通过在多个队列间进行领域适应提高髋部骨折风险预测的通用性
Improving Generalizability of Hip Fracture Risk Prediction via Domain Adaptation Across Multiple Cohorts
AI总结：
本文提出通过整合多种领域适应方法提升髋部骨折风险预测模型的泛化能力，实验证明结合MMD、CORAL和DANN的方法在不同队列中表现最优。
摘要：
临床风险预测模型往往无法在不同队列间泛化，因为底层数据分布会因临床地点、地区、人口统计学特征和测量协议而不同。这种限制在髋部骨折风险预测中尤为明显，因为在其他队列（目标队列）中部署训练于一个队列（源队列）的模型性能会显著下降。我们利用三个大型队列——骨质疏松性骨折研究（SOF）、男性骨质疏松性骨折研究（MrOS）和英国生物银行（UKB）中共享的临床和DXA衍生特征，系统评估了三种领域适应方法——最大均值差异（MMD）、相关对齐（CORAL）和领域对抗神经网络（DANN）及其组合的性能。对于仅含男性和仅含女性的源队列，领域适应方法始终比无适应基准（源队列单独训练）表现更好，使用多种领域适应方法的组合产生了最大的且最稳定的增益。结合MMD、CORAL和DANN的方法在仅含男性源队列中实现了最高区分度，AUC为0.88，在仅含女性源队列中为0.95，表明整合多种领域适应方法可以产生对数据集差异不敏感的特征表示。与现有方法不同，我们的无结果方法在现实部署条件下使模型选择更加可行，并提高了髋部骨折风险预测模型的泛化能力。
英文摘要：
Clinical risk prediction models often fail to be generalized across cohorts because underlying data distributions differ by clinical site, region, demographics, and measurement protocols. This limitation is particularly pronounced in hip fracture risk prediction, where the performance of models trained on one cohort (the source cohort) can degrade substantially when deployed in other cohorts (target cohorts). We used a shared set of clinical and DXA-derived features across three large cohorts - the Study of Osteoporotic Fractures (SOF), the Osteoporotic Fractures in Men Study (MrOS), and the UK Biobank (UKB), to systematically evaluate the performance of three domain adaptation methods - Maximum Mean Discrepancy (MMD), Correlation Alignment (CORAL), and Domain - Adversarial Neural Networks (DANN) and their combinations. For a source cohort with males only and a source cohort with females only, domain-adaptation methods consistently showed improved performance than the no-adaptation baseline (source-only training), and the use of combinations of multiple domain adaptation methods delivered the largest and most stable gains. The method that combines MMD, CORAL, and DANN achieved the highest discrimination with the area under curve (AUC) of 0.88 for a source cohort with males only and 0.95 for a source cohort with females only), demonstrating that integrating multiple domain adaptation methods could produce feature representations that are less sensitive to dataset differences. Unlike existing methods that rely heavily on supervised tuning or assume known outcomes of samples in target cohorts, our outcome-free approaches enable the model selection under realistic deployment conditions and improve generalization of models in hip fracture risk prediction.
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