Identifying Abdominal Aortic Aneurysm Cases and Controls using Natural Language Processing of Radiology Reports

Prevalence of abdominal aortic aneurysm (AAA) is increasing due to longer life expectancy and implementation of screening programs. Patient-specific longitudinal measurements of AAA are important to understand pathophysiology of disease development and modifiers of abdominal aortic size. In this paper, we applied natural language processing (NLP) techniques to process radiology reports and developed a rule-based algorithm to identify AAA patients and also extract the corresponding aneurysm size with the examination date. AAA patient cohorts were determined by a hierarchical approach that: 1) selected potential AAA reports using keywords; 2) classified reports into AAA-case vs. non-case using rules; and 3) determined the AAA patient cohort based on a report-level classification. Our system was built in an Unstructured Information Management Architecture framework that allows efficient use of existing NLP components. Our system produced an F-score of 0.961 for AAA-case report classification with an accuracy of 0.984 for aneurysm size extraction.