Machine Learning

This research will enhance an existing mobile platform to support self-management and shared decision making for those with asthma. Researchers will use patient-reported outcomes and calculate a Respiratory Symptoms Risk Score using voice biomarkers, with the aim of improving 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 develop and validate machine learning enhanced predictive models improving the allocation of critical care pharmacists to intensive care units to reduce adverse drug events. 

This research will use artificial intelligence and machine learning to create prediction tools integrated into visualization dashboards to guide critical care pharmacists in preventing adverse drug events.

This research will develop, implement, and evaluate an artificial intelligence-enabled workflow that would allow for online reading and same-day diagnostic imaging following screening mammography in breast cancer screening clinics.