Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea

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 computer-aided 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

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Acknowledgements

This research was funded by the research project entitled “DUCG theory and application of medical aided diagnosis-algorithm of introducing classification variables in DUCG” by the Institute of Internet Industry, Tsinghua University. We appreciate the assistance by the staff of Suining Central Hospital, Sichuan Province, China, in validating the artificial intelligence diagnostic model presented in this paper.

Compliance with ethics guidelines

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, and Qin Zhang declare no conflicts of interests. This study protocol has been approved by the Ethics Committee of Suining Central Hospital, Sichuan Province, China. The Ethics Committee waived the requirement for informed consent because anonymous data were analyzed retrospectively.

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