Annotation of Clinically Important Follow-up Recommendations in Radiology Reports

Abstract

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. The absence of an automated system to identify and track radiology recommendations is an important barrier to ensuring timely follow-up of patients especially with non-acute incidental findings on imaging studies. We are in the process of building a natural language processing (NLP) system to identify follow-up recommendations in free-text radiology reports. In this paper, we describe our efforts in creating a multiinstitutional radiology report corpus annotated for follow-up recommendation information. The annotated corpus will be used to train and test the NLP system.

Ähnliche Arbeiten