Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 19, 2025

A Piglet Model of Neonatal Hypoxic-Ischemic Encephalopathy

A Piglet Model of Neonatal Hypoxic-Ischemic Encephalopathy

Published on: May 16, 2015

Implementing Explainable Machine Learning Models for Practical Prediction of Early Neonatal Hypoglycemia.

Lin-Yu Wang^1,2,3, Lin-Yen Wang^1,3,4, Mei-I Sung⁵

¹Department of Pediatrics, Chi Mei Medical Center, Tainan City 71004, Taiwan.

Diagnostics (Basel, Switzerland)

|July 27, 2024

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Comparison of refractive outcomes and visual quality between femtosecond laser-assisted arcuate keratotomy and steep-axis meridian clear corneal incision combined with Implantable Collamer Lens (ICL) implantation for the correction of moderate-to-high myopia and low astigmatism.

BMC ophthalmology·2026

Same author

Fundus tessellation density as a quantitative marker of myopia progression: a five-year longitudinal deep learning study.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2026

Same author

Pulmonary Cryptococcosis Infection in Non-HIV Patients in a Tertiary Care Center in Taiwan.

Pulmonary medicine·2026

Same author

Heat stroke-induced structural and functional impairments of cognition-relevant brain areas in mice is associated with alpha-7 nicotinic acetylcholine receptors downregulation.

European journal of pharmacology·2026

Same author

Pulmonary Cryptococcosis Infection in Non-HIV Patients in a Tertiary Care Center in Taiwan.

Pulmonary medicine·2026

Same author

Correction to: Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings.

BMC medical informatics and decision making·2026

Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026

Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026

Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026

Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026

Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026

Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026

See all related articles

This summary is machine-generated.

A new machine learning model accurately predicts neonatal hypoglycemia risk within four hours after birth. This tool aids clinicians in identifying at-risk infants for timely intervention, improving neonatal care outcomes.

Area of Science:

Neonatal Medicine
Computational Biology
Medical Informatics

Background:

Neonatal hypoglycemia is a prevalent metabolic disorder requiring early detection.
Timely identification of at-risk neonates is crucial for optimizing neonatal care strategies.

Purpose of the Study:

To develop and implement a machine learning model for predicting neonatal hypoglycemia risk.
To create a predictive application integrated into a hospital information system for clinical decision support.

Main Methods:

Retrospective analysis of 2687 neonates' electronic medical records (≥35 weeks gestational age).
Evaluation of nine machine learning models using 12 clinical features.
Selection of the best performing model based on Area Under the Receiver Operating Characteristic Curve (AUC).

Keywords:

explainability hospital information system late preterm machine learning neonatal hypoglycemia prediction model term

More Related Videos

Early Pathological and Magnetic Resonance Detection of Cerebral Injury Using a Rat Model of Neonatal Hypoxic Ischemic Encephalopathy

Early Pathological and Magnetic Resonance Detection of Cerebral Injury Using a Rat Model of Neonatal Hypoxic Ischemic Encephalopathy

Published on: October 28, 2022

Biochemical Measurement of Neonatal Hypoxia

Biochemical Measurement of Neonatal Hypoxia

Published on: August 24, 2011

Related Experiment Videos

Last Updated: Jun 19, 2025

A Piglet Model of Neonatal Hypoxic-Ischemic Encephalopathy

A Piglet Model of Neonatal Hypoxic-Ischemic Encephalopathy

Published on: May 16, 2015

Early Pathological and Magnetic Resonance Detection of Cerebral Injury Using a Rat Model of Neonatal Hypoxic Ischemic Encephalopathy

Early Pathological and Magnetic Resonance Detection of Cerebral Injury Using a Rat Model of Neonatal Hypoxic Ischemic Encephalopathy

Published on: October 28, 2022

Biochemical Measurement of Neonatal Hypoxia

Biochemical Measurement of Neonatal Hypoxia

Published on: August 24, 2011

Main Results:

The Random Forest model demonstrated strong predictive performance (AUC=0.732) with good accuracy (0.658) and sensitivity (0.682).
Key predictors identified include mode of delivery, gestational age, multiparity, respiratory distress, and low birth weight (<2500 gm).
The best model was integrated into a web-based application within the hospital information system.

Conclusions:

The developed machine learning model effectively assists clinicians in identifying neonates at risk of early hypoglycemia.
Early identification enables timely interventions and treatment, potentially improving neonatal health outcomes.
The predictive application serves as a valuable tool in neonatal intensive care units.