Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea.
10.1007/s11684-020-0762-0
- Author:
Yang JIAO
1
,
2
;
Zhan ZHANG
3
;
Ting ZHANG
4
;
Wen SHI
5
;
Yan ZHU
6
;
Jie HU
7
;
Qin ZHANG
8
Author Information
1. Department of General Internal Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China. peterpumch@
2. com.
3. Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, 100084, China.
4. Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
5. Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
6. Institute of Internet Industry, Tsinghua University, Beijing, 100084, China.
7. Department of Medical Administration, Suining Central Hospital, Suining, 629000, China.
8. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China. qinzhang@tsinghua.edu.cn.
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
causality;
diagnosis;
dyspnea;
graphical model;
knowledge representation;
uncertain
- From:
Frontiers of Medicine
2020;14(4):488-497
- CountryChina
- Language:English
-
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.
