Clinical Assistant Diagnosis System Based on Dynamic Uncertain Causality Graph

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


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.


Assistant diagnosis Causal inference Probability model 


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© 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

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