Predictive Modeling

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.

This study created, trained, and tested machine learning (ML) algorithms to predict emergency department returns and morbidity or mortality among returns for COVID-19 patients, with results showing ML’s potential to inform clinicians on whether admission to the hospital may be needed for the prevention of complications and to prioritize resources for higher risk patients.