开发和验证VTE-BERT自然语言处理模型用于静脉血栓栓塞.
Omid Jafari1, Shengling Ma1, Barbara D Lam2
1Section of Hematology-Oncology, Baylor College of Medicine, Houston, TX.
Journal of thrombosis and haemostasis : JTH
|August 3, 2025
概括
一个新的自然语言处理 (NLP) 模型,VTE-BERT,准确地检测癌症患者的静脉血栓栓塞 (VTE) 事件. 该工具增强了用于大规模血栓塞瘤研究的VTE检测.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 临床研究 临床研究
背景情况:
- 静脉血栓栓塞 (VTE) 的准确和及时的表型确定对于纵向研究至关重要.
- 目前还缺少一个验证的自然语言处理 (NLP) 工具,用于在真实世界患者群体中检测VTE.
研究的目的:
- 开发和外部验证一个新的NLP平台NLPMed,以促进血栓瘤研究.
- 创建和验证VTE-BERT,一种专门的NLP模型,用于检测急性VTE事件及其纵向解剖位置.
主要方法:
- 开发了一个新的NLP平台NLPMed,用于数据预处理,注释和模型微调.
- 生物_临床BERT是使用癌症患者的临床笔记进行微调,以创建VTE-BERT.
- 该VTE-BERT模型在两个医疗机构的患者队列中经过了内部和外部验证.
主要成果:
- 在训练期间,VTE-BERT实现了高性能,达到95%的精度和98%的回忆.
- 内部验证表明95%的精度和91%的回忆率.
- 在一个独立的数据集中进行外部验证,结果为85%的精度和92%的回忆率.
结论:
- 一个高效的NLP模型,VTE-BERT,已成功训练并获得外部验证,用于纵向检测VTE事件.
- 预计VTE-BERT的采用将通过改进大规模的VTE检测来加速血栓瘤研究.
- 这种NLP工具可以减少与大数据流行病学研究中手动图表审查相关的时间和成本.
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