Dynamic uncertain causality graph for computer-aided general clinical diagnoses with nasal obstruction as an illustration

Many AI systems have been developed for clinical diagnoses, in which most of them lack interpretability in both knowledge representation and inference results. The newly developed Dynamic Uncertain Causality Graph (DUCG) is a probabilistic graphical model with strong interpretability. However, existing DUCG is mainly for fault diagnoses of large, complex industrial systems. In this paper, we extend DUCG for better application in general clinical diagnoses. Four extensions are introduced: (1) special logic gate and zoom function event variables to represent and quantify the influences of various risk factors on the morbidities of diseases. (2) Reversal logic gate to model the case that some diseases/causes may result in at least two simultaneous symptoms/consequences. (3) Disease-specific manifestation variable for special inference and easy understanding to diagnose a specific disease. (4) Event attention importance to count contributions of isolated state-abnormal variables in inference. To illustrate and verify the extended DUCG methodology, we performed a case study for diagnosing 25 diseases causing nasal obstruction. We tested 171 cases randomly selected from total 471 cases of discharged patients in the hospital information system of Xuanwu Hospital. The diagnosis precision of the extended DUCG was 100%. The diagnosis precision of the third-party verification performed by Suining Central Hospital was 98.86%, which exhibited the strong generalization ability of the extended DUCG.