背景:电子健康档案的使用是整个医疗行业数字化和智能化的最重要里程碑。AI可以有效地挖掘EHR中包含的巨大医疗信息,有可能帮助医生减少许多医疗错误。
目的:本文旨在总结近十三年来利用AI从EHR中挖掘医学信息的研究现状和趋势,并探讨其信息应用。
方法:在5个数据库中进行了系统搜索,包括WebofScience核心合集和PubMed,在过去的13年里,确定使用人工智能从EHR中挖掘医疗信息的研究。此外,采用文献计量和内容分析的方法探讨了研究热点和趋势,并系统分析了该领域研究资源的转化率。
结果:共纳入631篇文献并进行分析。2017年后发表的文章数量快速增长,年均增长55.73%。美国(41.68%)和中国(19.65%)发表文章最多,但是缺乏国际合作。病变的提取是目前研究的热点,研究主题逐渐从疾病风险分级转向疾病风险预测。分类(66%),回归(15%)是主要实现的人工智能任务。对于AI算法,深度学习(31.70%),决策树算法家族(26.47%),和回归算法家族(17.43%)使用频率最高。出版物的资助率为69.26%,投入产出转化率为21.05%。
结论:在过去的十年中,使用人工智能从EHR中挖掘医疗信息的技术发展迅速。然而,有必要加强国际合作,提高EHR数据的可用性,专注于可解释的人工智能算法,并在未来的研究中提高资源转化率。
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.