ArticlePloS one2020
Machine learning models for identifying preterm infants at risk of cerebral hemorrhage.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 26 citations in OpenAlex.
- Amplitude-integrated electroencephalography for early diagnosis and severity assessment of neonatal intracranial hemorrhage: a multicenter retrospective study.Italian journal of pediatrics · 2026Article
- Identification of risk factors of cystic periventricular leukomalacia in preterm infants.Frontiers in pediatrics · 2026Article
- Construction and Validation of a Risk Prediction Model for Early Severe Intraventricular Hemorrhage in Very Low Birth Weight Infants.The Kaohsiung journal of medical sciences · 2025Article
- Current progress and future prospects of machine learning in the diagnosis of neonatal encephalopathy: a narrative review.Translational pediatrics · 2025Review
- Early prediction of intraventricular hemorrhage in very low birth weight infants using deep neural networks with attention in low-resource settings.Scientific reports · 2025Article
- Development of a machine learning model to identify intraventricular hemorrhage using time-series analysis in preterm infants.Scientific reports · 2024Article
- Predicting severe intraventricular hemorrhage or early death using machine learning algorithms in VLBWI of the Korean Neonatal Network Database.Scientific reports · 2024Article
- Assessment of intraventricular hemorrhage risk in preterm infants using mathematically simulated cerebral blood flow.Frontiers in neurology · 2024Article
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- Article
- Prediction of extubation failure among low birthweight neonates using machine learning.Journal of perinatology : official journal of the California Perinatal Association · 2023Article
- The use of machine learning and artificial intelligence within pediatric critical care.Pediatric research · 2023Review
- Glycocalyx Sensing with a Mathematical Model of Acoustic Shear Wave Biosensor.Bioengineering (Basel, Switzerland) · 2022Article
- Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants.Yonsei medical journal · 2022Article
- Machine Learning Models for Predicting Mortality in 7472 Very Low Birth Weight Infants Using Data from a Nationwide Neonatal Network.Diagnostics (Basel, Switzerland) · 2022Article
- A Setup for Camera-Based Detection of Simulated Pathological States Using a Neonatal Phantom.Sensors (Basel, Switzerland) · 2022Article
- [Biomarkers and neuromonitoring for prognosis of development after perinatal brain damage].Monatsschrift Kinderheilkunde : Organ der Deutschen Gesellschaft fur Kinderheilkunde · 2022Review
- Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit.Journal of personalized medicine · 2021Article
- Effect of Minimally Invasive Puncture Drainage and Conservative Treatment on Prognosis of Patients with Cerebral Hemorrhage.Journal of healthcare engineering · 2021Article
- Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury.Frontiers in pediatrics · 2021Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifactorial. Among the risk factors are impaired cerebral autoregulation, infections, and coagulation disorders. Machine learning methods allow the identification of combinations of clinical factors to best differentiate preterm infants with intra-cerebral bleeding and the development of models for patients at risk of cerebral hemorrhage. In the current study, a Random Forest approach is applied to develop such models for extremely and very preterm infants (23-30 weeks gestation) based on data collected from a cohort of 229 individuals. The constructed models exhibit good prediction accuracy and might be used in clinical practice to reduce the risk of cerebral bleeding in prematurity.
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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.