Diagnosing Diabetes and Predicting Complications
Priya Sarkar (Computer Science), Lily Zerihun (Biology and Global Health), and Anqi Zhang (Biostatistics) spent ten weeks utilizing Duke Electronic Medical Record (EMR) data to identify subgroups of diabetic patients, and predict future complications associated with Type II Diabetes.
The team utilized t-Distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction of prescribed medications, medical diagnoses, laboratory tests, and patient outcomes. They then performed K-means clustering to identify meaningful clusters of similar patients and explored the sources of similarities. The team also constructed and tested statistical models to predict 13 common complications in diabetic patients, and found high predictive accuracy for several such complications when leveraging the rich data available in EMR.
- Ricardo Henao, Applied Genomics and Precision Medicine
- Liz Lorenzi, Ph.D. Candidate, Statistics
"Data+ provided an invaluable opportunity to work with motivated, hard-working students on exciting and challenging data problems. I learned so much about working with others, communicating effectively, and managing students with a variety of backgrounds. Though each of my students had a different level of statistics and coding experience, they made mentoring so easy with their hard work and interest in the project, as well as the effective organization of the summer as a whole. It was a great experience that I highly recommend to other graduate students!" Liz Lorenzi, Ph.D. Candidate, Statistics
- Lillian Zerihun, Duke University Biology & Global Health
- Priya Sarkar, Duke University Computer Science
- Anqi Zhang, Duke University Biostatistics
- Public Health
- All quantitative STEM