Classifying Measurements in Dictated, Free-Text Radiology Reports
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.
KeywordsRadiology report natural language processing measurements
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- 3.Aronson, A.R., Lang, F.M.: An overview of MetaMap: Historical perspective and recent advances. J. Am. Med. Inform. Assoc. 17(3), 229–236 (2010)Google Scholar
- 6.Nigam, K.: Using maximum entropy for text classification. In: IJCAI 1999 Workshop on Machine Learning for Information Filtering, pp. 61–67 (1999)Google Scholar