Mining electronic health records using artificial intelligence: Bibliometric and content analyses for current research status and product conversion

Jun Liang; Yunfan He; Jun Xie; Xianming Fan; Yiqi Liu; Qinglian Wen; Dongxia Shen; Jie Xu; Shuo Gu; Jianbo Lei

doi:10.1016/j.jbi.2023.104480

Mining electronic health records using artificial intelligence: Bibliometric and content analyses for current research status and product conversion

J Biomed Inform. 2023 Oct:146:104480. doi: 10.1016/j.jbi.2023.104480. Epub 2023 Aug 30.

Authors

Jun Liang¹, Yunfan He², Jun Xie³, Xianming Fan⁴, Yiqi Liu⁵, Qinglian Wen⁶, Dongxia Shen⁷, Jie Xu⁸, Shuo Gu⁹, Jianbo Lei¹⁰

Affiliations

¹ IT 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.
² Center for Health Policy Studies, School of Public Health, Zhejiang University, Hangzhou, Zhejiang Province, China.
³ Information Technology Center, West China Hospital of Sichuan University/Engineering Research Center of Medical Information Technology, Ministry of Education, Chengdu, Sichuan Province, China.
⁴ Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China.
⁵ Department of Infectious Disease, Center for Liver Disease, Peking University First Hospital, Beijing, China.
⁶ Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China.
⁷ Editorial Department of Journal of Practical Oncology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China.
⁸ IT Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China.
⁹ Hainan Provincial Center for Neurological Diseases, Department of Pediatric Neurosurgery of The First Affiliated Hospital, Hainan Medical University, Haikou, Hainan Province, China. Electronic address: gushuo007@163.com.
¹⁰ Clinical Research Center, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China; School of Medical Information and Engineering, SouthWest Medical University, Luzhou, Sichuan Province, China; Institute of Medical Technology, Health Science Center, Peking University, Beijing, China. Electronic address: jblei@hsc.pku.edu.cn.

PMID: 37657713
DOI: 10.1016/j.jbi.2023.104480

Abstract

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.

Keywords: Achievement conversion; Artificial intelligence; EHRs; Information mining; Resource conversion rate.

Publication types

Review