Using supervised machine learning and ICD10 to identify non-accidental trauma in pediatric trauma patients in the Maryland Health Services Cost Review Commission dataset.

Journal: Child abuse & neglect

PMID: 39799844

Abstract

BACKGROUND: Identifying non-accidental trauma (NAT) in pediatric trauma patients is challenging. We developed a machine learning model that uses demographic characteristics and ICD10 codes to detect the first diagnosis of NAT.

Authors

Titus Hou

University of Illinois College of Medicine - Peoria Campus, Bloomberg School of Public Health, United States of America. Electronic address: thou7@uic.edu.
Daniel An

Johns Hopkins School of Medicine, United States of America. Electronic address: dan1@jhmi.edu.
Caitlin W Hicks

Division of Vascular Surgery and Endovascular Therapy, 1466The Johns Hopkins Medical Institutions, Baltimore, MD, USA.
Elliott Haut

Johns Hopkins School of Medicine, United States of America. Electronic address: ehaut1@jhmi.edu.
Isam W Nasr

Johns Hopkins School of Medicine, United States of America. Electronic address: inasr1@jhmi.edu.

Keywords

Adolescent Child Child Abuse Child, Preschool Female Humans Infant Infant, Newborn International Classification of Diseases Machine Learning Male Maryland Supervised Machine Learning Wounds and Injuries Young Adult

External Resources

View on PubMed Access via DOI PubMed (39799844)

Using supervised machine learning and ICD10 to identify non-accidental trauma in pediatric trauma patients in the Maryland Health Services Cost Review Commission dataset.

Abstract

Authors

Keywords

External Resources

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