AIMC Topic: Infant

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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)...
Risk Factors Chemical and Drug Induced Liver Injury Humans Male Machine Learning Child, Preschool Retrospective Studies Infant Child Female Adolescent Antitubercular Agents Tuberculosis
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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.
Child Abuse Supervised Machine Learning International Classification of Diseases Wounds and Injuries Maryland Infant Humans Adolescent Machine Learning Child, Preschool Child Female Infant, Newborn Young Adult Male
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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...
Breast Feeding Xenobiotics Risk Assessment Humans Infant Machine Learning Milk, Human Female Environmental Exposure
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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...
Infant Biomarkers Humans Child, Preschool Female Carnitine Machine Learning Glycogen Storage Disease Glycogen Storage Disease Type I Infant, Newborn Male
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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 ...
Extracorporeal Membrane Oxygenation Cohort Studies Child, Preschool Child Brain Injuries Male Infant China Adolescent Machine Learning Retrospective Studies Female Humans
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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...
Mothers Infant ROC Curve Denmark Breast Feeding Humans Machine Learning Pregnancy Female Infant, Newborn Adult Male
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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.
Female Young Adult Male Infant Adolescent Child, Preschool Child Support Vector Machine Humans Machine Learning Electronic Health Records Natural Language Processing Status Epilepticus
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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...
Humans Machine Learning Female Infant Child, Preschool Dental Caries Mothers Risk Factors Adult Male Risk Assessment Health Behavior Bangladesh
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Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.

European respiratory review : an official journal of the European Respiratory Society Jan 8, 2025
INTRODUCTION: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the me...
Hypersensitivity Respiratory Sounds Age Factors Predictive Value of Tests Age of Onset Eczema Prognosis Female Male Risk Factors Asthma Risk Assessment Infant Humans Disease Progression Diagnosis, Computer-Assisted Machine Learning Adolescent Child, Preschool Child
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Fine-Grained Fidgety Movement Classification Using Active Learning.

IEEE journal of biomedical and health informatics Jan 7, 2025
Typically developing infants, between the corrected age of 9-20 weeks, produce fidgety movements. These movements can be identified with the General Movement Assessment, but their identification requires trained professionals to conduct the assessmen...
Humans Algorithms Image Processing, Computer-Assisted Supervised Machine Learning Video Recording Problem-Based Learning Movement Female Infant, Newborn Male Infant
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