关于电子健康记录的问题和答案:对数据集和模型的范围审查
Jayetri Bardhan1, Kirk Roberts2, Daisy Zhe Wang1
1Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, United States.
Journal of medical Internet research
|October 30, 2024
概括
本次对电子健康记录 (EHR) 问答系统 (QA) 的审查强调了该领域的新性和挑战. 未来的研究应该解决数据的局限性,并改善现实的EHR质量保证数据集生成.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 健康 数据科学 数据科学
背景情况:
- 用于患者数据的问答 (QA) 系统通过改善决策和了解病史来帮助临床医生和患者.
- 电子健康记录 (EHR) 包含大量的患者数据,使EHR质量保证成为与医学文献质量保证不同的一个关键研究领域.
- 电子健康记录中独特的数据格式和模式需要专门的研究方法来实现有效的质量保证.
研究的目的:
- 进行对EHR质量保证现有研究的方法审查.
- 识别和分析EHR质量保证数据集和最先进的方法.
- 为了比较评估指标,并确定在EHRQA.挑战.
主要方法:
- 从2005年1月到2023年9月,在四个数字数据库 (Google Scholar,ACL Anthology,ACM数字图书馆,PubMed) 进行系统的文献搜索.
- 根据PRISMA指南,对4111篇鉴定论文进行了选,最终有47篇论文被选中.
- 选择的研究被分类为"EHR质量保证数据集"和"EHR质量保证模型".
主要成果:
- 该审查确定了47篇关于EHR质量保证的论文,其中大多数工作是最近的,表明了一个新兴的研究领域.
- emrQA是最常被引用和使用的EHRQA数据集;MIMIC-III和n2c2数据集是流行的EHR数据库.
- 分析涵盖了各种EHR质量保证模型,它们的方法和评估指标.
结论:
- 电子卫生健康质量保证研究面临诸多挑战,包括有限的临床注释和概念规范化.
- 产生现实的EHR质量保证数据集仍然是一个重大障碍.
- 本次审查确定了研究缺口,并建议了EHRQA的未来方向.
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