AIMC Topic: Infant

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Factors Associated with Abusive Head Trauma in Young Children Presenting to Emergency Medical Services Using a Large Language Model.

Prehospital emergency care Jan 17, 2025
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 Larg...
Male Humans Infant Child Abuse Natural Language Processing Artificial Intelligence Large Language Models Retrospective Studies Child, Preschool Craniocerebral Trauma Female Emergency Medical Services
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Derivation and validation of a clinical predictive model for longer duration diarrhea among pediatric patients in Kenya using machine learning algorithms.

BMC medical informatics and decision making Jan 15, 2025
BACKGROUND: Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machin...
Infant Child, Preschool Diarrhea Algorithms Kenya Humans Female Machine Learning Male
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Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.

Drug design, development and therapy Jan 13, 2025
PURPOSE: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML)...
Adolescent Child, Preschool Child Antitubercular Agents Tuberculosis Humans Female Machine Learning Chemical and Drug Induced Liver Injury Risk Factors Retrospective Studies Male Infant
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Using 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 & neglect Jan 11, 2025
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.
Humans Child Machine Learning Supervised Machine Learning International Classification of Diseases Wounds and Injuries Maryland Female Male Child Abuse Infant, Newborn Infant Young Adult Adolescent Child, Preschool
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Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction.

Ecotoxicology and environmental safety Jan 11, 2025
Breast milk is essential for infant health, but the transfer of xenobiotic chemicals poses significant risks. Ethical challenges in clinical trials necessitate the use of in vitro predictive models to assess chemical exposure risks in breastfeeding i...
Risk Assessment Infant Milk, Human Environmental Exposure Breast Feeding Xenobiotics Humans Machine Learning Female
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A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles.

Orphanet journal of rare diseases Jan 9, 2025
BACKGROUND: Glycogen storage disease (GSD) Ia is an ultra-rare inherited disorder of carbohydrate metabolism. Patients often present in the first months of life with fasting hypoketotic hypoglycemia and hepatomegaly. The diagnosis of GSD Ia relies on...
Child, Preschool Carnitine Glycogen Storage Disease Type I Glycogen Storage Disease Infant, Newborn Male Infant Humans Machine Learning Biomarkers Female
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Development and external validation of a machine learning model for brain injury in pediatric patients on extracorporeal membrane oxygenation.

Critical care (London, England) Jan 9, 2025
BACKGROUND: Patients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of brain injury, contributing to significant morbidity and mortality. This study aimed to employ machine learning (ML) techniques to predict brain injury ...
Male Infant China Adolescent Humans Machine Learning Retrospective Studies Female Brain Injuries Extracorporeal Membrane Oxygenation Cohort Studies Child, Preschool Child
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Predicting early cessation of exclusive breastfeeding using machine learning techniques.

PloS one Jan 9, 2025
BACKGROUND: Identification of mother-infant pairs predisposed to early cessation of exclusive breastfeeding is important for delivering targeted support. Machine learning techniques enable development of transparent prediction models that enhance cli...
Pregnancy Female Infant, Newborn Adult Male Infant Breast Feeding ROC Curve Mothers Denmark Humans Machine Learning
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Utilizing natural language processing to identify pediatric patients experiencing status epilepticus.

Seizure Jan 8, 2025
PURPOSE: Compare the identification of patients with established status epilepticus (ESE) and refractory status epilepticus (RSE) in electronic health records (EHR) using human review versus natural language processing (NLP) assisted review.
Child Support Vector Machine Natural Language Processing Status Epilepticus Male Infant Adolescent Child, Preschool Electronic Health Records Female Young Adult Humans Machine Learning
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Early childhood caries risk prediction using machine learning approaches in Bangladesh.

BMC oral health Jan 8, 2025
BACKGROUND: In the last years, artificial intelligence (AI) has contributed to improving healthcare including dentistry. The objective of this study was to develop a machine learning (ML) model for early childhood caries (ECC) prediction by identifyi...
Machine Learning Female Risk Factors Adult Humans Dental Caries Mothers Health Behavior Bangladesh Male Risk Assessment Infant Child, Preschool
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