Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differenti
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RESEARCH ARTICLE
Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea Yang Jiao (
✉)1,*, Zhan Zhang2,*, Ting Zhang3, Wen Shi4, Yan Zhu5, Jie Hu6, Qin Zhang (✉)7
1
Department of General Internal Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; 2Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China; 3 Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; 4Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; 5Institute of Internet Industry, Tsinghua University, Beijing 100084, China; 6Department of Medical Administration, Suining Central Hospital, Suining 629000, China; 7Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
© Higher Education Press 2020
Abstract Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysfunction, and neuromuscular disorder, among other conditions. Identifying the causes of dyspnea in clinical practice, especially for the general practitioner, remains a challenge. This pilot study aimed to develop a computeraided tool for improving the efficiency of differential diagnosis. The disease set with dyspnea as the chief complaint was established on the basis of clinical experience and epidemiological data. Differential diagnosis approaches were established and optimized by clinical experts. The artificial intelligence (AI) diagnosis model was constructed according to the dynamic uncertain causality graph knowledge-based editor. Twenty-eight diseases and syndromes were included in the disease set. The model contained 132 variables of symptoms, signs, and serological and imaging parameters. Medical records from the electronic hospital records of Suining Central Hospital were randomly selected. A total of 202 discharged patients with dyspnea as the chief complaint were included for verification, in which the diagnoses of 195 cases were coincident with the record certified as correct. The overall diagnostic accuracy rate of the model was 96.5%. In conclusion, the diagnostic accuracy of the AI model is promising and may compensate for the limitation of medical experience. Keywords
knowledge representation; uncertain; causality; graphical model; artificial intelligence; diagnosis; dyspnea
Introduction Dyspnea is a subjective respiratory discomfort that includes a variety of intensity and a heterogeneous nature of sensations [1]. It may be the main clinical manifestation of pulmonary disease, cardiovascular disease, myocardial dysfunction, anemia, and obesity as well as anxiety, anger, and other psychological feelings. Dyspnea may also exist in the same patient with a variet
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