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在使用机器学习的随机对照试验中检测不规则

Walter Nelson^1,2, Jeremy Petch^1,3,4,5, Jonathan Ranisau¹

¹Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada.

Clinical trials (London, England)

|November 26, 2024

概括

机器学习算法可以在临床试验中像人类一样早检测数据不规则,提高效率. 这种自动化方法有助于在大型多中心试验中进行中央统计监测.

科学领域:

临床试验数据管理
生物统计学生物统计学
机器学习在医疗保健中的应用

背景情况:

临床试验产生可能含有不规则的数据,需要手动,资源密集的中央统计监测.
机器学习 (ML) 显示了在多中心试验中自动检测中心级异常的潜力.

研究的目的:

描述历史临床试验中的形式级数据不规则.
评估基于ML的异常值检测算法的识别这些不规则的能力.

主要方法:

将初步试验快照与最终数据库进行比较,以确定人类确定的违规行为.
使用一致性,正预测值和灵敏度评估ML算法性能.

主要成果:

分析了77,001名参与者的7项试验,确定了24,850种形式的不规则.
拟议的ML算法实现了0.74的中位对应度,超过了以前的ML方法 (中位AUC0.73).

结论:

机器学习算法可以在没有干预的情况下,比人类更早或更早地检测出形式级数据不规则.
这种ML方法可以补充现有的中央统计监测,可能提高大规模试验数据验证的效率.

关键词:

中央统计监测中心人工智能的人工智能是人工智能.数据质量数据质量数据质量机器学习是机器学习.异常标志的检测异常标志的检测质量保证质量保证质量保证随机对照试验是随机对照试验.

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