自动精确瘤学文献策划:使用大型语言模型的决策树方法
Frank P Lin1,2,3, Minh Tran3, Subotheni Thavaneswaran1,2,3,4
1Garvan Institute of Medical Research, Sydney, Australia.
Studies in health technology and informatics
|August 8, 2025
概括
大型语言模型 (LLM) 自动选精确瘤学知识库的文献. 这一人工智能管道实现了92.9%的准确性,大大提高了策划重要生物标志物-治疗关系数据的效率.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 精确瘤学知识库需要广泛的手册文献选.
- 大型语言模型 (LLM) 对自动化知识提取充满希望,但尚未为此任务进行评估.
- 自动化抽象选对于高效的知识库策划至关重要.
研究的目的:
- 开发和评估基于LLM的管道,用于自动化精密瘤学文献查.
- 评估管道在确定相关生物标志物治疗关系方面的表现.
主要方法:
- 开发了一个基于决策树的管道,利用LLM.
- 结构化提示被用来评估2024年瘤学期刊的8012个摘要中的生物标志物-治疗关系.
- 绩效与使用标准指标的专家评价进行了比较.
主要成果:
- 管道实现了92.9%的整体准确度 (87.1%的灵敏度,93.7%的特异性).
- 它列出了1,328个摘要,其分类精度增加了5.26倍,确定了888个相关的摘要.
- 确定的摘要涵盖了关键的分子标,免疫疗法,向疗法和癌症类型.
结论:
- 该LLM管道有效地自动化了精确瘤学知识库的文献分类.
- 它保持了高的特异性,并为可持续的知识库维护提供了可扩展的解决方案.
- 这种方法可以显著减少手动查的负担.
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