Knowledge-Based Extraction of Measurement-Entity Relations from German Radiology Reports

A large percentage of relevant radiologic patient information is currently only available in unstructured formats such as free text reports. In particular measurements are important since they are comparable and thus provide insight into the change of the health status over time, for example in response to some treatment. In radiology most of the measurements in reports describe the size of anatomical entities. Even though it is possible to extract measurements and anatomical entities from text using standard information extraction techniques, it is difficult to extract the relation between the measurement and the corresponding anatomical entity. Here we present a knowledge-based approach to extract this relation for size measurements using a model about typical size descriptions of anatomical entities in combination with hierarchical knowledge of existing medical ontologies. We evaluate our approach on two data sets of German radiology reports reaching an F1-measure of 0.85 and 0.79 respectively.