利用大型语言模型来生成对患者信息的响应 - - 一种主观分析
Siru Liu1, Allison B McCoy1, Aileen P Wright1,2
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37212, United States.
大型语言模型 (LLM) 显示出产生患者门户消息响应的前景. 像CLAIR-Long这样的微调模型提供了教育内容和同情的沟通,改善了患者和医疗服务提供者的互动.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
- 临床交流 临床沟通
背景情况:
- 患者门户信息对于医疗保健沟通至关重要.
- 目前的响应生成方法可能缺乏效率和个性化.
- 大型语言模型 (LLM) 提供了自动响应生成的潜力.
研究的目的:
- 开发和评估微调的LLM,用于生成对患者门户消息的响应.
- 以同情,响应,准确性和有用性来评估这些模型的表现.
主要方法:
- 开发了CLAIR-Short和CLAIR-Long模型,使用预先训练的LLM (LLaMA-65B) 和大量患者信息和响应数据集.
- 通过使用OpenAI API提高了患者教育,同情和专业性的响应.
- 通过让初级保健医生对CLAIR模型和ChatGPT的反应进行评价,评估模型的性能.
主要成果:
- CLAIR-Short生成了简洁的响应,而CLAIR-Long提供了增强的教育内容.
- 克莱尔-长响应的评分与ChatGPT的评分相似,响应度,同情心和准确性得分为积极.
- 医生发现克莱尔-朗的反应是中立有用的.
结论:
- 精心调整的LLM具有通过电子健康记录门户网站改善患者与医疗服务提供者的沟通的巨大潜力.
- 通过LLM生成的响应可以增强患者教育和简化沟通.
- 需要进一步的研究来优化实用性和临床整合.
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