Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics

Artificial intelligence (AI) has been deeply applied in the medical field and has shown broad application prospects. Pre-consultation system is an important supplement to the traditional face-to-face consultation. The combination of the AI and the pre-consultation system can help to raise the effici...

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Autores principales:	Han Qian, Bin Dong, Jia-jun Yuan, Fan Yin, Zhao Wang, Hai-ning Wang, Han-song Wang, Dan Tian, Wei-hua Li, Bin Zhang, Lie-bin Zhao, Bo-tao Ning
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	artificial intelligence pre-consultation outpatient medical records pediatric electronic health record Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/f94313959c2240faaa3a633229e4a3ba
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spelling	oai:doaj.org-article:f94313959c2240faaa3a633229e4a3ba2021-11-08T08:02:45ZPre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics2296-858X10.3389/fmed.2021.695185https://doaj.org/article/f94313959c2240faaa3a633229e4a3ba2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmed.2021.695185/fullhttps://doaj.org/toc/2296-858XArtificial intelligence (AI) has been deeply applied in the medical field and has shown broad application prospects. Pre-consultation system is an important supplement to the traditional face-to-face consultation. The combination of the AI and the pre-consultation system can help to raise the efficiency of the clinical work. However, it is still challenging for the AI to analyze and process the complicated electronic health record (EHR) data. Our pre-consultation system uses an automated natural language processing (NLP) system to communicate with the patients through the mobile terminals, applying the deep learning (DL) techniques to extract the symptomatic information, and finally outputs the structured electronic medical records. From November 2019 to May 2020, a total of 2,648 pediatric patients used our model to provide their medical history and get the primary diagnosis before visiting the physicians in the outpatient department of the Shanghai Children's Medical Center. Our task is to evaluate the ability of the AI and doctors to obtain the primary diagnosis and to analyze the effect of the consistency between the medical history described by our model and the physicians on the diagnostic performance. The results showed that if we do not consider whether the medical history recorded by the AI and doctors was consistent or not, our model performed worse compared to the physicians and had a lower average F1 score (0.825 vs. 0.912). However, when the chief complaint or the history of present illness described by the AI and doctors was consistent, our model had a higher average F1 score and was closer to the doctors. Finally, when the AI had the same diagnostic conditions with doctors, our model achieved a higher average F1 score (0.931) compared to the physicians (0.92). This study demonstrated that our model could obtain a more structured medical history and had a good diagnostic logic, which would help to improve the diagnostic accuracy of the outpatient doctors and reduce the misdiagnosis and missed diagnosis. But, our model still needs a good deal of training to obtain more accurate symptomatic information.Han QianHan QianBin DongBin DongJia-jun YuanJia-jun YuanFan YinZhao WangHai-ning WangHan-song WangHan-song WangDan TianDan TianDan TianWei-hua LiWei-hua LiWei-hua LiBin ZhangBin ZhangLie-bin ZhaoBo-tao NingBo-tao NingFrontiers Media S.A.articleartificial intelligencepre-consultationoutpatientmedical recordspediatricelectronic health recordMedicine (General)R5-920ENFrontiers in Medicine, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence pre-consultation outpatient medical records pediatric electronic health record Medicine (General) R5-920
spellingShingle	artificial intelligence pre-consultation outpatient medical records pediatric electronic health record Medicine (General) R5-920 Han Qian Han Qian Bin Dong Bin Dong Jia-jun Yuan Jia-jun Yuan Fan Yin Zhao Wang Hai-ning Wang Han-song Wang Han-song Wang Dan Tian Dan Tian Dan Tian Wei-hua Li Wei-hua Li Wei-hua Li Bin Zhang Bin Zhang Lie-bin Zhao Bo-tao Ning Bo-tao Ning Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics
description	Artificial intelligence (AI) has been deeply applied in the medical field and has shown broad application prospects. Pre-consultation system is an important supplement to the traditional face-to-face consultation. The combination of the AI and the pre-consultation system can help to raise the efficiency of the clinical work. However, it is still challenging for the AI to analyze and process the complicated electronic health record (EHR) data. Our pre-consultation system uses an automated natural language processing (NLP) system to communicate with the patients through the mobile terminals, applying the deep learning (DL) techniques to extract the symptomatic information, and finally outputs the structured electronic medical records. From November 2019 to May 2020, a total of 2,648 pediatric patients used our model to provide their medical history and get the primary diagnosis before visiting the physicians in the outpatient department of the Shanghai Children's Medical Center. Our task is to evaluate the ability of the AI and doctors to obtain the primary diagnosis and to analyze the effect of the consistency between the medical history described by our model and the physicians on the diagnostic performance. The results showed that if we do not consider whether the medical history recorded by the AI and doctors was consistent or not, our model performed worse compared to the physicians and had a lower average F1 score (0.825 vs. 0.912). However, when the chief complaint or the history of present illness described by the AI and doctors was consistent, our model had a higher average F1 score and was closer to the doctors. Finally, when the AI had the same diagnostic conditions with doctors, our model achieved a higher average F1 score (0.931) compared to the physicians (0.92). This study demonstrated that our model could obtain a more structured medical history and had a good diagnostic logic, which would help to improve the diagnostic accuracy of the outpatient doctors and reduce the misdiagnosis and missed diagnosis. But, our model still needs a good deal of training to obtain more accurate symptomatic information.
format	article
author	Han Qian Han Qian Bin Dong Bin Dong Jia-jun Yuan Jia-jun Yuan Fan Yin Zhao Wang Hai-ning Wang Han-song Wang Han-song Wang Dan Tian Dan Tian Dan Tian Wei-hua Li Wei-hua Li Wei-hua Li Bin Zhang Bin Zhang Lie-bin Zhao Bo-tao Ning Bo-tao Ning
author_facet	Han Qian Han Qian Bin Dong Bin Dong Jia-jun Yuan Jia-jun Yuan Fan Yin Zhao Wang Hai-ning Wang Han-song Wang Han-song Wang Dan Tian Dan Tian Dan Tian Wei-hua Li Wei-hua Li Wei-hua Li Bin Zhang Bin Zhang Lie-bin Zhao Bo-tao Ning Bo-tao Ning
author_sort	Han Qian
title	Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics
title_short	Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics
title_full	Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics
title_fullStr	Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics
title_full_unstemmed	Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics
title_sort	pre-consultation system based on the artificial intelligence has a better diagnostic performance than the physicians in the outpatient department of pediatrics
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/f94313959c2240faaa3a633229e4a3ba
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Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics

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