一份关于从临床笔记中提取症状发病日期和时间的试点报告,该报告涉及患有胸痛的患者
Anjaly George1, Aashrith Maisa1, Caitlin Dreisbach1,2
1Goergen Institute for Data Science, University of Rochester.
从电子健康记录中提取症状发病时间对于急性冠状动脉综合征 (ACS) 患者的护理至关重要. 试点测试显示,parsedatetime NLP 工具部分成功,性能优于 regex,但需要进一步开发.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 心脏病学 心脏病学
背景情况:
- 急性冠状动脉综合征 (ACS) 是一种快速发展的心脏病.
- 无ST段升高ACS (NSTE-ACS) 的预后可以通过症状发作的时间来告知.
- 从临床笔记中准确地提取症状发作是具有挑战性的.
研究的目的:
- 试点测试自然语言处理 (NLP) 管道,以提取患者报告的症状发病日期和时间 (DateTime).
- 在临床笔记上评估"parsedatetime"和"正则表达" (regex) NLP工具的性能.
主要方法:
- 两个NLP管道,parsedatetime和regex,在71个临床笔记上进行了测试 (历史和物理,急诊部查,分离笔记).
- 对胸痛症状的提取日期时间信息与手动注释的真实发病时间进行了比较.
- 基于成功提取日期和时间的次数来评估性能.
主要成果:
- 在71个笔记中,Parsedatetime成功地从36个笔记中提取了DateTime信息 (50.7%).
- 该regex管道产生了零匹配的日期时间输出.
- 与regex相比,parsedatetime表现出更高的性能,尽管结果不足于最佳.
结论:
- 像parsedatetime这样的现成NLP管道显示出潜力,但需要显著的定制才能从EHR中准确地提取DateTime.
- 为了改善NSTE-ACS患者管理,需要对从临床笔记中大规模提取DateTime的自动化方法进行进一步的研究.
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