Extracting a stroke phenotype risk factor from Veteran Health Administration clinical reports: an information content analysis
As part of the Electronic Medical Records and Genomics Network, we applied, extended and evaluated an open source clinical Natural Language Processing system, Mayo's Clinical Text Analysis and Knowledge Extraction System, for the discovery of peripheral arterial disease cases from radiology reports. The manually created gold standard consisted of 223 positive, 19 negative, 63 probable and 150 unknown cases. Overall accuracy agreement between the system and the gold standard was 0.93 as compared to a named entity recognition baseline of 0.46. Sensitivity for the positive, probable and unknown cases was 0.93-0.96, and for the negative cases was 0.72. Specificity and negative predictive value for all categories were in the 90's. The positive predictive value for the positive and unknown categories was in the high 90's, for the negative category was 0.84, and for the probable category was 0.63. We outline the main sources of errors and suggest improvements.