Nauder Faraday (Anesthesiology and Critical Care Medicine, School of Medicine)

Alexis Battle (Department of Biomedical Engineering, Whiting School of Engineering)

Kasper Hansen (Department of Biostatistics, Bloomberg School of Public Health)

Ali Afshar (Department of Biomedical Engineering, Whiting School of Engineering)

Our project aims to address some of the high-impact research problems in analyzing large-scale vital signs data available through Electronic Health Records. Specifically, our team plans to develop data analytics tools to visualize and interpret time-dependent vital signs data to: 1) Identify patients who experience significant variations in blood pressure for short (few minutes) and/or longer periods of time (several days). These would include, but are not limited to, patients diagnosed with heart failure, a common cause for hospital admission among people over 65 years of age. 2) Determine the relationship between variability in vital signs and clinical outcome. The overall goal of this work is to improve quality of medical care by using data analytics tools that can simplify complex data and better inform clinical decision making.