ArticleJournal of the American Medical Informatics Association : JAMIA2015
A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.
Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 4 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed, 4 syntheses or guidelines pooled it, 81 citations in OpenAlex.
- Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.Blood advances · 2024Pooled it
- Are hospital nurse staffing practices associated with postoperative cardiac events and death? A systematic review.PloS one · 2019Pooled it
- Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.F1000Research · 2019Pooled it
- Prediction and Diagnosis of Venous Thromboembolism Using Artificial Intelligence Approaches: A Systematic Review and Meta-Analysis.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisPooled it
- Development and external validation of a text-matching algorithm to identify patients with lower extremity deep venous thrombosis from radiology reports at five medical centers.Thrombosis research · 2026Article
- Development and validation of venous thromboembolism-bidirectional encoder representations from transformers (VTE-BERT) natural language processing model.Journal of thrombosis and haemostasis : JTH · 2026Article
- Automated Clinical Information Extraction from Diagnostic and Nondiagnostic Radiology Reports Using Modern Language Models.Journal of imaging informatics in medicine · 2026Article
- Dynamic Ensemble Selection for Early Detection of Deep Vein Thrombosis in Fracture Patients.Journal of medical systems · 2026Article
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- Predicting Preoperative Deep Vein Thrombosis in Elderly Hip Fracture Patients Using an Interpretable Machine Learning Model.International journal of general medicine · 2025Article
- Derivation and external validation of a portable method to identify patients with pulmonary embolism from radiology reports: The READ-PE algorithm.Thrombosis research · 2024Article
- Utility of plasma D-dimer for diagnosis of venous thromboembolism after hepatectomy.World journal of clinical cases · 2024Article
- Natural Language Processing in a Clinical Decision Support System for the Identification of Venous Thromboembolism: Algorithm Development and Validation.Journal of medical Internet research · 2023Article
- Exploring the Applicability of Using Natural Language Processing to Support Nationwide Venous Thromboembolism Surveillance: Model Evaluation Study.JMIR bioinformatics and biotechnology · 2022Article
- Accuracy of identifying hospital acquired venous thromboembolism by administrative coding: implications for big data and machine learning research.Journal of clinical monitoring and computing · 2022Article
- A narrative review on the validity of electronic health record-based research in epidemiology.BMC medical research methodology · 2021Review
- Nurse staffing practices and adverse events in acute care hospitals: The research protocol of a multisite patient-level longitudinal study.Journal of advanced nursing · 2021Article
- Linking clinotypes to phenotypes and genotypes from laboratory test results in comprehensive physical exams.BMC medical informatics and decision making · 2021Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundVenous 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.
methodsWe 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.
resultsOn 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).
conclusionsStatistical NLP can accurately identify VTE from narrative radiology reports.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.