Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19.
10.7507/1001-5515.202003045
- Author:
Xiaorong PU
1
,
2
;
Kecheng CHEN
1
,
2
;
Junchi LIU
1
,
2
;
Jin WEN
3
;
Shangwei ZHNENG
3
;
Honghao LI
3
Author Information
1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P.R.China
2. Health Big Data Institute of Big Data Center, University of Electronic Science and Technology of China, Chengdu 611731, P.R.China.
3. Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu 610041, P.R.China.
- Publication Type:Journal Article
- Keywords:
COVID-19;
artificial intelligence;
biomedical engineering;
diagnosis and treatment plan;
machine learning;
novel coronavirus
- MeSH:
Betacoronavirus;
China;
Coronavirus Infections;
diagnosis;
therapy;
Humans;
Machine Learning;
Pandemics;
Pneumonia, Viral;
diagnosis;
therapy;
Practice Guidelines as Topic
- From:
Journal of Biomedical Engineering
2020;37(3):365-372
- CountryChina
- Language:Chinese
-
Abstract:
The outbreak of pneumonia caused by novel coronavirus (COVID-19) at the end of 2019 was a major public health emergency in human history. In a short period of time, Chinese medical workers have experienced the gradual understanding, evidence accumulation and clinical practice of the unknown virus. So far, National Health Commission of the People's Republic of China has issued seven trial versions of the "Guidelines for the Diagnosis and Treatment of COVID-19". However, it is difficult for clinicians and laymen to quickly and accurately distinguish the similarities and differences among the different versions and locate the key points of the new version. This paper reports a computer-aided intelligent analysis method based on machine learning, which can automatically analyze the similarities and differences of different treatment plans, present the focus of the new version to doctors, reduce the difficulty in interpreting the "diagnosis and treatment plan" for the professional, and help the general public better understand the professional knowledge of medicine. Experimental results show that this method can achieve the topic prediction and matching of the new version of the program text through unsupervised learning of the previous versions of the program topic with an accuracy of 100%. It enables the computer interpretation of "diagnosis and treatment plan" automatically and intelligently.
