the general clinical information and the last two are further information. Each category can be further classified as sub-categories, so that users are easy to find the right position to fill the clinical information. Based on such designed DUCG medical software, 203 randomly selected jaundice related cases out of 3,985 case records of a hospital are tested. The final diagnostic accuracy of the system reaches 99.01%.

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来源
《International Journal of Information and Communication Technology》 |2022年第4期|439-462|共24页
作者
Nan Deng; Shichao Gengy; Shaorui HaoQin ZhangLanjuan Liz;
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作者单位

School of Software Engineering, Beijing Institute of Technology;

School of Communication, Shandong Normal University;

The First Affiliated Hospital, Zhejiang University, Zhejiang;

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正文语种英语
中图分类通信;
关键词
uncertainty; causality; probabilistic reasoning; medical diagnosis;

Intelligent diagnosis system for jaundice based on dynamic uncertain causality graph

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