Predictive Modeling


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)

An AI-Directed CDS Tool to Reduce Iron Deficiency Anemia in Pregnancy: A Randomized Controlled Trial (AID-IDA Trial)

Description

This study will develop and establish the efficacy of an actionable predictive model to identify pregnant individuals at high risk for postpartum hemorrhage which can be used in combination with a clinical decision support tool to reduce the risk of hemorrhage-related morbidity and improve maternal health outcomes.

Grant Number
R21 HS030148
Principal Investigator(s)

An Electronic Health Record-Based Screening Tool to Support Safe Discharges of COVID-19 Patients in the Emergency Department – Final Report

Principal Investigator

Predictive modeling to identify children with complex health needs at risk for hospitalization.

Principal Investigator

Applying machine learning in distributed data networks for pharmacoepidemiologic and pharmacovigilance studies: Opportunities, challenges, and considerations.

Principal Investigator