论文标题
寻找多中心重症监护数据中的分布环境
Looking for Out-of-Distribution Environments in Multi-center Critical Care Data
论文作者
论文摘要
临床机器学习模型显示在训练期间未见的设置中进行测试时的性能下降。领域的概括模型有望减轻这个问题，但是，仍然对它们是否在传统培训上有所改善仍然存在怀疑。在这项工作中，我们采取了一种原则性的方法来识别由重症监护中跨医院概括的问题引起的分布（OOD）环境。我们提出了基于模型的启发式方法，以识别OOD环境，并系统地比较具有不同级别的固定信息的模型。我们发现，对OOD数据的访问并不能转化为增加的性能，这表明由于数据协调和采样而定义潜在的OOD环境的固有局限性。在文献中与其他流行的临床基准相呼应类似的结果，需要采用新的方法来评估健康记录上的健壮模型。
Clinical machine learning models show a significant performance drop when tested in settings not seen during training. Domain generalisation models promise to alleviate this problem, however, there is still scepticism about whether they improve over traditional training. In this work, we take a principled approach to identifying Out of Distribution (OoD) environments, motivated by the problem of cross-hospital generalization in critical care. We propose model-based and heuristic approaches to identify OoD environments and systematically compare models with different levels of held-out information. We find that access to OoD data does not translate to increased performance, pointing to inherent limitations in defining potential OoD environments potentially due to data harmonisation and sampling. Echoing similar results with other popular clinical benchmarks in the literature, new approaches are required to evaluate robust models on health records.
