CLC number: R447
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-04-19
Cited: 0
Clicked: 5517
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.
@article{title="Intelligent diagnosis of jaundice with dynamic uncertain causality graph model",
author="Shao-rui Hao, Shi-chao Geng, Lin-xiao Fan, Jia-jia Chen, Qin Zhang, Lan-juan Li",
journal="Journal of Zhejiang University Science B",
volume="18",
number="5",
pages="393-401",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B1600273"
}
%0 Journal Article
%T Intelligent diagnosis of jaundice with dynamic uncertain causality graph model
%A Shao-rui Hao
%A Shi-chao Geng
%A Lin-xiao Fan
%A Jia-jia Chen
%A Qin Zhang
%A Lan-juan Li
%J Journal of Zhejiang University SCIENCE B
%V 18
%N 5
%P 393-401
%@ 1673-1581
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1600273
TY - JOUR
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
VL - 18
IS - 5
SP - 393
EP - 401
%@ 1673-1581
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B1600273
Abstract: 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.
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