乳腺癌治疗后以患者为中心的结果的自动提取:一个基于大型语言模型的开源工具包
Man Luo1, Shubham Trivedi1, Allison W Kurian2
1Department of Radiology, Mayo Clinic, Phoenix, AZ.
JCO clinical cancer informatics
|August 21, 2024
概括
微调大型语言模型 (LLM) 显著改善了从临床笔记中提取以患者为中心的结果 (PCO). 特定任务的微调提高了癌症治疗研究的LLM性能,优于一般模型.
科学领域:
- 在瘤学中的自然语言处理.
- 临床信息学 临床信息学
- 患者为中心的护理研究研究
背景情况:
- 以患者为中心的结果 (PCOs) 对于评估癌症治疗有效性和生活质量至关重要.
- 从非结构化的临床叙述中提取PCO是具有挑战性的,但对于全面分析至关重要.
- 现有的PCO数据通常在规模和范围上是有限的.
研究的目的:
- 评估大型语言模型 (LLM) 的适应性,以从临床笔记中提取PCO.
- 开发和评估一个开源框架,用于为PCO提取任务微调LLM.
- 在多个机构中比较不同LLM (GPT-2,BioGPT,PMC-LLaMA) 在PCO识别方面的表现.
主要方法:
- 三个LLM (GPT-2,BioGPT,PMC-LLaMA) 在PCO提取任务上进行了评估.
- 开发了一个开源框架,用于对临床叙述进行微调LLM.
- 在三个机构测试模型:梅奥诊所,埃默里大学医院和斯坦福大学.
主要成果:
- 零射击和少数射击的LLM在PCO提取中表现不佳.
- 精细调整的,针对特定任务的LLM显著优于非精细调整的模型.
- 微调的GPT-2模型在比较较大的LLM如BioGPT和PMC-LLaMA时表现出更高的性能.
结论:
- 在临床环境中有效应用,LLM需要特定领域的微调.
- 拟议的微调框架为PCO信息提取提供了一种高效和可适应的方法.
- 这种方法有可能在没有大量计算资源的情况下改进PCO数据分析.
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