Pediatric Healthcare

AI Tool Predicts Blood Clot Risk in Hospitalized Pediatric Patients

October 24, 2023 - 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 that clinicians often hesitated to follow the tool’s recommendations. Blood clots are rare in pediatric patients, but patients who do develop...


More Articles

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...

ML Tool Identifies Biomarkers of Neonatal Opioid Withdrawal Syndrome

by Shania Kennedy

A new study published in JAMA Network Open last week described how a machine-learning (ML) tool accurately identified a set of biomarkers for neonatal opioid withdrawal syndrome (NOWS) using newborn...

How Children’s of Alabama Uses Real-Time Analytics to Support ICU Liberation

by Shania Kennedy

As many healthcare organizations move toward value-based care, they must create and implement strategies to improve the quality of care and reduce the risk of adverse patient outcomes in the short and long term. Strategies differ by type...

New Screening Tool Effective in Detecting Pediatric Asthma Risk

by Mark Melchionna

A study published in JAMA Network Open concluded that a newly developed symptom-based screening tool could detect asthma risk levels among pediatric patients as well as persistent wheezing symptoms and...

ML Tools Facilitate Early Detection of Autism Spectrum Disorder

by Shania Kennedy

A new study published in BMJ Health & Care Informatics shows that machine-learning (ML) models can accurately predict autism spectrum disorder (ASD) risk in children 18 to 30 months old using...

ML Algorithm Can Differentiate Between Inflammatory Conditions in Kids

by Mark Melchionna

A study published in Lancet Digital Health found that a machine-learning algorithm identified the differences between multisystem inflammatory syndrome in children (MIS-C) and Kawasaki Disease (KD),...

Artificial Intelligence Enhances Pediatric Tuberculosis Diagnosis Process

by Mark Melchionna

Developed by researchers at Tulane University and described in a study published in Nature Biomedical Engineering, a new blood testing system displayed the ability to enhance the pediatric tuberculosis...