UCLA

Registries provide a valuable tool for cancer research and for enabling decision support systems. However, populating cancer registries with information from medical records can be a tedious and bottlenecking process. This thesis presents an annotator system that automatically extracts data elements from lung cancer radiology reports to populate a lung cancer registry. Annotators systems such as this utilize natural language processing (NLP) techniques to locate concepts from a text source. A web-based framework that wraps the annotator system into a graphical user interface for researchers and clinicians is also discussed.