The article by Oh et al describes the EEG findings in a group of infants diagnosed with abusive head trauma but does not pay attention to the symptoms that triggered admission.
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| http://dx.doi.org/10.1177/0883073819876136 | DOI Listing |
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Predicting fall parameters from infant skull fractures using machine learning.
Biomech Model Mechanobiol
January 2025
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
When infants are admitted to the hospital with skull fractures, providers must distinguish between cases of accidental and abusive head trauma. Limited information about the incident is available in such cases, and witness statements are not always reliable. In this study, we introduce a novel, data-driven approach to predict fall parameters that lead to skull fractures in infants in order to aid in determinations of abusive head trauma.
View Article and Find Full Text PDFFactors Associated with Abusive Head Trauma in Young Children Presenting to Emergency Medical Services Using a Large Language Model.
Prehosp Emerg Care
January 2025
Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado.
Objectives: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Large Language Models (LLM) may identify rare presentations like AHT through factors not found in structured data.
View Article and Find Full Text PDFUsing supervised machine learning and ICD10 to identify non-accidental trauma in pediatric trauma patients in the Maryland Health Services Cost Review Commission dataset.
Child Abuse Negl
January 2025
Johns Hopkins School of Medicine, United States of America. Electronic address:
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.
Methods: We analyzed data from the Maryland Health Services Cost Review Commission (2015-2020) for patients aged 0-19 years.
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