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Mining electronic health records using artificial intelligence: Bibliometric and content analyses for current
Jun Liang1, Yunfan He2, Jun Xie3
1IT Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China; Center for Health Policy Studies, School of Public Health, Zhejiang University, Hangzhou, Zhejiang Province, China; Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China; School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China.
Background:
The use of Electronic Health Records is the most important milestone in the digitization and intelligence of the entire medical industry. AI can effectively mine the immense medical information contained in EHRs, potentially assist doctors in reducing many medical errors.
Objective:
This article aims to summarize the research status and trends in using AI to mine medical information from EHRs for the past thirteen years and investigate its information application.
Methods:
A systematic search was carried out in 5 databases, including Web of Science Core Collection and PubMed, to identify research using AI to mine medical information from EHRs for the past thirteen years. Furthermore, bibliometric and content analysis were used to explore the research hotspots and trends, and systematically analyze the conversion rate of research resources in this field.
Results:
A total of 631 articles were included and analyzed. The number of published articles has increased rapidly after 2017, with an average annual growth rate of 55.73%. The US (41.68%) and China (19.65%) publish the most articles, but there is a lack of international cooperation. The extraction of disease lesions is a hot topic at present, and the research topic is gradually shifting from disease risk grading to disease risk prediction. Classification (66%), and regress (15%) are the main implemented AI tasks. For AI algorithms, deep learning (31.70%), decision tree algorithms family (26.47%), and regression algorithms family (17.43%) are used most frequently. The funding rate for publications is 69.26%, and the input-output conversion rate is 21.05%.
Conclusions:
Over the past decade, the use of AI to mine medical information from EHRs has been developing rapidly. However, it is necessary to strengthen international cooperation, improve EHRs data availability, focus on interpretable AI algorithms, and improve the resource conversion rate in future research.
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