Despite advances in machine learning-based clinical prediction models, only few of such models are actually deployed in clinical contexts. Among other reasons, this is due to a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients initially developed on the MIMIC-III dataset when applied to an external cohort of an American research hospital. To help account for the performance differences observed, we utilized interpretability methods based on feature importance, which allowed experts to scrutinize model behavior both at the global and local level, making it possible to gain further insights into why it did not behave as expected on the validation cohort. The knowledge gleaned upon derivation can be potentially useful to assist model update during validation for more generalizable and simpler models. We argue that interpretability methods should be considered by practitioners as a further tool to help explain performance differences and inform model update in validation studies.

尽管基于机器学习的临床预测模型取得了进步，但只有少数模型真正部署在临床环境中。除其他原因外，这是由于缺乏验证研究。在本文中，我们介绍并讨论了用于预测心脏手术患者急性肾损伤的机器学习模型的验证结果，该模型最初是在 MIMIC-III 数据集上开发的，应用于美国研究医院的外部队列时。为了帮助解释观察到的性能差异，我们使用了基于特征重要性的可解释性方法，这使得专家能够在全局和局部层面上仔细检查模型行为，从而可以进一步深入了解为什么它在验证队列。推导过程中收集的知识可能有助于在验证更通用和更简单的模型期间协助模型更新。我们认为，从业者应将可解释性方法视为进一步的工具，以帮助解释性能差异并为验证研究中的模型更新提供信息。