评估大型语言模型支持的药物使用说明:迈向全面模型的第一步
Zilma Silveira Nogueira Reis1, Adriana Silvina Pagano2, Isaias Jose Ramos de Oliveira1
1Health Informatics Center, Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Mayo Clinic proceedings. Digital health
|December 16, 2024
概括
大型语言模型 (LLM) 可以为电子处方生成更清晰,个性化的药物说明. 快速工程显著提高了输出质量和减少了偏差,增强了患者的沟通.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床交流 临床沟通
背景情况:
- 电子处方系统旨在提高药物安全性和效率.
- 目前的电子处方 (电子处方) 指令往往缺乏清晰度和个性化.
- 大型语言模型 (LLM) 提供了增强药物信息传递的潜力.
研究的目的:
- 评估大型语言模型 (LLM) 在生成电子处方中改进的药物说明的有效性.
- 评估不同提示策略对LLM生成指令的清晰度,个性化和偏差的影响.
- 通过更好的药物沟通,增强患者的理解和坚持.
主要方法:
- 为药物说明制定了以患者为中心的指导方针.
- 创建了一个基于巴西电子处方标准的104个门诊病情的数据集.
- 利用闭源和开源的LLM,使用不同的提示策略 (通用,增强,偏差缓解).
- 使用自动化指标和人类评估评估LLM输出.
主要成果:
- 缓解偏差的提示显著改善了指令的充分性和可接受性 (94.3%).
- 个性化得到了很高的评分,特别是在增强和偏差缓解提示的情况下.
- 事实错误和幻觉不常见;偏见通过特定提示和LLM选择来减轻.
- 两种LLM均表现出相似的充分性,幻觉频率不同.
结论:
- 通过LLM支持的生成显示了创造有效的处方指令的前景.
- 优化的提示可以提高清晰度,个性化,并减少电子处方说明中的偏见.
- 在电子处方系统中,LLM可以改善医疗保健提供者和患者之间的沟通.
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