自然语言处理在急诊室分拣中的应用:一个叙事审查
Jonathon Stewart1,2,3, Juan Lu1,2,4, Adrian Goudie3
1School of Medicine, The University of Western Australia, Crawley, Western Australia, Australia.
PloS one
|December 14, 2023
概括
自然语言处理 (NLP) 模型可以使用紧急部门 (ED) 的分拣笔记来预测结果. 虽然结合自由文本和结构化数据可以改善预测,但大多数研究显示偏差风险很高,临床实施有限.
科学领域:
- 计算语言学计算语言学
- 医疗信息学医学信息学
- 临床预测建模临床预测建模
背景情况:
- 自然语言处理 (NLP) 通过计算分析人类语言.
- 为了预测结果,NLP已经应用于紧急部门 (ED) 的分拣数据.
- 本综述评估了NLP在ED分拣中的应用,将模型性能与人类预测和现有方法进行比较.
研究的目的:
- 为了评估NLP应用程序的ED分拣数据.
- 评估NLP模型是否优于人类或当前的风险分层.
- 为了确定自由文本数据是否能比单独的结构化数据提高预测性能.
主要方法:
- 范围审查英语语言的同行评审研究.
- 纳入标准:NLP应用于ED分类中的自由文本.
- 排除标准:疾病监测,分拣后数据;搜索了5个主要数据库;使用PROBAST评估了偏差风险;由于异质性而进行叙述综合.
主要成果:
- 从3730项选中,包括20项研究,患者人群多样化.
- NLP模型准确地预测了入院,分拣得分和危急疾病.
- 将自由文本与结构化数据相结合,提高了模型性能.
- 80%的研究具有高偏差风险;只有一个报告临床部署.
结论:
- NLP模型有效地预测了非结构化的ED分选笔记的结果.
- 偏差和回顾性质的高风险限制了当前的发现.
- 未来的研究需要前性研究来验证NLP对ED结果的临床影响.
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