Radiological measurements (e.g., '3.2 x 1.4 cm') are the predominant type of quantitative data in free-text radiology reports. We report on the development and evaluation of a classifier that labels measurement descriptors with the exam they refer to: current and/or prior exam. Our classifier aggregates regular expressions as binary features in a maximum entropy model. It has average F-measure 0.942 on 2,000 annotated instances; the rule-based baseline algorithm has F-measure 0.795. Potential applications and routes for future are discussed. © 2013 Springer-Verlag.
CITATION STYLE
Sevenster, M. (2013). Classifying measurements in dictated, free-text radiology reports. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7885 LNAI, pp. 310–314). Springer Verlag. https://doi.org/10.1007/978-3-642-38326-7_43
Mendeley helps you to discover research relevant for your work.