Summary
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.
Objectives: 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.
Citation: Sevenster M, Buurman J, Liu P, Peters JF, Chang PJ. Natural language processing techniques
for extracting and categorizing finding measurements in narrative radiology reports.
Appl Clin Inform 2015; 6: 600–610
http://dx.doi.org/10.4338/ACI-2014-11-RA-0110
Keywords
Natural language processing - radiology report - measurement - maximum entropy - quantitative
imaging
