ArticleJMIR bioinformatics and biotechnology2022
Exploring the Applicability of Using Natural Language Processing to Support Nationwide Venous Thromboembolism Surveillance: Model Evaluation Study.
Article in JMIR bioinformatics and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.Blood advances · 2024Pooled it
- Development and validation of venous thromboembolism-bidirectional encoder representations from transformers (VTE-BERT) natural language processing model.Journal of thrombosis and haemostasis : JTH · 2026Article
- Artificial Intelligence in Venous Thromboembolism Prevention: A Narrative Review of Machine Learning, Deep Learning, and Natural Language Processing.Journal of cardiovascular development and disease · 2026Review
- Development and validation of machine learning models for assessing the risk of postoperative venous thromboembolism in cervical cancer patients.Scientific reports · 2025Article
- Using a transformer language model to curate a pulmonary embolism dataset from the Medical Information Mart for Intensive Care IV: MIMIC-IV-Ext-PE.Research and practice in thrombosis and haemostasis · 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
- Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot.Bleeding, thrombosis and vascular biology · 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
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Background: Venous thromboembolism (VTE) is a preventable, common vascular disease that has been estimated to affect up to 900,000 people per year. It has been associated with risk factors such as recent surgery, cancer, and hospitalization. VTE surveillance for patient management and safety can be improved via natural language processing (NLP). NLP tools have the ability to access electronic medical records, identify patients that meet the VTE case definition, and subsequently enter the relevant information into a database for hospital review. Objective: We aimed to evaluate the performance of a VTE identification model of IDEAL-X (Information and Data Extraction Using Adaptive Learning; Emory University)-an NLP tool-in automatically classifying cases of VTE by "reading" unstructured text from diagnostic imaging records collected from 2012 to 2014. Methods: After accessing imaging records from pilot surveillance systems for VTE from Duke University and the University of Oklahoma Health Sciences Center (OUHSC), we used a VTE identification model of IDEAL-X to classify cases of VTE that had previously been manually classified. Experts reviewed the technicians' comments in each record to determine if a VTE event occurred. The performance measures calculated (with 95% CIs) were accuracy, sensitivity, specificity, and positive and negative predictive values. Chi-square tests of homogeneity were conducted to evaluate differences in performance measures by site, using a significance level of .05. Results: The VTE model of IDEAL-X "read" 1591 records from Duke University and 1487 records from the OUHSC, for a total of 3078 records. The combined performance measures were 93.7% accuracy (95% CI 93.7%-93.8%), 96.3% sensitivity (95% CI 96.2%-96.4%), 92% specificity (95% CI 91.9%-92%), an 89.1% positive predictive value (95% CI 89%-89.2%), and a 97.3% negative predictive value (95% CI 97.3%-97.4%). The sensitivity was higher at Duke University (97.9%, 95% CI 97.8%-98%) than at the OUHSC (93.3%, 95% CI 93.1%-93.4%; Conclusions: The VTE model of IDEAL-X accurately classified cases of VTE from the pilot surveillance systems of two separate health systems in Durham, North Carolina, and Oklahoma City, Oklahoma. NLP is a promising tool for the design and implementation of an automated, cost-effective national surveillance system for VTE. Conducting public health surveillance at a national scale is important for measuring disease burden and the impact of prevention measures. We recommend additional studies to identify how integrating IDEAL-X in a medical record system could further automate the surveillance process.
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