Deep venous thrombosis and pulmonary embolism are diseases associated with significant morbidity and mortality. Known risk factors are attributed for only slight majority of venous thromboembolic disease (VTE) with the remainder of risk presumably related to unidentified genetic factors. We designed a general purpose Natural Language (NLP) algorithm to retrospectively capture both acute and historical cases of thromboembolic disease in a de-identified electronic health record. Applying the NLP algorithm to a separate evaluation set found a positive predictive value of 84.7% and sensitivity of 95.3% for an F-measure of 0.897, which was similar to the training set of 0.925. Use of the same algorithm on problem lists only in patients without VTE ICD-9s was found to be the best means of capturing historical cases with a PPV of 83%. NLP of VTE ICD-9 positive cases and non-ICD-9 positive problem lists provides an effective means for capture of both acute and historical cases of venous thromboembolic disease.
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