title: A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.
abstract: BACKGROUND
  Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mortality, morbidity, and cost in hospitalized patients. To evaluate the success of preventive measures, accurate and efficient methods for monitoring VTE rates are needed. Therefore, we sought to determine the accuracy of statistical natural language processing (NLP) for identifying DVT and PE from electronic health record data.
  
  
  METHODS
  We randomly sampled 2000 narrative radiology reports from patients with a suspected DVT/PE in Montreal (Canada) between 2008 and 2012. We manually identified DVT/PE within each report, which served as our reference standard. Using a bag-of-words approach, we trained 10 alternative support vector machine (SVM) models predicting DVT, and 10 predicting PE. SVM training and testing was performed with nested 10-fold cross-validation, and the average accuracy of each model was measured and compared.
  
  
  RESULTS
  On manual review, 324 (16.2%) reports were DVT-positive and 154 (7.7%) were PE-positive. The best DVT model achieved an average sensitivity of 0.80 (95% CI 0.76 to 0.85), specificity of 0.98 (98% CI 0.97 to 0.99), positive predictive value (PPV) of 0.89 (95% CI 0.85 to 0.93), and an area under the curve (AUC) of 0.98 (95% CI 0.97 to 0.99). The best PE model achieved sensitivity of 0.79 (95% CI 0.73 to 0.85), specificity of 0.99 (95% CI 0.98 to 0.99), PPV of 0.84 (95% CI 0.75 to 0.92), and AUC of 0.99 (95% CI 0.98 to 1.00).
  
  
  CONCLUSIONS
  Statistical NLP can accurately identify VTE from narrative radiology reports.
authors.affiliation: Faculty of Medicine, Ingram School of Nursing, McGill University, Montreal, Canada McGill Clinical and Health Informatics Research Group, McGill University, Montreal, Canada Department of Epidemiology, Biostatics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Canada.
authors.forename: Christian M
authors.initials: CM
authors.lastname: Rochefort
authors.affiliation: McGill Clinical and Health Informatics Research Group, McGill University, Montreal, Canada Department of Epidemiology, Biostatics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Canada.
authors.forename: Aman D
authors.initials: AD
authors.lastname: Verma
authors.affiliation: McGill Clinical and Health Informatics Research Group, McGill University, Montreal, Canada Brigham and Women's Hospital, Boston, Massachusetts, USA.
authors.forename: Tewodros
authors.initials: T
authors.lastname: Eguale
authors.affiliation: McGill University Health Centre (MUHC), Montreal, Canada.
authors.forename: Todd C
authors.initials: TC
authors.lastname: Lee
authors.affiliation: McGill Clinical and Health Informatics Research Group, McGill University, Montreal, Canada Department of Epidemiology, Biostatics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Canada.
authors.forename: David L
authors.initials: DL
authors.lastname: Buckeridge
mesh_terms: D057286:Electronic Health Records; D006760:Hospitalization; D006801:Humans; D009323:Natural Language Processing; D011655:Pulmonary Embolism; D060388:Support Vector Machine; D054556:Venous Thromboembolism
keywords: acute care hospital; automated text classification; deep vein thrombosis; natural language processing; pulmonary embolism; support vector machines