Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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,  sought to determine the accuracy of statistical natural language processing (NLP) for identifying DVT and PE from electronic health record data.#N##N#Methods  randomly sampled 2000 narrative radiology reports from patients with a suspected DVT/PE in Montreal (Canada) beten 2008 and 2012.  manually identified DVT/PE within each report, which served as our reference standard. Using a bag-of-words approach,  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.#N##N#Results On manual review, 324 (16.2%) reports re DVT-positive and 154 (7.7%) re 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).#N##N#Conclusions Statistical NLP can accurately identify VTE from narrative radiology reports.
['Christian M. Rochefort', 'Aman Verma', 'Tewodros Eguale', 'Todd C. Lee', 'David L. Buckeridge']
I am writing a research paper on a idea A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data
[{"from": "human", "value": "I am writing a research paper on a idea A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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,  sought to determine the accuracy of statistical natural language processing (NLP) for identifying DVT and PE from electronic health record data.#N##N#Methods  randomly sampled 2000 narrative radiology reports from patients with a suspected DVT/PE in Montreal (Canada) beten 2008 and 2012.  manually identified DVT/PE within each report, which served as our reference standard. Using a bag-of-words approach,  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.#N##N#Results On manual review, 324 (16.2%) reports re DVT-positive and 154 (7.7%) re 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).#N##N#Conclusions Statistical NLP can accurately identify VTE from narrative radiology reports."}]