Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.
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.
['Hao|Shao-Rui|SR|', 'Geng|Shi-Chao|SC|', 'Fan|Lin-Xiao|LX|', 'Chen|Jia-Jia|JJ|', 'Zhang|Qin|Q|', 'Li|Lan-Juan|LJ|']
D000465:Algorithms D001499:Bayes Theorem D015984:Causality D003196:Computer Graphics D003198:Computer Simulation D020000:Decision Support Systems, Clinical D003936:Diagnosis, Computer-Assisted D006801:Humans D007565:Jaundice D000069550:Machine Learning D015233:Models, Statistical D015995:Prevalence D015203:Reproducibility of Results D012680:Sensitivity and Specificity
Intelligent diagnosis Jaundice Dynamic uncertain causality graph Expert system