Machine Learning Algorithms to Predict Mortality of Neonates on Mechanical Intubation for Respiratory Failure.

ABSTRACT: Early identification of critically ill neonates with poor outcomes can optimize therapeutic strategies. We aimed to examine whether machine learning (ML) methods can improve mortality prediction for neonatal intensive care unit (NICU) patients on intubation for respiratory failure. A total of 1734 neonates with respiratory failure were randomly divided into training (70%, n = 1214) and test (30%, n = 520) sets. The primary outcome was the probability of NICU mortality. The areas under the receiver operating characteristic curves (AUCs) of several ML algorithms were compared with those of the conventional neonatal illness severity scoring systems including the NTISS and SNAPPE-II. For NICU mortality, the random forest (RF) model showed the highest AUC (0.939 (0.921-0.958)) for the predictio

SUBMITTER: Hsu JF 

PROVIDER: S-EPMC8533201 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature