Dept. of Medical Informatics, Amsterdam UMC, Location AMC, The Netherlands.
Stud Health Technol Inform. 2022 Jun 29;295:148-151. doi: 10.3233/SHTI220683.
External validation of models for the prediction of acute kidney injury (AKI) is rare. We externally validate AKI prediction models in intensive care units. The models were developed on the Medical Information Mart for Intensive Care dataset and validated on the eICU dataset. Traditional machine learning models show limited transportability to the new population (AUROC < 0.8). Models based on recurrent neural networks, which can capture complex relationships between the data, transport well to the new population (AUROC 0.8-0.9). Such models can help clinicians to recognize AKI and improve the outcome.
外部验证用于预测急性肾损伤 (AKI) 的模型很少见。我们在重症监护病房对 AKI 预测模型进行了外部验证。这些模型是基于医疗信息集市重症监护数据集开发的，并在 eICU 数据集上进行了验证。传统的机器学习模型在新人群中的可转移性有限（AUROC < 0.8）。基于可以捕获数据之间复杂关系的递归神经网络的模型，在新人群中很好地转移（AUROC 0.8-0.9）。此类模型可以帮助临床医生识别 AKI 并改善预后。
