Predictive Modeling

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 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.

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. 

This research will use digital health tools leveraging patient-reported outcomes and data from electronic health records to engage individuals with multiple chronic conditions to improve understanding of individualized risk of adverse events during care transitions.