Children


Machine-Learning Prediction Model for Personalized Urinary Tract Infection Care in Children

Description

The study will develop and implement a validated machine learning model to optimize voiding cystourethrogram timing and use for diagnosing vesicoureteral reflux (VUR) in children, aiming to reduce the significant health and economic impacts of VUR and recurrent febrile urinary tract infections (fUTIs) by standardizing practices, minimizing unnecessary procedures, and ensuring timely diagnosis for those at highest risk, ultimately seeking to prevent renal injury from fUTIs.

Grant Number
K08 HS029526
Principal Investigator(s)

Improving Pediatric Donor Heart Utilization with Predictive Analytics

Description

This study aims to optimize the use of donor hearts for infants and children awaiting heart transplantation by developing predictive models to assess in real-time the potential for transplant success and to evaluate risk. Researchers plan to display these data through intuitive visualizations on a custom-built interface to reduce clinicians’ cognitive burden, enhance decision making confidence, and help ensure the best donor match for pediatric patients.

Grant Number
R21 HS029548
Principal Investigator(s)

Cardiometabolic risk in pediatric patients with intellectual and developmental disabilities.

Principal Investigator

Application of participatory ergonomics to the dissemination of a quality improvement program for optimizing blood culture use.

Principal Investigator

Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial.

Principal Investigator

Estimation of racial and language disparities in pediatric emergency department triage using statistical modeling and natural language processing.

Principal Investigator

School-Based Tele-Physiatry Assistance for Rehabilitative and Therapeutic Services (STARS) for Children with Special Health Care Needs Living in Rural and Underserved Communities - Final Report

Principal Investigator