机器学习方法是否会导致类似的个性化治疗规则? 一项对真实数据的比较研究
Florie Bouvier1, Etienne Peyrot1, Alan Balendran1
1Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Université Paris Cité and Université Sorbonne Paris Nord, Paris, France.
Statistics in medicine
|March 13, 2024
概括
针对个性化治疗规则 (ITR) 的不同机器学习方法在患者建议中显示出显著的分歧. 这种变化引发了人们对这些个性化医疗方法的实际应用和可互换性的担忧.
科学领域:
- * 生物统计学
- * 机器学习 * 机器学习
- * 个性化的医疗
背景情况:
- *个性化医疗旨在通过个性化治疗规则 (ITR) 量身定制治疗.
- * 对于ITR开发,有许多机器学习方法存在,但它们的比较性能和一致性尚未得到充分理解.
研究的目的:
- * 为了比较22种用于生成ITR的常见机器学习方法的性能和一致性.
- * 评估不同方法在多大程度上为患者提供了类似的治疗建议.
主要方法:
- * 在两个随机对照试验中评估了22种机器学习方法.
- *将方法分类为预测个性化治疗效果的方法和直接估计ITR的方法.
- *使用各种指标评估ITR绩效,并计算ITR之间的对对协议.
主要成果:
- * 通过不同的方法生成的ITR中,在患者选择治疗方面观察到显著的分歧.
- *具有相似基础方法的方法显示出更好的一致性.
- *在验证样本上评估的ITR表现有限,表明潜在的过拟合,特别是在非参数方法中.
结论:
- * 机器学习方法的选择对患者治疗建议有很大影响.
- * 不同的ITR方法是不可互换的,并产生不相似的结果.
- *性能指标的变化和乐观的潜力引起了人们对当前ITR方法的临床实用性的担忧.
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