AIMC Topic: Infant
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External Validation of a Machine Learning Model to Diagnose Kawasaki Disease.
The Journal of pediatrics
Mar 21, 2025
We investigated the generalizability of a machine learning model trained to predict Kawasaki disease using laboratory and clinical data. The algorithm performed with >89% accuracy at 3 children's hospitals across the United States, demonstrating its ...
Development of machine learning-based differential diagnosis model and risk prediction model of organ damage for severe Mycoplasma pneumoniae pneumonia in children.
Scientific reports
Mar 19, 2025
Severe Mycoplasma pneumoniae pneumonia (SMPP) poses significant diagnostic challenges due to its clinical features overlapping with those of other common respiratory diseases. This study aims to develop and validate machine learning (ML) models for t...
Machine Learning in Predicting Child Malnutrition: A Meta-Analysis of Demographic and Health Surveys Data.
International journal of environmental research and public health
Mar 18, 2025
BACKGROUND: Childhood malnutrition remains a significant global public health concern. The Demographic and Health Surveys (DHS) program provides specific data on child health across numerous countries. This meta-analysis aims to comprehensively asses...
Social and economic predictors of under-five stunting in Mexico: a comprehensive approach through the XGB model.
Journal of global health
Mar 14, 2025
BACKGROUND: The multifaceted issue of childhood stunting in low- and middle-income countries has a profound and enduring impact on children's well-being, cognitive development, and future earning potential. Childhood stunting arises from a complex in...
Predicting infant brain connectivity with federated multi-trajectory GNNs using scarce data.
Medical image analysis
Mar 13, 2025
The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Thanks to the valuable insights into the brain's anatomy, existing...
Deep learning algorithm classification of tympanostomy tube images from a heterogenous pediatric population.
International journal of pediatric otorhinolaryngology
Mar 13, 2025
IMPORTANCE: The ability to augment routine post-operative tube check appointments with at-home digital otoscopes and deep learning AI could improve health care access as well as reduce financial and time burden on families.
Artificial intelligence for weight estimation in paediatric emergency care.
BMJ paediatrics open
Mar 12, 2025
OBJECTIVE: To develop and validate a paediatric weight estimation model adapted to the characteristics of the Spanish population as an alternative to currently extended methods.
An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children.
Scientific reports
Mar 7, 2025
Kawasaki disease (KD) is a syndrome of acute systemic vasculitis commonly observed in children. Due to its unclear pathogenesis and the lack of specific diagnostic markers, it is prone to being confused with other diseases that exhibit similar sympto...
Development and Validation of an Electronic Health Record-Based, Pediatric Acute Respiratory Distress Syndrome Subphenotype Classifier Model.
Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
Mar 6, 2025
OBJECTIVE: To determine if hyperinflammatory and hypoinflammatory pediatric acute respiratory distress syndrome (PARDS) subphenotypes defined using serum biomarkers can be determined solely from electronic health record (EHR) data using machine learn...
Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images.
Scientific reports
Mar 6, 2025
In recent times, severe acute malnutrition (SAM) in India is considered a serious issue as per UNICEF 2022 records. In that record, 35.5% of children under age 5 are stunted, 19.3% are wasted, and 32% are underweight. Malnutrition, defined as these t...
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