使用自然语言处理在紧急医疗卫生服务研究:一个系统的审查和元分析
Hao Wang1, Naomi Alanis1, Laura Haygood2
1Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA.
概括
自然语言处理 (NLP) 有效地对紧急医疗数据进行结构化. NLP表现出高精度,特别是在放射学解释方面,支持其在紧急护理研究中的应用.
科学领域:
- 人工智能和机器学习
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 在紧急医疗 (EM) 中,非结构化数据带来了挑战.
- 自然语言处理 (NLP) 提供了一个结构化这些数据的方法.
研究的目的:
- 评估NLP在EM中识别和分类非结构化数据方面的表现.
- 评估NLP在特定EM研究应用中的准确性.
主要方法:
- 在主要数据库 (MEDLINE,Embase,Scopus等) 进行系统的文献搜索. ) 的情况.
- 27项选定的研究的元分析.
- 将NLP应用分类为综合征监测,放射学解释和疾病识别.
- 计算性能指标,包括灵敏度,特异性和接收器操作特征曲线 (SROC) 总结下的区域.
主要成果:
- 总体平均灵敏度为82%-87%,特异性为95%.
- 在SROC下的面积为0.96 (95%CI为0.94-0.98).
- 在放射学解释中,NLP表现出最佳的性能,灵敏度为93%,特异性为96%.
结论:
- 在紧急医疗研究中,NLP表现出良好的性能准确性.
- 在EM中,NLP对于放射学解释特别有效.
- 建议采用基于NLP的研究来加强EM医疗管理.
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