在初级保健笔记中提取静脉血栓塞栓症症状的工具,以促进电子临床质量测量报告:算法开发和验证研究
John Novoa-Laurentiev1, Mica Bowen1, Avery Pullman1
1Department of Medicine, Brigham & Women's Hospital, 75 Francis Street, Boston, MA, 02115, United States, 1 8572824088.
JMIR medical informatics
|August 26, 2025
概括
一个新的自然语言处理 (NLP) 工具,VTExt,准确地从临床记录中提取静脉血栓塞 (VTE) 症状. 这样可以在初级保健中更好地测量和及时诊断VTE.
科学领域:
- 自然语言处理 (NLP)
- 临床信息学
- 医疗质量测量
背景情况:
- 延迟诊断静脉血栓塞栓症 (VTE) 会增加患者的发病率和死亡率.
- 临床笔记包含了及时诊断和测量VTE质量的关键信息,但从非结构化的文本中提取信息是具有挑战性的.
- 目前电子临床质量测量 (eCQM) 缺乏NLP数据提取,限制了准确性和效率.
研究的目的:
- 开发一种NLP工具 (VTExt) 来从初级保健临床笔记中提取VTE症状.
- 将该工具整合到eCQM中,以量化延迟的静脉瘤诊断.
主要方法:
- 使用内部数据集代开发基于规则的NLP工具 (VTExt).
- 在医生指导下创建和优化VTE症状词汇.
- 通过两个独立的医疗保健机构进行外部验证.
- 使用AUC,PPV,NPV,灵敏度和特异性等指标进行严格的性能评估.
主要成果:
- 在从初级保健笔记中提取VTE症状时,VTExt表现近乎完美.
- 该工具在不同机构和电子健康记录系统的外部验证方面取得了有希望的结果.
- VTExt的性能与深度学习和机器学习模型相美.
结论:
- 这项研究提出了一种可通用的NLP方法,用于从各种电子健康记录系统中提取临床信息.
- VTExt是第一个纳入国家认可的eCQM的NLP应用程序.
- 这种工具提高了在初级保健中精确检测和质量测量的潜力.
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