通过上下文识别查询和索引增强临床信息检索
Andrew Wen1, Yanshan Wang1, Vinod C Kaggal2
1Division of Digital Health Sciences Mayo Clinic, Rochester MN, USA.
概括
这项研究通过结合临床背景来增强电子健康记录 (EHR) 的搜索,提高了检索相关性高达23.1%,查询速度提高了35%. 这种先进的信息检索 (IR) 方法促进了临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 信息检索 信息检索
- 临床数据科学 临床数据科学
背景情况:
- 电子健康记录 (EHR) 的数量不断增加,需要为临床实践,研究和管理提供高效的搜索能力.
- 一般信息检索 (IR) 方法难以应对临床数据的复杂性,无法考虑否定和时间性等上下文因素.
- 在护理点准确识别相关的临床叙述对于有效的决策至关重要.
研究的目的:
- 提高搜索大规模电子健康数据的准确性和效率.
- 通过结合语义上下文来解决临床领域一般IR方法的局限性.
- 评估整合上下文信息对检索质量,执行时间和存储要求的影响.
主要方法:
- 使用Elasticsearch实现了一个解决方案,将语义上下文 (否定,时间性,主体) 纳入IR索引和评分中.
- 进行实验,将新方法与基线方法进行比较.
- 评估了对检索质量 (相关性),查询执行时间和存储需求的影响.
主要成果:
- 在检索质量 (相关性) 中实现了5.1-23.1%的改进.
- 与基线方法相比,显示了35%更快的查询执行时间.
- 观察到空间需求增加了1.5-2倍,索引时间增加了3倍.
结论:
- 整合上下文信息显著提高了从EHR中检索临床信息 (IR) 的相关性.
- 拟议的解决方案显示出有希望的可扩展性,随着时间和空间成本的可控增加.
- 这种方法为临床决策支持系统和医学研究提供了宝贵的进步.
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