SynthesisJournal of perinatology : official journal of the California Perinatal Association2022
Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review.
Synthesis in Journal of perinatology : official journal of the California Perinatal Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Opportunities and Challenges of Using Artificial Intelligence in Predicting Clinical Outcomes and Length of Stay in Neonatal Intensive Care Units: Systematic Review.Journal of medical Internet research · 2025Pooled it
- The application of machine learning for infant cries classification and pathological cries detection: A systematic review.Science progressPooled it
- Beyond early diagnosis: toward AI-enabled multi-omics prediction of necrotizing enterocolitis.World journal of pediatrics : WJP · 2026Article
- Ethical aspects of artificial intelligence use in neonatal intensive care units: a scoping review.European journal of pediatrics · 2026Article
- Maturational physiology in preterm infants: morbidity impact and 2-year neurodevelopmental outcome.Pediatric research · 2026Article
- Application of Machine Learning Algorithms for Predicting Infant Mortality in India: An Analysis of the National Family Health Survey-5, 2019-2021.Healthcare informatics research · 2026Article
- Neurodevelopmental Outcome in Very Low Birth Weight Preterm Infants: An Exploratory Multivariable Analysis Including Sonographic Brain Volume Trajectories-Data from the NeoNEVS Project.Children (Basel, Switzerland) · 2026Article
- Machine learning risk stratification for high-risk infant follow-up of term and late preterm infants.Pediatric research · 2026Article
- Heat stress and cardiovascular hospitalizations in a semi-arid megacity: a multi-method epidemiological and machine-learning analysis in Isfahan, Iran.BMC public health · 2026Article
- Artificial intelligence for patent ductus arteriosus-a systematic review.Frontiers in pediatrics · 2026Review
- Transcriptomics-guided serum podoplanin and interpretable machine learning for early risk stratification of necrotizing enterocolitis in preterm infants.Frontiers in immunology · 2026Article
- Emerging modalities for neuroprognostication in neonatal encephalopathy: harnessing the potential of artificial intelligence.Pediatric research · 2025Review
- Transforming neonatal care through informatics: A review of artificial intelligence, data, and implementation considerations.Seminars in perinatology · 2025Review
- [Recent advances in artificial intelligence for auxiliary diagnosis and management of neonatal necrotizing enterocolitis].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2025Review
- Neonatal nurses' experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus.BMC nursing · 2025Article
- Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI.NPJ digital medicine · 2025Article
- An efficient interpretable framework for unsupervised low, very low and extreme birth weight detection.PloS one · 2025Article
- Integrating sequencing methods with machine learning for antimicrobial susceptibility testing in pediatric infections: current advances and future insights.Frontiers in microbiology · 2025Review
- A decision tree analysis to predict massive pulmonary hemorrhage in extremely low birth weight infants: a nationwide large cohort database.Frontiers in pediatrics · 2025Article
- Development of a machine learning model for prediction of intraventricular hemorrhage in premature neonates.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Article
Corrections and comments
- Commented on by
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdvances in technology, data availability, and analytics have helped improve quality of care in the neonatal intensive care unit.
objectiveTo provide an in-depth review of artificial intelligence (AI) and machine learning techniques being utilized to predict neonatal outcomes.
methodsThe PRISMA protocol was followed that considered articles from established digital repositories. Included articles were categorized based on predictions of: (a) major neonatal morbidities such as sepsis, bronchopulmonary dysplasia, intraventricular hemorrhage, necrotizing enterocolitis, and retinopathy of prematurity; (b) mortality; and (c) length of stay.
resultsA total of 366 studies were considered; 68 studies were eligible for inclusion in the review. The current set of predictor models are primarily built on supervised learning and mostly used regression models built on retrospective data.
conclusionWith the availability of EMR data and data-sharing of NICU outcomes across neonatal research networks, machine learning algorithms have shown breakthrough performance in predicting neonatal disease.
Indexed as
Identifiers
35562414What OpenQuestion holds
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.