Project Results: Working with a Duke Hospital dataset of 5 Doppler ultrasound recordings taken from each of 38 patients, the team used machine-learning to predict whether or not a patient was healthy from the recordings. They extracted features from the recordings using a variety of tools from signal processing, visualized the separation of healthy and unhealthy patients in this feature space, and built a competitive classifier using standard supervised-learning tools. They had the opportunity to present their findings to Duke's Provost and to senior leadership within Duke Hospital and the Duke Clinical Research Institute.
Faculty Leads: Wilkins Aquino, Leila Mureebe
Project Manager: Kyle Burris
Click here for the Executive Summary
"Participating in Data+ definitely changed my perception of Data Science research. It was more interdisciplinary than I expected, and the opportunity to work with experts across different fields (Medicine, Civil Engineering, Statistics) was a defining aspect of my Data+ experience." - Serge Assad, Biomedical Engineering, Electrical & Computer Engineering
"The project mentor was fantastic. The three students I worked with were superb. We were able to make great progress that will lead to journal publications and grant proposals." — Wilkins Aquino, Professor in the Department of Civil and Environmental Engineering. Pratt School of Engineering
