ArticleYonsei medical journal2022
Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants.
Article in Yonsei medical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers, 2 of them syntheses that pooled it.
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.
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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- The intelligent neonatal healthcare: a systematic review of machine learning architectures integrating the internet of medical things and blockchain.Frontiers in artificial intelligence · 2026Pooled 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
- Predicting low birth weight in Bangladesh using interpretable machine learning models.BMC pregnancy and childbirth · 2026Article
- Machine-Learning Models Outperform Clinicians in Predicting Postnatal Growth Failure Among Very Low Birth Weight Infants.Diagnostics (Basel, Switzerland) · 2026Article
- AI to predict extrauterine growth restriction during transitional nutrition of preterm infants: a retrospective study.Journal of perinatology : official journal of the California Perinatal Association · 2026Observational
- Predictors and Risk Scoring of Postnatal Growth Failure in Very-Low-Birth-Weight Infants.Nutrients · 2026Article
- Article
- Early life factors of precocious puberty based on Korean nationwide data.Scientific reports · 2025Article
- Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning.Pediatric research · 2025Article
- Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective Study.Children (Basel, Switzerland) · 2025Article
- Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.Microbial genomics · 2025Article
- Post-natal gestational age assessment using targeted metabolites of neonatal heel prick and umbilical cord blood: A GARBH-Ini cohort study from North India.Journal of global health · 2024Article
- Focused review on artificial intelligence for disease detection in infants.Frontiers in digital health · 2024Review
- Prediction of Postnatal Growth Failure in Very Low Birth Weight Infants Using a Machine Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- Comparative effectiveness of explainable machine learning approaches for extrauterine growth restriction classification in preterm infants using longitudinal data.Frontiers in medicine · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
purposeThe aims of the study were to develop and evaluate a machine learning model with which to predict postnatal growth failure (PGF) among very low birth weight (VLBW) infants. MATERIALS AND
methodsOf 10425 VLBW infants registered in the Korean Neonatal Network between 2013 and 2017, 7954 infants were included. PGF was defined as a decrease in Z score >1.28 at discharge, compared to that at birth. Six metrics [area under the receiver operating characteristic curve (AUROC), accuracy, precision, sensitivity, specificity, and F1 score] were obtained at five time points (at birth, 7 days, 14 days, 28 days after birth, and at discharge). Machine learning models were built using four different techniques [extreme gradient boosting (XGB), random forest, support vector machine, and convolutional neural network] to compare against the conventional multiple logistic regression (MLR) model.
resultsThe XGB algorithm showed the best performance with all six metrics across the board. When compared with MLR, XGB showed a significantly higher AUROC (
conclusionWe have shown the possibility of predicting PGF through machine learning algorithms, especially XGB. Such models may help neonatologists in the early diagnosis of high-risk infants for PGF for early intervention.
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What 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.