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On-line Access: 2017-05-04

Received: 2016-06-15

Revision Accepted: 2016-10-17

Crosschecked: 2017-04-19

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Journal of Zhejiang University SCIENCE B 2017 Vol.18 No.5 P.393-401


Intelligent diagnosis of jaundice with dynamic uncertain causality graph model

Author(s):  Shao-rui Hao, Shi-chao Geng, Lin-xiao Fan, Jia-jia Chen, Qin Zhang, Lan-juan Li

Affiliation(s):  State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China; more

Corresponding email(s):   zhangqin@buaa.edu.cn, ljli@zju.edu.cn

Key Words:  Jaundice, Intelligent diagnosis, Dynamic uncertain causality graph, Expert system

Shao-rui Hao, Shi-chao Geng, Lin-xiao Fan, Jia-jia Chen, Qin Zhang, Lan-juan Li. Intelligent diagnosis of jaundice with dynamic uncertain causality graph model[J]. Journal of Zhejiang University Science B, 2017, 18(5): 393-401.

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author="Shao-rui Hao, Shi-chao Geng, Lin-xiao Fan, Jia-jia Chen, Qin Zhang, Lan-juan Li",
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publisher="Zhejiang University Press & Springer",

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%A Shao-rui Hao
%A Shi-chao Geng
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1600273

T1 - Intelligent diagnosis of jaundice with dynamic uncertain causality graph model
A1 - Shao-rui Hao
A1 - Shi-chao Geng
A1 - Lin-xiao Fan
A1 - Jia-jia Chen
A1 - Qin Zhang
A1 - Lan-juan Li
J0 - Journal of Zhejiang University Science B
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EP - 401
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.B1600273

jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A “chaining” inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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