应用基于一般大语言模型的分类系统来检索有关瘤学试验的信息
Fabio Dennstädt1,2, Paul Windisch3,4, Irina Filchenko5
1Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland, fabiodennstaedt@gmx.de.
Oncology
|June 15, 2025
概括
大型语言模型 (LLM) 在分类瘤学临床试验和文献方面表现出高准确性. 这种自动化方法对于管理不断增长的医学研究量至关重要.
科学领域:
- 人工智能在医学中的应用
- 计算生物学 计算生物学
- 医疗信息学 医疗信息学
背景情况:
- 瘤学出版物的指数增长需要自动化方法来分类临床试验和医学文献.
- 大型语言模型 (LLM) 为自动化各种分类任务提供了潜在的解决方案,包括用于瘤学试验的信息检索.
研究的目的:
- 开发和评估使用LLMs进行瘤学临床试验分类的一般文本分类框架.
- 评估各种LLM在分类瘤试验数据方面的表现.
主要方法:
- 开发了一个灵活的文本分类框架,具有可适应的提示,模型和类别.
- 该框架在与瘤学试验相关的四个数据集中的九个二进制分类问题上进行了测试.
- 评估使用了本地Mixtral-8x7B-Instruct-GPTQ模型和基于云的LLMs:Mixtral-8x7B-Instruct,Llama3.1-70B-Instruct,以及Qwen-2.5-72B.
主要成果:
- 该框架在测试的LLM中实现了高响应有效率 (高达99.88%).
- 总体性能指标包括准确度>94%,精度>92%,回忆>90%,F1分数>92%.
- 具体问题准确性因模型而异,但总体而言仍然很高,在所有任务和模型中超过77%.
结论:
- 基于LLM的框架证明了强大的准确性和适应性,用于分类瘤学试验和文献.
- 挑战包括快速依赖和显著的计算资源需求.
- 随着技术的成熟,LLMs将在自动化瘤学研究的分类方面发挥重要作用.
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