ArticleBiomedicines2021
Machine Learning Algorithms to Predict Mortality of Neonates on Mechanical Intubation for Respiratory Failure.
Article in Biomedicines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine learning models for prediction of continuous positive airway pressure failure and high-flow nasal cannula failure in preterm neonates with respiratory distress.European journal of pediatrics · 2026Article
- Artificial Intelligence for Neonatal and Perinatal Mortality Prevention: A Systematic Review of Machine Learning and Deep Learning Applications.Healthcare (Basel, Switzerland) · 2026Review
- A deep-learning approach to predict mortality in very-low-birth-weight infants treated in the intensive care units using DeepSurv.Frontiers in pediatrics · 2026Article
- Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh.BMC public health · 2025Article
- Machine learning-based prediction of hemodynamically significant patent ductus arteriosus in preterm neonates: a pioneering insight.Frontiers in pediatrics · 2025Article
- A nomogram to predict the risk of death during hospitalization in Chinese neonates with respiratory failure.Heliyon · 2024Article
- Computational Approaches for Predicting Preterm Birth and Newborn Outcomes.Clinics in perinatology · 2024Review
- Risk factors for death caused by early onset sepsis in neonates: a retrospective cohort study.BMC infectious diseases · 2023Article
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- Comparing different scoring systems for predicting mortality risk in preterm infants: a systematic review and network meta-analysis.Frontiers in pediatrics · 2023Review
- Machine Learning Models for Predicting Mortality in 7472 Very Low Birth Weight Infants Using Data from a Nationwide Neonatal Network.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundEarly 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.
methodsA total of 1734 neonates with respiratory failure were randomly divided into training (70%,
resultsFor NICU mortality, the random forest (RF) model showed the highest AUC (0.939 (0.921-0.958)) for the prediction of neonates with respiratory failure, and the bagged classification and regression tree model demonstrated the next best results (0.915 (0.891-0.939)). The AUCs of both models were significantly better than the traditional NTISS (0.836 (0.800-0.871)) and SNAPPE-II scores (0.805 (0.766-0.843)). The superior performances were confirmed by higher accuracy and F1 score and better calibration, and the superior and net benefit was confirmed by decision curve analysis. In addition, Shapley additive explanation (SHAP) values were utilized to explain the RF prediction model.
conclusionsMachine learning algorithms increase the accuracy and predictive ability for mortality of neonates with respiratory failure compared with conventional neonatal illness severity scores. The RF model is suitable for clinical use in the NICU, and clinicians can gain insights and have better communication with families in advance.
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