J Am Med Inform Assoc - A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground venous thromboembol vtes includ deep vein thrombosi dvt pulmonari embol pe associ signific mortal morbid cost hospit patient evalu success prevent measur accur effici method monitor vte rate need therefor sought determin accuraci statist natur languag process nlp identifi dvt pe electron health record datamethod random sampl narrat radiolog report patient suspect dvtpe montreal canada manual identifi dvtpe within report serv refer standard use bagofword approach train altern support vector machin svm model predict dvt predict pe svm train test perform nest fold crossvalid averag accuraci model measur comparedresult manual review report dvtposit peposit best dvt model achiev averag sensit ci specif ci posit predict valu ppv ci area curv auc ci best pe model achiev sensit ci specif ci ppv ci auc ci conclus statist nlp can accur identifi vte narrat radiolog report

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