加强大动脉动脉瘤监测:转换器自然语言处理用于放射学报告中的标记和测量
William Kartsonis1, Paola Pastena2, Janos Hajagos3
1Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Annals of vascular surgery
|October 18, 2024
概括
基于转换器的自然语言处理 (NLP) 能够有效地检测出大动脉动脉瘤 (AA) 并识别失踪的患者,改善监测和患者管理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 放射学 信息学 信息学
背景情况:
- 偶发性大动脉动脉瘤 (AA) 发现往往被忽视,患者经常缺乏后续检查.
- 传统方法在报告和患者跟踪方面扎.
- 基于变压器的大型语言模型 (LLM) 为应对这一挑战提供了先进的NLP功能.
研究的目的:
- 评估一种基于变压器的新型NLP管道用于大动脉动脉瘤 (AA) 检测.
- 量化临床影响和受益于这项技术的患者数量.
- 通过协调后续预约改善患者的监测和管理.
主要方法:
- 使用基于变压器的NLP管道处理了3,229份放射学报告,具有"查询和查找" (Q&F) 功能.
- 提取的大动脉直径测量,标记那些超过值的人.
- 进行了图表审查,并与患者/PCP联系,以对发现进行分类,并评估随访遵守情况.
主要成果:
- 在三个批次中实现了高F1得分 (96.7%-99.4%) 和测量精度 (98.1%-99.6%).
- 与手动审查相比,NLP系统显示出更高的准确性 (例如,47对98个错误,P < 0.000021).
- 确定了14.1%的偶然发现,15.3%的患者失去了随访,并协调了12.1%的随访预约.
结论:
- 基于变压器的NLP显著提高了大动脉动脉瘤 (AA) 监测.
- 该系统在电子医疗记录中起到至关重要的安全作用,用于检测偶然发现和丢失的患者.
- 通过及时干预和监测,改善患者的治疗结果和医疗保健效率.
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