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| FMD | Research in "Frontiers of Medicine": Evaluation of the effectiveness of an artificial intelligence diagnosis model developed based on the theory of dynamic uncertain causal diagrams on the differential diagnosis of dyspnea as the chief complaint disease |
Paper Title: Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea
Journal: Frontiers of Medicine
Frontiers of Medicine Frontiers of Medicine Frontiers of MedicineAuthors: Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang
Posting time: 16 Jul 2020
DOI: 10.
1007/s11684-020-0762-0
1007/s11684-020-0762-0 10.
1007/s11684-020-0762-0
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Introduction: Jiao Yang from Peking Union Medical College Hospital and Zhang Qin from Tsinghua University published a research paper in Frontiers of Medicine " Development of an artificial artificial intelligence diagnosis model based on the theory of dynamic uncertain causality diagram for the differential diagnosis of dyspnea as the chief complaint.
" intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea )
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" intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea )
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Introduction Frontiers of Medicine Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea
Artificial intelligence (AI)-assisted diagnosis has developed rapidly in recent years
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In the current mature pathology, radiological imaging, and skin disease diagnosis, the accuracy of AI-assisted diagnosis can even exceed the average diagnosis level of specialists
By using the rich clinical experience of Peking Union Medical College Hospital in the diagnosis of difficult and difficult diseases, and after a series of exploratory research, the author team gradually improved and established an artificial intelligence-assisted diagnosis system based on logical reasoning
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Studies have confirmed that the artificial intelligence diagnosis system can make up for the limitations of personal knowledge and professional experience, and can significantly improve the diagnostic efficiency of general medicine
Abstract Based on the theory of Dynamic Uncertain Causality Diagram (DUCG), an artificial intelligence-assisted diagnosis model with dyspnea symptoms as the main complaint was established, and its diagnostic value was verified
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Combining the experience of clinical experts and epidemiological data, determine the disease library where dyspnea is the chief complaint, optimize the diagnosis and differential diagnosis path of each disease in the disease library; then use the DUCG knowledge base editor according to the DUCG theory of uncertain causal knowledge expression method , Build an artificial intelligence-assisted diagnosis model; screen the medical records of hospitalized patients with dyspnea in the electronic information medical record system of a tertiary hospital from January 2013 to December 2018, and randomly select the cases diagnosed as each disease in the disease database , Use the DUCG test platform and inference engine to test to verify the diagnostic accuracy of the model
Summary
Original information
Textual information textual informationtitle
titleDevelopment of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea
Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspneaauthor
authorYang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang
https://journal.
hep.
com.
cn/fmd/EN/10.
1007/s11684-020-0762-0
hep.
com.
cn/fmd/EN/10.
1007/s11684-020-0762-0 https://journal.
hep.
com.
cn/fmd/EN/10.
1007/s11684-020-0762-0
https://link.
springer.
com/article/10.
1007/s11684-020-0762-0
springer.
com/article/10.
1007/s11684-020-0762-0 https://link.
springer.
com/article/10.
1007/s11684-020-0762-0
"Frontier" series of English academic journals
"Frontier" series of English academic journals "Frontier" series of English academic journals "Frontier" series of English academic journals The "Frontiers" series of English academic journals sponsored by the Ministry of Education and Higher Education Press was officially launched in 2006 and distributed to the world in online and print editions
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The series of journals includes four themes of basic sciences, life sciences, engineering technology, and humanities and social sciences.
Higher Education Press was selected as a cluster project of the "Excellence Action Plan for Chinese Sci-tech Journals"
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In the Frontier series of journals: 13 are included by SCI; 1 are included by A&HCI; 6 are included by Ei; 2 are included by MEDLINE; 11 are Chinese core journals of science and technology; 16 are included by CSCD
China Academic Frontier Journal Network
China Academic Frontier Journal Net China Academic Frontier Journal Nethttp://journal.
http://journal.
