对医学研究和医疗保健的生成性大语言模型的研究
Cheng Peng1, Xi Yang1,2, Aokun Chen1,2
1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
NPJ digital medicine
|November 17, 2023
概括
研究人员开发了GatorTronGPT,这是一个专门用于医疗保健的大型语言模型 (LLM). 这项临床LLM在生物医学自然语言处理方面表现出卓越的性能,并产生与人类医生无法区分的合成文本.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
背景情况:
- 一般用途的大型语言模型 (LLM) 对专门的医疗保健应用有局限性.
- 现有的LLM并没有针对临床数据和医学研究的细微差别进行优化.
研究的目的:
- 开发和评估GatorTronGPT,一种基于大量临床和一般英语文本的生成性临床法学士课程.
- 评估GatorTronGPT对生物医学自然语言处理 (NLP) 和医疗保健文本生成的实用性.
主要方法:
- 经过训练的GatorTronGPT使用GPT-3架构,在2770亿个文字 (临床和一般英语) 上有200亿个参数.
- 评估了GatorTronGPT在生物医学NLP任务上的表现.
- 使用GatorTronGPT生成了200亿字的合成临床文本.
- 进行了医生图灵测试,将合成文本与人类生成的文本进行比较.
主要成果:
- GatorTronGPT显著改善了生物医学NLP任务.
- 在GatorTronGPT生成文本上训练的合成NLP模型的表现优于在真实世界的临床文本上训练的模型.
- 在语言可读性和临床相关性方面,医生无法将GatorTronGPT生成的文本与人类医生的文本区分开来 (p < 0.001).
结论:
- GatorTronGPT在将LLMs应用于医疗保健和医学研究方面取得了重大进展.
- 该研究强调了专门的临床LLM在改善NLP和生成高质量的合成临床数据方面的潜力.
- 对LLM在医疗保健中的机会和挑战进行进一步的研究是有必要的.
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