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Creating a text classifier to detect radiology reports describing mediastinal findings associated with inhalational
Wendy Webber Chapman1, Gregory F Cooper, Paul Hanbury
1Center for Biomedical Informatics, University of Pittsburgh, Suite 8084 Forbes Tower, Pittsburgh, PA 15213, USA. chapman@cbmi.upmc.edu
Objective:
The aim of this study was to create a classifier for automatic detection of chest radiograph reports consistent with the mediastinal findings of inhalational anthrax.
Design:
The authors used the Identify Patient Sets (IPS) system to create a key word classifier for detecting reports describing mediastinal findings consistent with anthrax and compared their performances on a test set of 79,032 chest radiograph reports.
Measurements:
Area under the ROC curve was the main outcome measure of the IPS classifier. Sensitivity and specificity of an initial IPS model were calculated based on an existing key word search and were compared against a Boolean version of the IPS classifier.
Results:
The IPS classifier received an area under the ROC curve of 0.677 (90% CI = 0.628 to 0.772) with a specificity of 0.99 and maximum sensitivity of 0.35. The initial IPS model attained a specificity of 1.0 and a sensitivity of 0.04.
Conclusion:
The IPS system is a useful tool for helping domain experts create a statistical key word classifier for textual reports that is a potentially useful component in surveillance of radiographic findings suspicious for anthrax.