从现实世界的临床笔记中理解避孕药切换的理由,使用大型语言模型
Brenda Y Miao1, Christopher Y K Williams2, Ebenezer Chinedu-Eneh3
1Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA. miao.brenda1@gmail.com.
NPJ digital medicine
|April 24, 2025
概括
大型语言模型 (LLM) 可以从临床笔记中提取避孕药切换的原因. GPT-4在确定患者偏好,不良事件和保险作为关键因素方面表现得非常准确.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 药物监督 药物监督 药物监督
背景情况:
- 从非结构化的临床笔记中提取治疗转换的原因是具有挑战性的.
- 了解这些原因对于医学研究和患者护理至关重要.
研究的目的:
- 评估GPT-4和开源大型语言模型 (LLM) 在提取避孕药切换信息中的零射击能力.
- 通过使用先进的AI技术,识别影响避孕转换的关键因素.
主要方法:
- 在1964年临床笔记中对GPT-4和八个开源LLM进行零射击评估.
- 使用基于变压器的主题建模来识别切换原因.
- 临床专家对提取的信息进行准确性和幻觉率的评估.
主要成果:
- GPT-4获得了高的microF1评分 (0.85开始,0.88停止避孕药),超过了最好的开源模型.
- 在提取切换原因方面,GPT-4的准确性为91.4%,幻觉率低至2.2%.
- 确定的主要转换原因包括患者偏好,不良事件和保险覆盖.
结论:
- LLM,特别是GPT-4,是从临床笔记中提取复杂的治疗因素的宝贵工具.
- 这些发现提供了对现实世界避孕药切换行为的见解.
- 人工智能驱动的分析可以提高对治疗坚持和以患者为中心的护理的理解.
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