使用人工智能挖掘电子健康记录:对当前研究状况和产品转换进行文献计量和内容分析
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.
人工智能 (AI) 正在快速推进对电子健康记录 (EHR) 的分析,以获得医学见解. 研究表明,人工智能应用在EHR数据挖掘方面激增,重点是疾病预测和病变提取.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数据科学数据科学数据科学
背景情况:
- 电子健康记录 (EHR) 代表了医疗数字化和智能化的重大进步.
- 人工智能 (AI) 提供了强大的能力,可以从EHR中提取有价值的医疗信息.
- 在EHR中的AI有可能显著减少医疗错误并改善患者护理.
研究的目的:
- 在过去的13年里,系统地审查和分析人工智能应用的研究状态和趋势,从电子健康记录中挖掘医疗信息.
- 调查人工智能驱动的EHR数据分析的信息应用和转换率.
- 在这个快速发展的领域中确定当前的研究热点和未来的方向.
主要方法:
- 在五个主要数据库中进行了全面的系统搜索,包括Web of Science核心集合和PubMed.
- 用文献计量分析和内容分析来检查631篇已识别的研究文章.
- 该研究重点关注了过去13年的趋势,分析了出版物增长,地理分布,研究主题,AI任务,算法和融资率.
主要成果:
- 自2017年以来,EHR中关于AI的发表研究已经呈指数级增长 (55.73%的年增长).
- 美国和中国在出版方面处于领先地位,国际合作有限.
- 目前的研究重点是疾病病变提取,转向疾病风险预测,其中深度学习和决策树算法占主导地位.
结论:
- 对于EHR数据挖掘的AI领域正在经历快速发展.
- 未来的研究应该优先加强国际合作,提高电子健康记录数据的可访问性,并开发可解释的AI算法.
- 在人工智能驱动的EHR分析中提高资源转换效率对于未来的进步至关重要.
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