Continuous imputation of blood gas and metabolic panel laboratory values in the intensive care unit using machine learning.

We compared the performance of the deep learning models using laboratory variables imputed hourly by our machine learning estimators versus models using laboratory variables imputed by a previous-value baseline.

Accuracy of laboratory imputations were compared using mean absolute error (mean absolute error) and root mean squared error (RMSE).

We compared performance using area under the receiver operating characteristics curve (AUROC) and area under the precision-recall curve (area under the precision-recall curve).