自然语言处理自动化脑血管事件识别在中风警报中.
Asala N Erekat1,2, Laura K Stein2, Bradley N Delman3
1Clinical Neuro-Informatics Center Icahn School of Medicine at Mount Sinai New York NY.
Stroke (Hoboken, N.J.)
|January 29, 2026
概括
弱标签成功注释了大型中风警报注册表,提高了机器学习模型的效率. 这种方法可以通过准确识别中风预警来减少医疗保健资源压力.
科学领域:
- 临床信息学是一种临床信息学.
- 机器学习在医疗保健中的应用
- 脑血管疾病研究研究
背景情况:
- 频繁的错误阳性中风警报会给医疗保健资源带来压力.
- 机器学习模型可以预测中风警报的准确性,但需要广泛的手动数据标签.
- 软标签提供了一种方法,可以通过启发式规则加速机器学习的开发.
研究的目的:
- 标记一个大,未标记的样本中风警报使用弱标签.
- 确定中风警报的二元结果 (急性脑血管疾病的存在/不存在).
- 评估中风预警的弱标签框架的性能.
主要方法:
- 开发了一个四层层的等级弱标签启发式组合.
- 在放射学报告 (一级) 上使用基于规则的命名实体识别 (一级).
- 聚合的输出,结合的诊断代码和组合的标签,以获得最终的遭遇级结果 (2-4级).
主要成果:
- 分析了16512次中风预警活动 (2011-2021年).
- 一级 (报告) 实现了0.84的敏感性,0.96的特异性,0.87的F1.1.
- 4级 (最终标签) 获得了0.92的灵敏度,0.86的特异性,0.87的F1.1.
结论:
- 通过使用弱标签成功标记了一大批中风警报登记册.
- 开发的框架显示了有效的临床数据注释的潜力.
- 这种方法可以扩展到用于机器学习应用的其他临床数据集.
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