利用混合自然语言处理技术进行大规模的肺栓塞识别
Syed Moin Hassan1, Ruben Mylvaganam2, Tekle Didebulidze3
1Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
JACC. Advances
|December 5, 2025
概括
一种混合自然语言处理 (NLP) 方法在放射学报告中准确识别肺栓塞 (PE). 这种方法结合了机器学习和规则,提高了诊断效率和临床监测.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 分析健康数据 分析健康数据
背景情况:
- 肺栓塞 (PE) 诊断和管理仍然具有挑战性,尽管医学的进步.
- 大规模的电子健康记录分析对于了解PE病理生理学和优化治疗至关重要.
- 混合自然语言处理 (NLP) 为此分析提供了一个有前途的方法.
研究的目的:
- 开发和验证混合NLP管道,以准确识别PE病例.
- 该管道结合了机器学习 (ML) 和基于规则的技术.
- 目标是处理大规模的放射学报告数据集.
主要方法:
- 一个ML算法被训练在1040个计算机断层扫描肺血管造影报告上.
- 该管道在马萨斯州布里格姆总医院 (MGB) 医疗保健系统的49,611份放射学报告中得到验证.
- 性能指标包括准确性,敏感性,特异性,PPV和NPV.
主要成果:
- 最初的ML模型在测试数据上实现了91%的准确性.
- 在MGB数据集上部署显示了85%的准确性,在应用基于规则的算法后,准确性提高到94.8%.
- 最终的混合模型在MGB数据集上显示出高灵敏度 (96.4%) 和特异性 (93.2%).
结论:
- 混合NLP方法需要的训练数据比纯ML模型少.
- 它在各种各样的医疗保健环境中表现出了高绩效.
- 这个管道可以有效地识别PE病例,用于研究和临床监测.
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