机器学习和临床试验中的自然语言处理 资格标准 解析:范围审查
Klaudia Kantor1, Mikołaj Morzy2
1Roche Informatics, Warsaw, Poland; Poznan University of Technology, Poland.
Drug discovery today
|August 18, 2024
概括
使用机器学习 (ML) 和自然语言处理 (NLP) 自动分析临床试验资格标准有助于患者招募. 然而,目前AI在临床协议分析中的采用仍然有限.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 临床试验管理 临床试验管理
背景情况:
- 自动分析合格标准对于有效的临床试验招聘和数据完整性至关重要.
- 机器学习 (ML) 和自然语言处理 (NLP) 提供了先进的功能,以简化患者积累.
- 尽管取得了进展,但这些技术在临床研究工作流程中的整合尚未完全实现.
研究的目的:
- 进行基于PRISMA的范围审查,评估ML/NLP模型用于分析临床试验资格标准的应用.
- 评估ML/NLP模型在分析临床试验方案中的使用程度和性质.
- 确定在这个领域采用最先进的人工智能 (AI) 的差距.
主要方法:
- 在主要数据库中对2000年至2024年间发表的论文进行系统的文献搜索.
- 基于PRISMA的范围审查方法.
- 在17个维度的88个选定的出版物的数据图表.
主要成果:
- 审查涵盖了9160篇最初识别的论文,其中88篇正在接受详细分析.
- 分析显示,对ML/NLP进行了大量的研究,以分析资格标准.
- 在临床协议的实际分析中,观察到尖端AI模型的显着不足.
结论:
- 在临床试验中,ML/NLP模型在自动化资格标准分析方面显示出相当大的前景.
- 在先进人工智能的潜力与其在临床协议分析中的当前应用之间存在很大的差距.
- 未来的研究应该专注于整合最先进的AI,以提高临床试验效率和数据有效性.
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