Context-Driven Concept Annotation in Radiology Reports: Anatomical Phrase Labeling.

During a radiology reading session, it is common that the radiologist refers back to the prior history of the patient for comparison. As a result, structuring of radiology report content for seamless, fast, and accurate access is in high demand in Radiology Information Systems (RIS). A common approach for defining a structure is based on the anatomical sites of radiological observations. Nevertheless, the language used for referring to and describing anatomical regions varies quite significantly among radiologists. Conventional approaches relying on ontology-based keyword matching fail to achieve acceptable precision and recall in anatomical phrase labeling in radiology reports due to such variation in language. In this work, a novel context-driven anatomical labeling framework is proposed. The proposed framework consists of two parallel Recurrent Neural Networks (RNN), one for inferring the context of a sentence and the other for word (token)-level labeling. The proposed framework was trained on a large set of radiology reports from a clinical site and evaluated on reports from two other clinical sites. The proposed framework outperformed the state-of-the-art approaches, especially in correctly labeling ambiguous cases.