弥合瘤学数据缺口:大型语言模型和协作过用于癌症治疗建议
Tengjie Tang1, Angkai Li1, Xingye Tan1
1Department of Statistics, Pennsylvania State University, University Park, Pennsylvania, U.S.A.
medRxiv : the preprint server for health sciences
|April 29, 2025
概括
大型语言模型 (LLM) 和人工智能算法可以通过分析临床试验数据来识别新的癌症治疗方法. 这种方法为治疗选择有限的罕见癌症患者提供了希望.
科学领域:
- 在瘤学中使用人工智能
- 临床试验数据分析
- 个性化医疗是个性化的医疗.
背景情况:
- 由于临床试验稀少,罕见癌症患者面临着有限的基于证据的治疗选择.
- 大型语言模型 (LLM) 和推算法可以利用现有的临床试验数据来改进治疗决策.
研究的目的:
- 开发和验证一种新的方法,使用LLM和推算法来识别有效的癌症治疗方法.
- 通过系统地分析临床试验数据,应对罕见癌症治疗选择有限的挑战.
主要方法:
- 利用LLM从clinicaltrials.gov.gov提取和标准化超过10万个癌症试验.
- 用自定义评分系统对癌症治疗相互作用进行注释,对每个试验进行了注释.
- 实施了三种协作过算法,以推各种癌症类型的治疗方法.
主要成果:
- 创建了一个涵盖78种癌症类型和5315项干预措施的综合数据库.
- 推模型实现了高预测准确度 (交叉验证的RMSE:0.49-0.62).
- 确定了临床上有意义的黑色素瘤新型治疗方法,经过专家的验证.
结论:
- 证明了结合LLM和推算法的概念证明,以确定新的癌症治疗方法.
- 这种综合方法可以加快为罕见癌症发现有效疗法的速度.
- 在数据稀缺的情况下,有可能通过提供基于证据的治疗建议来改善患者的治疗结果.
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