Pediatrics

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.

The study will develop, validate, implement, and assess the usability of a machine learning based clinical decision support tool designed to reduce laboratory testing overutilization in pediatric intensive care unit patients.

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.

This research will develop, evaluate, and test the efficacy of Empower NICU – A Bridge to Resources for Adjusting and Coping with Emotions (EmBRACE), a mobile health application designed to screen and monitor psychological symptoms in parents of infants hospitalized in the neonatal intensive care unit, identify those at risk, and connect parents with services, information, support, and resources.

This research will enhance an augmented reality headset used to provide real-time feedback on pediatric chest compressions. Researchers will evaluate the usability and user experience of the augmented reality cardiopulmonary resuscitation tool in an international multicenter randomized simulation study, with the aim of improving the quality of chest compressions and saving lives.

This research is developing and evaluating a machine learning algorithm that uses existing electronic health record data to predict childhood asthma treatment response and better inform personalized treatment.