Deep learning enabled national cancer surveillance
Georgia D. Tourassi · 2017
Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this talk I will discuss the latest deep learning technology, presenting both theoretical and practical perspectives that are relevant to natural language processing of clinical pathology reports. Using different deep learning architectures, I will present benchmark studies for various information extraction tasks and discuss their importance in supporting a comprehensive and scalable national cancer surveillance program.