AIMC Topic: Child, Preschool

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High-frequency monitoring enables machine learning-based forecasting of acute child malnutrition for early warning.

Proceedings of the National Academy of Sciences of the United States of America
The number of acutely food insecure people worldwide has doubled since 2017, increasing demand for early warning systems (EWS) that can predict food emergencies. Advances in computational methods, and the growing availability of near-real time remote...

Diagnostic Stewardship of Blood Cultures in the Pediatric ICU Using Machine Learning.

Hospital pediatrics
OBJECTIVE: The medical community recently experienced a severe shortage of blood culture media bottles. Rates of blood stream infection (BSI) among critically ill children are low. We sought to design a machine learning (ML) model able to identify ch...

Use of an Untrained Large Language Model for Antibiotic Prescription in Pediatric Infectious Diseases at Primary Care Settings: A Study From the Italian Society for Pediatric Infectious Diseases.

The Pediatric infectious disease journal
The development of artificial intelligence systems is revolutionizing many fields of medicine, but specific studies are still missing in pediatrics. In our study, we showed that an untrained free-to-use large language model provided reliable response...

Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation.

Pediatric transplantation
BACKGROUND: Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, particularly for candidates with congenital heart disease. Listing center organ offer acceptance practices have been identified as a factor influ...

Pediatric chest X-ray diagnosis using neuromorphic models.

Computers in biology and medicine
This research presents an innovative neuromorphic method utilizing Spiking Neural Networks (SNNs) to analyze pediatric chest X-rays (PediCXR) to identify prevalent thoracic illnesses. We incorporate spiking-based machine learning models such as Spiki...

Wrist and elbow fracture detection and segmentation by artificial intelligence using point-of-care ultrasound.

Journal of ultrasound
PURPOSE: Distal radius (wrist) and supracondylar (elbow) fractures are common in children presenting to Pediatric Emergency Departments (EDs). These fractures are treated conservatively or surgically depending on deformity severity. Radiographs are t...

Deep Learning Algorithms to Predict Differential Renal Function <40% in Unilateral Hydronephrosis Based on Key Parameters of Urinary Tract Ultrasound.

Urology
OBJECTIVE: To identify the correlation between ultrasound findings and the incidence of differential renal function (DRF) <40%, we conducted an analysis of the key parameters of urinary tract ultrasound in children with unilateral hydronephrosis. For...

Explainable transformer-based deep survival analysis in childhood acute lymphoblastic leukemia.

Computers in biology and medicine
BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common type of leukemia among children and adolescents and can be life-threatening. The incidence of new cases has been increasing in recent years. Developing a predictive model to forecast t...

Data independent acquisition proteomics and machine learning reveals that proteins associated with immunity are potential molecular markers for early diagnosis of autism.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: Early diagnosis of autism is critical to its treatment, but so far, there is no clear molecular marker for early diagnosis in children.

Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects.

Pediatric cardiology
Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnosis complications. Recent work demonstrated artificial intelligence-enhanced ECG analysis shows promise to detect ASD2 in adults. However, its applicat...