使用因果模型评估认知障碍临床预测模型的可转移性
Jana Fehr1,2, Marco Piccininni3,4, Tobias Kurth3
1Digital Engineering Faculty, University of Potsdam, Potsdam, Germany. jana.fehr@hpi.de.
BMC medical research methodology
|August 19, 2023
概括
使用结果的原因预测认知障碍可以提高模型的可转移性和在新环境中的校准性. 这与基于后果的预测形成鲜明对比,强调校准.
科学领域:
- 在医疗保健和诊断领域的机器学习.
- 认知障碍预测模型模型的模型.
- 生物统计学和因果推理.
背景情况:
- 机器学习模型对诊断预测有希望,但在新环境中经常失败.
- 为没有数据的新环境选择最佳模型是一个重大挑战.
- 调查模型的可运输性对于可靠的临床应用至关重要.
研究的目的:
- 评估认知障碍预测模型在模拟外部环境中的可转移性.
- 在不同的人口和临床分布下使用校准和歧视指标评估模型性能.
- 了解如何预测原因与后果对模型可转移性的影响.
主要方法:
- 使用因果图和结构方程模型来量化ADNI数据中的变量关系.
- 模拟数据集被生成以评估各种预测器集的预测模型.
- 通过在指导干预下比较内部和外部模型性能 (校准,AUC) 来测量可移植性.
主要成果:
- 预测认知障碍原因的模型在校准方面表现出卓越的可转移性.
- 曲线下的面积 (AUC) 对于可运输性的趋势在外部设置中不一致.
- 预测后果的模型在外部显示出更高的AUC,而使用父母或所有变量的模型具有相似的AUC.
结论:
- 与反因果预测相比,根据结果的原因进行预测可以提高模型的可传输性,特别是在校准方面.
- 在评估预测模型的可转移性时,校准性能是关键因素.
- 这些发现强调了因果关系在开发强大和可泛化的诊断工具方面的重要性.
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