在临床试验数据中检测MACE事件的机器学习方法
John A Spanias1, Robbie Buderi1, Pierre-Louis Bourlon1
1Medidata Solutions.
Journal of biopharmaceutical statistics
|November 17, 2024
概括
机器学习模型现在可以在临床试验数据中识别主要不良心血管事件 (MACE). 这种算法方法增强了现实世界的数据分析,并可以减少临床试验资源需求.
科学领域:
- 临床研究方法论临床研究方法论
- 医疗信息学 医疗信息学
- 机器学习在医学中的应用.
背景情况:
- 随机对照试验 (RCT) 是标准的,但可能不反映现实世界的药物影响.
- 现实世界数据 (RWD) 提供了更广泛的患者群体和护理环境.
- 在RWD的一个关键挑战是缺少算法结果识别.
研究的目的:
- 开发机器学习模型,在临床试验数据 (CTD) 中识别主要不良心血管事件 (MACE).
- 评估使用这些模型来分析CTD和RWD的结果的可行性.
- 证明减少RCT资源的潜力,并支持实用试验的监管提交.
主要方法:
- 匿名的CTD被用来开发用于识别MACE的特征.
- 三个随机森林模型被训练来检测3点MACE的组件.
- 用回忆和精度指标评估模型性能.
主要成果:
- 开发的模型证明了在未来的试验中确定临床结果的可行性.
- 模型实现了0.72 (0.07) 的回忆和0.68 (0.12) 的精度.
- 该研究提出了在临床环境中部署这些模型的成本效益分析.
结论:
- 先进的算法可以有效地识别潜在试验中的临床结果.
- 部署这些机器学习模型可能会减少RCT所需的资源.
- 将这些模型扩展到RWD可以促进实用临床试验的监管批准.
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