Pediatric Healthcare

Online big data dashboard to help map enteric infectious diseases

March 28, 2024 - A team from the University of Virginia (UVA) is developing an online big data dashboard to map enteric infectious disease burden in low- and middle-income countries, which researchers and public health stakeholders can use to guide decision-making. The tool, outlined in a recent PLOS One study, is part of UVA’s Planetary Child Health &...

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Machine learning enables prediction of pediatric urinary condition

by Shania Kennedy

Researchers from Boston Children's Hospital have developed a machine learning model to predict risk of dilating vesicoureteral reflux (VUR) in infants with hydronephrosis – a condition in...

AI Smartphone Tool Accurately Diagnoses Pediatric Ear Infections

by Shania Kennedy

Researchers from the University of Pittsburgh (Pitt) and UPMC have developed a smartphone application that leverages artificial intelligence (AI) to diagnose ear infections, specifically acute otitis...

Precision Medicine Tool May Personalize Pediatric Cancer Treatment

by Shania Kennedy

Researchers from University of Utah Health have developed a precision medicine tool to provide insights into how medications can best be used in pediatric cancer populations. Drug dynamics, or...

Deep Learning Tool May Help Detect Pediatric Rheumatic Heart Disease

by Shania Kennedy

Researchers from Children’s National Hospital have developed a deep learning (DL) to detect latent rheumatic heart disease (RHD) in children, which may improve case identification and treatment...

Exploring a Framework for Pediatric Data Use in Health AI Research

by Shania Kennedy

The number of artificial intelligence (AI) and machine learning (ML)-enabled medical devices authorized by the United States Food and Drug Administration (FDA) has risen in recent years as healthcare organizations have become increasingly...

AI Tool Predicts Blood Clot Risk in Hospitalized Pediatric Patients

by Shania Kennedy

Researchers at Vanderbilt University Medical Center (VUMC) have developed an artificial intelligence (AI) tool capable of accurately identifying the risk of blood clots in pediatric patients, but found...

Risk Stratification May Reduce Unnecessary Pediatric Oophorectomies

by Shania Kennedy

A consensus-based, preoperative risk stratification algorithm may help reduce unnecessary oophorectomies in pediatric and adolescent patients with benign ovarian disease, according to a study published...

Machine Learning Tools Flag Predictors of Fetal Heart Rate Changes

by Shania Kennedy

Researchers have developed machine learning (ML) methods that can accurately identify predictors associated with fetal heart rate changes following neuraxial analgesia in healthy pregnant patients,...

Deep-Learning Model Shows Promise in Measuring Joint Attention

by Mark Melchionna

A study published in JAMA Network Open describes a deep-learning (DL) model that showed the ability to determine differences between children with autism spectrum disorder (ASD) and typical development...

Federal Grant to Bolster Neonatal Research Consortium

by Shania Kennedy

The University of New Mexico (UNM) Health Sciences won a renewal of a federal grant to participate in the Neonatal Research Network, a data-sharing consortium focused on improving care for high-risk...

AI Partnership to Improve Pediatric Hospital Operations, Care Coordination

by Shania Kennedy

Children's Mercy Kansas City and GE HealthCare have launched the Patient Progression Hub – a hospital operations center designed to leverage artificial intelligence (AI), predictive...

Machine-Learning Model Predicts Risk of Pediatric Deterioration

by Sarai Rodriguez

Nationwide Children's Hospital developed and deployed a machine-learning (ML) model that uses the deterioration risk index to promptly predict hospitalized children at risk for pediatric...

Wearables, ML Predict ADHD, Sleep Problems in Children

by Shania Kennedy

A study published last month in JAMA Network Open describes how researchers combined data from wearable devices and machine-learning (ML) methodologies to help predict attention-deficit/hyperactivity...

Model Predicts Neurodevelopmental Outcomes, Death in Preterm Infants

by Shania Kennedy

A study published earlier this month in JAMA Network Open demonstrates that a newly-developed multimodal model using brain function information and other risk factors can improve the prediction of...

ML Model Predicts Prematurity Complications in Newborns Using EMR Data

by Shania Kennedy

In a study published this week in Science Translational Medicine, researchers from the Stanford School of Medicine revealed that a machine-learning (ML) algorithm could predict prematurity...

Machine-Learning Algorithm Helps Monitor Movement Patterns in Infants

by Mark Melchionna

After receiving a grant from the National Science Foundation, a group of researchers from Dell Children’s Medical Center of Central Texas created a machine-learning (ML) algorithm to track the...

Predictive Analytics Tools Accurately Detect Pediatric Autism

by Shania Kennedy

A study published last week in JAMA Network Open describes how a set of EHR data-based predictive analytics tools can detect early autism using patient data collected before 1 year of age. According...

Researchers Identify Biomarkers, Potential Utility of ML in ADHD

by Shania Kennedy

Researchers from Yale School of Medicine have identified biomarkers of attention-deficit/hyperactivity disorder (ADHD) using MRI exams and showcased the potential role of machine learning (ML)-based...

Predictive Analytics Use EHR Data for Hospital Readmissions

by Shania Kennedy

In a new study published last week in JAMA Network Open, researchers found that a suite of predictive analytics tools leveraging readily available EHR data can accurately identify all-cause 30-day...

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