SEETrials:利用大型语言模型在瘤学临床试验中提取安全性和有效性
medRxiv : the preprint server for health sciences
|May 27, 2024
概括
使用SEETrials从摘要中自动提取瘤临床试验数据可以提高准确性和回忆力. 该系统将研究结果转化为可计算的数据,以改善临床决策.
科学领域:
- 在瘤学瘤学.
- 临床试验 临床试验
- 数据科学数据科学数据科学
背景情况:
- 从摘要中手动提取瘤学临床试验结果是耗时的.
- 开发一个用于精确提取安全性和疗效数据的自动化系统对于及时做出临床决策至关重要.
研究的目的:
- 开发一个自动化系统 (SEETrials) 来从瘤学临床试验摘要中提取安全性和有效性数据.
- 为了在数据提取中实现高精度和颗粒度.
- 为了实现可计算的数据转换,以加强临床决策.
主要方法:
- 收集了来自会议和PubMed (2012-2023) 的临床试验摘要.
- 开发了SEETrials系统的四个模块:预处理,快速建模,知识摄入和后处理.
- 在多发性骨髓瘤,乳腺,肺,淋巴瘤和白血病中定性和定量评估系统性能.
主要成果:
- 对于多发性骨髓瘤数据,SEETrials实现了高精度 (0.958),回忆 (0.944) 和F1得分 (0.951).
- 在其他四种癌症的概括性测试中,准确度,回忆力和F1分数在0.966-0.986.6之间.
- 对CAR-T,双特异抗体和ADC疗法的比较分析揭示了不同的安全性和有效性概况.
结论:
- 在从瘤学临床试验摘要中提取数据方面,SEETrials表现出高准确性和多功能性.
- 大数据集的自动处理可以进行细微的比较,并促进临床见解的传播.
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