title: Natural Language Processing Techniques for Extracting and Categorizing Finding Measurements in Narrative Radiology Reports.
abstract: BACKGROUND
  Accumulating quantitative outcome parameters may contribute to constructing a healthcare organization in which outcomes of clinical procedures are reproducible and predictable. In imaging studies, measurements are the principal category of quantitative para meters.
  
  
  OBJECTIVE
  The purpose of this work is to develop and evaluate two natural language processing engines that extract finding and organ measurements from narrative radiology reports and to categorize extracted measurements by their "temporality".
  
  
  METHODS
  The measurement extraction engine is developed as a set of regular expressions. The engine was evaluated against a manually created ground truth. Automated categorization of measurement temporality is defined as a machine learning problem. A ground truth was manually developed based on a corpus of radiology reports. A maximum entropy model was created using features that characterize the measurement itself and its narrative context. The model was evaluated in a ten-fold cross validation protocol.
  
  
  RESULTS
  The measurement extraction engine has precision 0.994 and recall 0.991. Accuracy of the measurement classification engine is 0.960.
  
  
  CONCLUSIONS
  The work contributes to machine understanding of radiology reports and may find application in software applications that process medical data.
authors.affiliation: Philips Research, Briarcliff Manor , NY, United States.
authors.forename: M
authors.initials: M
authors.lastname: Sevenster
authors.affiliation: Philips Research , Eindhoven, Netherlands.
authors.forename: J
authors.initials: J
authors.lastname: Buurman
authors.affiliation: University of Chicago Hospitals , Chicago, IL, United States.
authors.forename: P
authors.initials: P
authors.lastname: Liu
authors.affiliation: Philips Healthcare , Best, Netherlands.
authors.forename: J F
authors.initials: JF
authors.lastname: Peters
authors.affiliation: University of Chicago Hospitals , Chicago, IL, United States.
authors.forename: P J
authors.initials: PJ
authors.lastname: Chang
mesh_terms: D057225:Data Mining / Q000379:methods*; D009323:Natural Language Processing; D011871:Radiology; D058028:Research Report; D012984:Software
keywords: Natural language processing; maximum entropy; measurement; quantitative imaging; radiology report