从临床记录中自动提取皮肤和软组织感染状况
Jamie L W Rhoads1,2, Lee Christensen3, Skylar Westerdahl1
1Dept. Dermatology, University of Utah, Salt Lake City, UT, USA.
Studies in health technology and informatics
|January 25, 2024
概括
这项研究开发了一个自然语言处理系统,从电子健康记录中自动识别和分类皮肤和软组织感染 (SSTI) 亚型,提高研究和质量改进的数据可靠性.
科学领域:
- 医疗信息学 医疗信息学
- 临床自然语言处理 临床自然语言处理
- 公共卫生 公共卫生
背景情况:
- 从电子健康记录 (EHR) 中准确识别皮肤和软组织感染 (SSTI) 对质量改进,临床指南开发和流行病学分析至关重要.
- 目前美国的结构化电子健康记录数据无法可靠地捕捉SSTI亚型,例如性vs非性感染.
研究的目的:
- 开发和评估基于规则的临床自然语言处理 (NLP) 系统,用于从临床笔记中自动提取和分类SSTI亚型.
- 解决结构化数据的局限性,以捕获详细的SSTI信息.
主要方法:
- 接受了基于规则的临床NLP系统的培训和评估.
- 利用来自美国退伍军人卫生管理局 (VA) 的65,76份手动注释的临床笔记.
- 专注于自动提取和分类SSTI亚型.
主要成果:
- 该系统实现了提及级性能指标,范围从0.39到0.80.
- 文档级分类性能在0.49到0.98.8之间.
- 证明了使用NLP用于详细的SSTI亚型的可行性.
结论:
- 基于规则的临床NLP系统可以有效地从非结构化的临床笔记中提取和分类SSTI亚型.
- 这种方法提高了SSTI数据的可靠性,这些数据来自EHRs.
- 这些发现支持改善用于临床和研究应用的数据利用.
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