ArticlePediatric pulmonology2025
Early Prediction of Mechanical Ventilation Needs in Very Preterm Neonates Using Machine Learning.
Article in Pediatric pulmonology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence and digital technologies transforming neonatal respiratory assessment: a scoping review.Journal of perinatology : official journal of the California Perinatal Association · 2026Review
- Video-based artificial intelligence for automated neonatal respiratory monitoring.European journal of pediatrics · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundThe use of invasive mechanical ventilation (IMV) increases morbidity in patients but may be required for survival on a subset of patients. Delayed, as well as unnecessary, initiation of IMV increases morbidity and mortality among preterm neonates.
objectiveTo assess the use of machine learning in predicting need for IMV among patients on lower levels of respiratory support (CPAP failure) using the earliest clinical data captured in the electronic health records across multiple health systems in the United States. Accurate predictions may enable earlier proactive intervention to minimize CPAP failure and improve clinical outcomes.
methodsThis study was conducted using the Oracle EHR Real-World Data (OERWD) database including preterm neonatal intensive care unit (NICU) admissions between 2012 and 2022. Demographics and the first set of vital signs and laboratory values were retrieved and used to train an extreme gradient boosting (XGBoost) machine learning model.
resultsTwenty thousand three hundred and sixty-three neonates from 27 NICUs qualified for the study with CPAP failure rate of 69.0%. Fraction of inspired oxygen (FiO
conclusionsPatients requiring IMV can be predicted early at birth with high accuracy. This may result in earlier initiation of IMV on patients who need it while reducing unnecessary intubations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.