ArticlePediatric research2019
Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning.
Article in Pediatric research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.Applied clinical informatics · 2022Pooled it
- A LASSO-based nomogram for predicting acute bilirubin encephalopathy in newborns with severe hyperbilirubinemia.Frontiers in pediatrics · 2026Article
- Multimodal feature analysis for automated neonatal jaundice assessment using machine learning.JAMIA open · 2025Article
- Neonatal hyperbilirubinemia: past lessons, current practices, and future directions.European journal of pediatrics · 2025Review
- Neonatal Jaundice Requiring Phototherapy Risk Factors in a Newborn Nursery: Machine Learning Approach.Children (Basel, Switzerland) · 2025Article
- Integrating vision transformer-based deep learning model with kernel extreme learning machine for non-invasive diagnosis of neonatal jaundice using biomedical images.Scientific reports · 2025Article
- [Risk factors and construction of a risk prediction model for readmission due to hyperbilirubinemia in neonates with ABO hemolytic disease of the newborn].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2025Article
- Leveraging Machine Learning for Pediatric Appendicitis Diagnosis: A Retrospective Study Integrating Clinical, Laboratory, and Imaging Data.Health science reports · 2025Article
- Feasibility study of texture-based machine learning approach for early detection of neonatal jaundice.Scientific reports · 2025Article
- Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians.BMC emergency medicine · 2024Article
- Low-dimensional neural ODEs and their application in pharmacokinetics.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Developing and evaluating a predictive model for neonatal hyperbilirubinemia based onFrontiers in pediatrics · 2024Article
- Early-Term Neonates Demonstrate a Higher Likelihood of Requiring Phototherapy Compared to Those Born Full-Term.Children (Basel, Switzerland) · 2023Article
- Developing a model to predict neonatal respiratory distress syndrome and affecting factors using data mining: A cross-sectional study.International journal of reproductive biomedicine · 2023Article
- The effect of well-known burn-related features on machine learning algorithms in burn patients' mortality prediction.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2023Article
- Prediction of extubation failure among low birthweight neonates using machine learning.Journal of perinatology : official journal of the California Perinatal Association · 2023Article
- Validating the early phototherapy prediction tool across cohorts.Frontiers in pediatrics · 2023Article
- Development of a machine-learning based model for predicting multidimensional outcome after surgery for degenerative disorders of the spine.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Article
- A data-driven health index for neonatal morbidities.iScience · 2022Article
- Maternal disease factors associated with neonatal jaundice: a case-control study.BMC pregnancy and childbirth · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning models may enhance the early detection of clinically relevant hyperbilirubinemia based on patient information available in every hospital.
methodsWe conducted a longitudinal study on preterm and term born neonates with serial measurements of total serum bilirubin in the first two weeks of life. An ensemble, that combines a logistic regression with a random forest classifier, was trained to discriminate between the two classes phototherapy treatment vs. no treatment.
resultsOf 362 neonates included in this study, 98 had a phototherapy treatment, which our model was able to predict up to 48 h in advance with an area under the ROC-curve of 95.20%. From a set of 44 variables, including potential laboratory and clinical confounders, a subset of just four (bilirubin, weight, gestational age, hours since birth) suffices for a strong predictive performance. The resulting early phototherapy prediction tool (EPPT) is provided as an open web application.
conclusionEarly detection of clinically relevant hyperbilirubinemia can be enhanced by the application of machine learning. Existing guidelines can be further improved to optimize timing of bilirubin measurements to avoid toxic hyperbilirubinemia in high-risk patients while minimizing unneeded measurements in neonates who are at low risk.
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