通过自然语言处理和概率语言模型进行可扩展的事件检测
medRxiv : the preprint server for health sciences
|December 11, 2023
概括
这项研究引入了一种新方法,使用临床笔记上的自然语言处理 (NLP) 来识别患者事件,如自杀未遂和睡眠行为,改进了超越结构化数据的安全监测.
科学领域:
- 医疗信息学 医疗信息学
- 临床信息学 临床信息学
- 在医疗保健中的自然语言处理.
背景情况:
- 营销后安全监测对于大规模检测临床事件至关重要.
- 电子健康记录 (EHR) 中的结构化数据 (例如诊断代码) 可能对监控不准确.
- 非结构化的临床文本提供了更丰富的数据来源,但需要先进的分析.
研究的目的:
- 开发和验证一种使用非结构化临床文本数据的新型事件表型方法.
- 为了证明这种方法在不同临床表型的普遍性.
- 评估表型化方法的性能,包括潜在的种族差异.
主要方法:
- 开发了一种基于验证方法 (PheRe) 的新型表型方法,用于分析非结构化EHR数据.
- 用大型数据集验证了两种表型的方法:自杀企图和与睡眠有关的行为.
- 根据银标准 (诊断编码) 和黄金标准 (手动图表审查) 进行验证.
主要成果:
- 为了自杀企图表型化,实现了精度回忆曲线 (AUPR) 下的面积为0.77.
- 观察到睡眠相关行为表型的AUPR为~0.31.
- 根据编码的种族确定了性能变化,表明需要对算法保持警和缓解.
结论:
- 开发的NLP方法有效地利用非结构化的临床文本进行可扩展的事件表型化.
- 该方法有望通过克服结构化数据的局限性来加强营销后安全监测.
- 需要进一步的工作来解决算法偏见,并确保在多样化的患者群体中提供公平的表现.
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