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优化自然语言处理工具的精度,通过与索赔数据的整合来检测肺栓塞:PE-EHR+研究
Sina Rashedi1, Syed Bukhari2, Darsiya Krishnathasan1
1Thrombosis Research Group, Brigham and Women's Hospital, Boston, United States.
Thrombosis and haemostasis
|January 28, 2026
概括
基于规则的自然语言处理 (NLP) 工具可以在放射学报告中检测肺栓塞 (PE). 将NLP与行政索赔数据相结合,可以显著提高PE检测的准确性.
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
背景情况:
- 基于规则的自然语言处理 (NLP) 显示出在放射学报告中识别肺栓塞 (PE) 的前景.
- 这些NLP工具在各种临床环境中的外部有效性和最佳应用需要进一步研究.
研究的目的:
- 评估两个NLP算法的外部有效性,用于在一个大型的多医院卫生系统中检测PE.
- 将NLP在所有放射学报告中应用时的性能与局限于行政数据 (ICD-10代码和当前入院指标) 的性能进行比较.
主要方法:
- 一项横截面研究分析了1712名在Mass General Brigham医院的PE和没有PE的住院患者 (2016-2021).
- 两种NLP算法应用于放射学报告,医生图表审查作为参考标准.
- 测试了三种方法: (A) 对所有报告的NLP, (B) 对具有PE排放代码的报告的NLP,以及 (C) 对具有PE排放代码或PE入学时呈现指标的报告的NLP.
主要成果:
- 方法A (所有报告) 显示高灵敏度但低的积极预测值 (PPV).
- 方法B (排放代码) 改善了PPV,但降低了灵敏度.
- 方法C (排放码+POA指标) 实现了高灵敏度和PPV,为两种NLP工具提供了最好的F1得分 (88.6%,94.4%),明显优于方法A和B (P<0.001).
结论:
- 将行政索赔数据,特别是ICD-10代码和当前入院指标与放射学报告的NLP分析相结合,可以显著提高肺栓塞检测的准确性.
- 这种结合的方法提供了一个更强大的和外部有效的方法来识别PE,而不是使用NLP仅用于放射学报告.
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