临床预测模型的虚幻概括性
Adam M Chekroud1,2, Matt Hawrilenko1, Hieronimus Loho2
1Spring Health, New York City, NY 10010, USA.
Science (New York, N.Y.)
|January 11, 2024
概括
医疗治疗决策的统计模型显示普遍性较差. 对于精神分裂症治疗结果的机器学习模型在开发过程中是准确的,但在新的试验中失败了,表明了上下文依赖.
科学领域:
- 精神病学
- 医疗信息学
- 生物统计学
背景情况:
- 统计模型有望加强医疗治疗决策.
- 由于数据成本和稀缺性,模型开发通常依赖于有限的数据集或临床环境.
- 这种依赖引发了医疗预测模型的普遍性问题.
研究的目的:
- 仔细研究医疗决策中的统计模型的乐观性.
- 检查机器学习模型在精神分裂症独立临床试验中的表现.
- 评估抗精神病药物的预测模型的概括性.
主要方法:
- 使用精神分裂症临床试验数据开发了一种机器学习模型.
- 该模型的预测准确性在独立临床试验的样本外数据上进行了测试.
- 探索了跨试验的数据聚合,以提高样本外预测性能.
主要成果:
- 机器学习模型在开发的数据集中实现了高准确性.
- 当该模型应用于独立的样本外临床试验时, 结果并不比偶然的结果更好.
- 在不同试验中汇集数据并没有提高模型对未见数据的预测性能.
结论:
- 预测精神分裂症治疗结果的机器学习模型高度依赖环境.
- 这些模型在不同临床试验或患者群体中表现出有限的概括性.
- 这些发现挑战了当前预测模型在个性化精神治疗中的广泛适用性.
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