How useful is deep learning? Image credit: Forbes

Final HII seminar to discuss how deep learning can improve public health surveillance

Google uses it. Netflix uses it. It’s why these systems seem to know you and what you want to search for or watch. Deep learning is a type of machine learning model that can take in large amounts of data and use it to interpret new data as a person might, even predicting behavior. It’s been described as cutting edge of the cutting edge of artificial intelligence, and deep learning can play a crucial role in improving how public health informatics can use data to improve health and prevent disease.

In the final Health Informatics Institute seminar of the fall semester, the CDC’s Scott Lee will “Deep Learning Applied to Public Health Surveillance: Two Case Studies and an Overview of the Current State of the Art” on December 7th at 12:00 p.m. in 235 Russell Hall on the Health Sciences Campus.

Scott Lee is a statistician in the Center for Surveillance, Epidemiology, and Laboratory Services at the Centers for Disease Control and Prevention in Atlanta. His current work focuses on the application of contemporary machine learning models to problems in public health and biomedical informatics. He also has interests in experimental design and global health. He is a graduate of UGA, having received his PhD in linguistics in 2014.