使用大型语言模型在临床开发中对协议偏差进行先进和灵活的标记.
Min Zou1, Leszek Popko2, Michelle Gaudio3
1F.Hoffmann-La Roche AG, 4070, Basel, Switzerland. elaine.zou@roche.com.
Therapeutic innovation & regulatory science
|May 13, 2025
概括
一种新的大型语言模型 (LLM) 方法自动化了协议偏差 (PD) 分类,提高了临床试验的效率. 这种方法快速识别有影响力的PDs,增强人类受试者保护和临床开发中的疾病进展评估.
科学领域:
- 临床试验管理 临床试验管理
- 监管科学 监管科学
- 医疗保健中的人工智能
背景情况:
- 协议偏差 (PD) 是与研究协议的变化,由于定义和审查过程不一致,对人类受试者保护构成挑战.
- 现有的识别和趋势影响PD的方法是有限的,尽管行业的努力.
- 传统的自然语言处理 (NLP) 方法用于PD分类是繁的,需要先进的解决方案.
研究的目的:
- 开发一种自动化,高效和灵活的解决方案来分类自由文本协议偏差.
- 为了利用大型语言模型 (LLM) 进行细微和上下文意识的PD分类.
- 在临床方案中实现PD的系统趋势和风险评估.
主要方法:
- 使用一个大型语言模型 (LLM),Meta Llama2,有一个专门设计的提示符来分类协议偏差.
- 将LLM应用于来自PD管理系统的自由文本数据.
- 分析模型输出以确定趋势,评估风险,并支持专家决策.
主要成果:
- 该LLM方法成功标记了80%以上的PDs可能影响疾病进展评估.
- 自动分类在几分钟内提供了可操作的见解,与手动方法相比,分析时间显著减少.
- 确定了第一线对照中的差距,有助于改善在试验中处理疾病进展的过程.
结论:
- 基于LLM的自动分类为临床开发中有效的PD管理提供了可概括的框架.
- 这种方法提高了疾病进展评估的准确性,并支持更好的人类受试者保护.
- 该方法通过突出控制差距并使快速风险识别成为可能,促进了流程改进.
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