Advertisement

Clinical Assistant Diagnosis System Based on Dynamic Uncertain Causality Graph

  • Xusong Bu
  • Zhan Zhang
  • Qin Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 856)

Abstract

Artificial intelligence clinical assistant diagnosis system has become one of the mainstream in the field of medical research at present. The main challenge of this system is to have both high diagnostic accuracy and good interpretability for diagnostic results. Dynamic Uncertainty Causality diagram is a probability graph model, which can explain the calculation results in a graphical form. This paper introduces the clinical assistant diagnosis system based on DUCG, and shows the system diagnosis process through a case study. Experiments show that: the system with high accuracy and good interpretability.

Keywords

Assistant diagnosis Causal inference Probability model 

References

  1. 1.
    Shortliffe, E.H.: Knowledge engineering for medical decision making: a review of computer-based clinical decision aids. Proc. IEEE 67(9), 1207–1224 (1979)CrossRefGoogle Scholar
  2. 2.
    Suzuki, K.: Massive-training artificial neural network coupled With Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in ct colonography. IEEE Trans. Med. Imaging 29(11), 1907–1917 (2010)CrossRefGoogle Scholar
  3. 3.
    Miller, A.S.: Review of neural network applications in medical imaging and signal processing. Med. Biol. Eng. Comput. 30(5), 449–464 (1992)CrossRefGoogle Scholar
  4. 4.
    Costa, C.A.: From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artif. Intell. Med. 67, 75–93 (2016)CrossRefGoogle Scholar
  5. 5.
    Zhang, Q.: Dynamic uncertain causality graph for knowledge representation and reasoning: discrete DAG cases. J. Comput. Sci. Technol. 27(1), 1–23 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Zhang, Q., Geng, S.: Dynamic uncertain causality graph applied to dynamic fault diagnosis of large and complex systems. IEEE Trans. Reliab. 64, 910–927 (2015)CrossRefGoogle Scholar
  7. 7.
    Shao-rui, H.A.O.: Intelligent diagnosis of jaundice with dynamic uncertain causality graph model. Zhejiang Univ. Sci. B (Biomed. Biotechnol.) 18(5), 393–401 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  2. 2.Beijing Tsingrui Intelligence Technology Co., Ltd.BeijingChina

Personalised recommendations