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Yong Hoon Jun

Biographic Data

ID8022276
NAMEYong Hoon Jun
GIVEN NAMESYong Hoon
FAMILY NAMEJun
SIGNATUREJUN Y H
AFFILIATIONSInha University Hospital
ORCID0000-0002-0145-3306
VERIFIEDYes
TOTAL WORKS2
TOTAL CITATIONS0
WORKS AS AUTHOR2
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2022
LATEST PUBLICATION YEAR2024
H-INDEX0
  • A Predictive Model for Perinatal Brain Injury Using Machine Learning Based on Early Birth Data

    Open Access•Gye Rok Jeon, Ga Won Jeon et al.•ARTICLE•Children•2024

    the gradient boosting model with ADASYN oversampling effectively predicts perinatal brain injury, potentially improving early detection for predicting long-term outcomes, reducing unnecessary MRI scans, and lowering healthcare costs

  • Neonatal Abusive Head Trauma without External Injuries

    Open Access•Seokwon Yoon, Ju Young Lee et al.•ARTICLE•Children•2022

    The term "shaken baby syndrome" has been replaced by "abusive head trauma (AHT)" based on the mechanism of injury. The reported mortality rate of AHT ranges from 10% to 30%. Up to two-thirds of survivors suffer from serious long-term disabilities. Thus, an expeditious and accurate diagnosis is crucial to prevent further abuse that might result in death or serious disabilities. It remains a challenge for physicians to diagnose AHT when parents do …

No prominent works on this page.

  • Neonatal Abusive Head Trauma without External Injuries

    Open Access•Seokwon Yoon, Ju Young Lee et al.•ARTICLE•Children•2022

    The term "shaken baby syndrome" has been replaced by "abusive head trauma (AHT)" based on the mechanism of injury. The reported mortality rate of AHT ranges from 10% to 30%. Up to two-thirds of survivors suffer from serious long-term disabilities. Thus, an expeditious and accurate diagnosis is crucial to prevent further abuse that might result in death or serious disabilities. It remains a challenge for physicians to diagnose AHT when parents do …

  • A Predictive Model for Perinatal Brain Injury Using Machine Learning Based on Early Birth Data

    Open Access•Gye Rok Jeon, Ga Won Jeon et al.•ARTICLE•Children•2024

    the gradient boosting model with ADASYN oversampling effectively predicts perinatal brain injury, potentially improving early detection for predicting long-term outcomes, reducing unnecessary MRI scans, and lowering healthcare costs

Acquired brain injury (1 work) · Artificial Intelligence (1 work) · Cardiac Arrest and Resuscitation (1 work) · Child abuse (1 work) · Child Abuse and Related Trauma (1 work) · Computer Science (1 work) · Head trauma (1 work) · Homicide, Infanticide, and Child Abuse (1 work) · Injury prevention (1 work) · Intensive care medicine (1 work)

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