Full Text:   <3326>

Summary:  <1763>

CLC number: R447

On-line Access: 2017-05-04

Received: 2016-06-15

Revision Accepted: 2016-10-17

Crosschecked: 2017-04-19

Cited: 0

Clicked: 5035

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

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.

@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",
publisher="Zhejiang University Press & Springer",

%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

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

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


[1]Avci, E., 2012. A new expert system for diagnosis of lung cancer: GDA-LS_SVM. J. Med. Syst., 36(3):2005-2009.

[2]Bhat, S., Acharya, U.R., Adeli, H., et al., 2014. Automated diagnosis of autism: in search of a mathematical marker. Rev. Neurosci., 25(6):851-861.

[3]Bhutani, V.K., Johnson-Hamerman, L., 2015. The clinical syndrome of bilirubin-induced neurologic dysfunction. Semin. Fetal Neonatal Med., 20(1):6-13.

[4]Dong, C., Wang, Y., Zhang, Q., et al., 2014a. The methodology of dynamic uncertain causality graph for intelligent diagnosis of vertigo. Comput. Methods Prog. Biomed., 113(1):162-174.

[5]Dong, C., Zhang, Q., Geng, S., 2014b. A modeling and probabilistic reasoning method of dynamic uncertain causality graph for industrial fault diagnosis. Int. J. Automat. Comput., 11(3):288-298.

[6]Gottesman, L.E., del Vecchio, M.T., Aronoff, S.C., 2015. Etiologies of conjugated hyperbilirubinemia in infancy: a systematic review of 1692 subjects. BMC Pediatrics, 15(1):8.

[7]Hatzilygeroudis, I., Prentzas, J., 2004. Integrating (rules, neural networks) and cases for knowledge representation and reasoning in expert systems. Expert Syst. Appl., 27(1): 63-75.

[8]Keith, R.D., Beckley, S., Garibaldi, J.M., et al., 1995. A multicentre comparative study of 17 experts and an intelligent computer system for managing labour using the cardiotocogram. Br. J. Obstet. Gynaecol., 102(9): 688-700.

[9]Kruk, M., Osowski, S., Markiewicz, T., et al., 2014. Computer approach to recognition of Fuhrman grade of cells in clear-cell renal cell carcinoma. Anal. Quant Cytopathol. Histpathol., 36(3):147-160.

[10]Lee, G.H., 2008. Rule-based and case-based reasoning approach for internal audit of bank. Knowl.-Based Syst., 21(2):140-147.

[11]Li, P., Bi, T., Huang, J., et al., 2014. Breast cancer early diagnosis based on hybrid strategy. Biomed. Mater. Eng., 24(6):3397-3404.

[12]Madabhushi, A., Doyle, S., Lee, G., et al., 2010. Integrated diagnostics: a conceptual framework with examples. Clin. Chem. Lab. Med., 48(7):989-998.

[13]Malek, S., Phillips, R., Mohsen, A., et al., 2005. Computer assisted orthopaedic surgical system for insertion of distal locking screws in intra-medullary nails: a valid and reliable navigation system. Int. J. Med. Robot. Comput. Assist. Surg., 1(4):34-44.

[14]Oladipupo, O.O., Uwadia, C.O., Ayo, C.K., 2012. Improving medical rule-based expert systems comprehensibility: fuzzy association rule mining approach. Int. J. Artif. Intell. Soft Comput., 3(1):29-38.

[15]Pearl, J., 2009. Causality: Models, Reasoning, and Inference. Cambridge University Press, New York.

[16]Poole, D., Zhang, N.L., 2003. Exploiting contextual independence in probabilistic inference. J. Artif. Intell. Res., 18:263-313.

[17]Sasikumar, M., Ramani, S., Raman, S.M., et al., 2007. A practical introduction to rule based expert systems. Narosa Publishing House, New Delhi.

[18]Shen, Y., Colloc, J., Jacquet-Andrieu, A., et al., 2015. Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system. J. Biomed. Inform., 56:307-317.

[19]Siniscalchi, S.M., Svendsen, T., Lee, C., 2014. An artificial neural network approach to automatic speech processing. Neurocomputing, 140:326-338.

[20]Suk, H.I., Lee, S.W., Shen, D., 2014. Subclass-based multi-task learning for Alzheimer’s disease diagnosis. Front. Aging Neurosci., 6:168.

[21]Xu, B.G., 2012. Intelligent fault inference for rotating flexible rotors using bayesian belief network. Expert Syst. Appl., 39(1):816-822.

[22]Zhang, Q., 2012. Dynamic uncertain causality graph for knowledge representation and reasoning: discrete DAG cases. J. Comput. Sci. Technol., 27(1):1-23.

[23]Zhang, Q., 2015a. Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: directed cyclic graph and joint probability distribution. IEEE Trans. Neural Netw. Learn. Syst., 26(7):1503-1517.

[24]Zhang, Q., 2015b. Dynamic uncertain causality graph for knowledge representation and reasoning: continuous variable, uncertain evidence, and failure forecast. IEEE Trans. Syst. Man Cybern. Syst., 45(7):990-1003.

[25]Zhang, Q., Geng, S., 2015. Dynamic uncertain causality graph applied to dynamic fault diagnoses of large and complex systems. IEEE Trans. Reliab., 64(3):910-927.

[26]Zhang, Q., Dong, C., Cui, Y., et al., 2014. Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: statistics base, matrix, and application. IEEE Trans. Neural Netw. Learn. Syst., 25(4): 645-663.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE