药物开发 药物开发
Joseph Geraci1,2,3, Patrick P O'Keefe4, Bessi Qorri1
1NetraMark Corp, Toronto, ON, Canada.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
概括
机器学习在阿尔茨海默病试验中检测异常患者,仅使用基线数据识别潜在的站点级欺诈行为. 这种方法客观地提高了临床试验数据的完整性.
科学领域:
- 临床试验 临床试验
- 机器学习 机器学习
- 数据完整性 数据完整性
背景情况:
- 阿尔茨海默病 (AD) 临床试验面临主观终点和部位不一致的挑战.
- 具有悖论性的患者,表现出不寻常的数据模式,可能会表明试验异常或欺诈.
- 机器学习框架NetraAI被开发用于检测这些悖论患者并评估位点异常.
研究的目的:
- 确定患者级数据分析是否可以在AD试验中发现患者特异性和地点特异性欺诈活动.
- 通过仅使用查和基线数据验证NetraAI识别异常的能力.
主要方法:
- NetraAI使用基线和查变量 (临床量表,实验室,不良事件) 模拟患者场景.
- 产生了一个悖论性患者风险评分,整合了信息理论的可变性,单元频率和协同关联处罚.
- 患者得分被映射到网站,并与独立衍生的网站欺诈得分进行比较.
主要成果:
- NetraAI的调查结果与独立网站欺诈数据有很强的相关性.
- 具有高风险悖论性患者高度的网站显示了更高的真正欺诈得分.
- 该框架成功地根据患者级数据识别了G和M等有问题的部位.
结论:
- NetraAI的悖论性患者方法有效地识别了AD试验中的异常患者和异常部位,仅使用基线数据.
- 该方法为改善临床试验数据完整性提供了一个可扩展和客观的策略.
- 未来的工作旨在扩展NetraAI用于临床试验中的直接现场评估.
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