使用大型语言模型了解口服抗凝药在心房的原因
Sulaiman Somani1, Dale Daniel Kim2, Eduardo Perez-Guerrero1
1Department of Medicine Stanford University Stanford CA USA.
Journal of the American Heart Association
|March 27, 2025
概括
在心房的患者中,口服抗凝剂 (OAC) 的高率持续存在. 大型语言模型确定了关键原因,包括抗血小板使用和治疗惯性,提供了改善指南推护理的见解.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 临床信息学 临床信息学
背景情况:
- 口服抗凝剂 (OAC) 在大约50%的心房患者中处方不足.
- 了解OAC非处方的原因对于改善符合指南的护理至关重要.
- 这项研究利用先进的AI来分析OAC非处方驾驶员的临床笔记.
研究的目的:
- 在心房动 (AF) 患者中识别和分类口服抗凝剂 (OAC) 非处方的记录原因.
- 评估大型语言模型 (LLM) 在从临床笔记中提取这些原因的性能.
- 发现指南推的AF管理中的潜在缺陷.
主要方法:
- 确定了大量没有OAC处方的心房动患者队列.
- 临床注释被注释为OAC非处方原因的人类审稿人.
- 生成预训练变压器4 (GPT-4) 和临床BERT被训练并比较了预测原因的准确性.
主要成果:
- 分析了超过3.5万名患者,其中21.6%不是OAC.
- 与临床BERT (0.69) 相比,GPT-4显示出更高的性能 (宏观F1:0.79).
- 通常记录的原因包括抗血小板使用 (23.3%),治疗惯性 (21.0%) 和低AF负担 (17.1%).
结论:
- 这项研究开创了LLM的使用,以从AF患者的临床笔记中提取OAC非处方原因.
- 调查结果突出了与指导方针不一致的做法,并为干预提供了可操作的见解.
- 人工智能驱动的分析可以为卫生系统的策略提供信息,以减少OAC的不足利用.
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