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
Colin G Walsh1,2,3,4, Drew Wilimitis5, Qingxia Chen5,6
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. Colin.walsh@vumc.org.
Scientific reports
|October 8, 2024
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) 来识别诸如自杀企图和睡眠行为等健康事件. 这种方法对大规模的安全监测有希望,但需要仔细监测偏差.
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.
- 药物监督 药物监督 药物监督
背景情况:
- 营销后安全监测对于检测临床事件至关重要.
- 目前使用结构化数据的方法在准确性和完整性方面存在局限性.
- 自然语言处理 (NLP) 提供了分析非结构化临床文本的潜力.
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.
- 评估方法在不同表型 (自杀企图,与睡眠有关的行为) 中的概括性.
- 评估表型模型的性能,包括潜在的种族差异.
主要方法:
- 开发了一种基于验证的方法 (PheRe) 的新型表型方法,用于分析整个医疗记录.
- 使用了非结构化的临床文本数据,无关电子健康记录 (EHR) 和笔记类型.
- 通过使用银和金标准,对自杀企图 (89,428条记录) 和睡眠相关行为 (35,863条记录) 的大型数据集进行了验证.
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.
- 通过表型和编码的种族识别了表现差异,突出了对算法警和调解的需求.
结论:
- 开发的基于NLP的表型化方法是一种可扩展的方法,用于从非结构化文本中识别临床事件.
- 该模型表明可通用性,但在实施医疗保健AI之前需要仔细验证和偏见评估.
- 需要进一步的研究来解决绩效差异,并确保这些AI工具的公平应用.
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