自动化自然语言处理用于从诊断成像报告中识别静脉血栓栓塞
Naveen Subramanian1, Hector Garcia Pleitez2, Daniel Nguyen1
1Department of Internal Medicine, The University of Texas McGovern Medical School, Houston, TX.
Blood vessels, thrombosis & hemostasis
|January 8, 2026
概括
自然语言处理 (NLP) 算法可以有效地检测血造干细胞移植 (HSCT) 后患者的静脉血栓栓塞 (VTE). 这种自动化方法改进了手动图表检查,用于识别VTE风险因素.
科学领域:
- 血液学 血液学 血液学
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 同源性造血干细胞移植 (HSCT) 与静脉血栓栓塞 (VTE) 的风险显著增加有关,可能高达50倍.
- 准确的VTE发病率数据和在HSCT患者中识别相关的临床风险因素传统上依赖于耗时的手动图表审查.
- 自然语言处理 (NLP) 为电子医疗记录 (EMR) 系统中的VTE检测提供了一个有希望的自动化解决方案.
研究的目的:
- 开发和评估一个机构NLP算法来检测急性VTE在接受HSCT的患者.
- 将NLP算法的性能与手动图表审查进行比较,用于在HSCT后100天内识别VTE.
- 在这个特定的患者群体中评估NLP工具的准确度指标 (灵敏度,特异性,PPV,NPV).
主要方法:
- 从2016年到2020年对成年患者病历进行了回顾性分析.
- 从EMR数据开发NLP算法,以从EMR数据中识别急性VTE在HSCT后100天内.
- 基于NLP的VTE检测与传统的手动图表检查进行性能评估的比较.
主要成果:
- 总共分析了1300份电子健康记录.
- 100天VTE发生率是10.3%的手动审查和8.8%的NLP.
- NLP算法表现出高性能,特异性,灵敏度,PPV和NPV超过0.85,其中小差异归因于被忽视的报告类型.
结论:
- 开发的NLP算法在识别HSCT患者的VTE方面表现出色.
- 与手动图表审查相比,NLP显著提高了VTE检测的效率,在这个高风险队列中.
- 未来的改进和NLP与其他方法的整合可以进一步改善在HSCT和其他风险人群中的VTE检测.
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