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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

Kyeryoung Lee¹, Hunki Paek¹, Liang-Chin Huang¹

¹IMO Health, Rosemont, IL, USA.

Informatics in medicine unlocked

|November 4, 2024

概括

本研究介绍了SEETrials,这是一个自动化系统,用于从摘要中提取瘤学临床试验数据. 它精确地捕获安全性和疗效信息,使得临床决策得以更快.

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.
需要自动化系统来准确和精细地提取安全性和疗效数据.
来自摘要的可计算数据可以为及时的临床决策提供信息.

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信息.
将非结构化抽象数据转化为可计算的格式,用于临床决策.
评估系统在各种癌症类型和疗法中的精度,回忆和通用性.

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,包括预处理,快速工程/知识摄入和后处理模块.
从质量和数量上评估系统性能,评估多发性骨髓瘤,乳腺,肺,淋巴瘤和白血病的概括性.

主要成果:

在多发性骨髓瘤试验中,SEETrials在70个数据元素中实现了高精度 (0.964),回忆 (0.988) 和F1得分 (0.974).
在四种额外的癌症中,概括性测试显示精度,回忆和F1得分从0.979到0.992.
在多发性骨髓瘤中对CAR-T,双特异性抗体和ADC疗法的比较分析显示了不同的疗效概况 (例如,CAR-T ORR:88%).

结论:

在提取瘤学试验数据方面,SEETrials表现出高准确性和多功能性.
该自动化系统可方便大规模的数据处理和细微的比较.
这项技术可以快速传播临床见解,以改善决策.

关键词:

自动安全和疗效提取自动化.会议摘要会议摘要在 GPT-4 中使用.大型语言模型.大规模的分析分析.瘤学临床试验临床试验

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