识别与中风风险相关的神经文本标记物的识别
Anoop Mayampurath1, Avery Rosado2, Elida Romo2
1Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI.
概括
自然语言处理在急诊室 (ED) 中识别了文字标记,这些标记表明中风的呈现. 这些发现可以帮助预防推迟中风诊断,并改善患者的治疗结果.
科学领域:
- 医疗信息学医学信息学
- 神经学 神经学
- 紧急医疗 紧急医疗
背景情况:
- 推迟中风诊断导致患者的治疗结果不佳.
- 自然语言处理 (NLP) 可以分析紧急部门 (ED) 的笔记.
- 在非神经学ED访问中识别早期中风指标至关重要.
研究的目的:
- 为了识别ED笔记中的文本短语,这些笔记表明了中风的呈现.
- 要将这些短语与中风住院30天内联系起来.
- 开发一种预防中风延迟诊断的工具.
主要方法:
- 对中风和对照患者的回顾性分析,这些患者之前曾经经历过ED.
- 从ED笔记中提取医学概念,使用NLP.
- 统计分析用于在发现队列中识别神经学文本标记,并在单独的队列中验证.
- 弹性网模型对预测性能和通用性的比较.
主要成果:
- 确定了58个与中风病例有显著关联的医学概念.
- 开发和验证了11个结合医疗概念的文字标记器.
- 这些标记在两个学术医疗中心证明了外部有效性和通用性.
- 弹性净模型显示了同等的预测性能.
结论:
- 在诊断前的ED遭遇中检测和验证了特征性中风的神经文本标记.
- 这些标记可以促使进一步的神经学评估.
- 实施可能会防止推迟中风诊断,并改善结果.
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