ArticleJournal of thrombosis and haemostasis : JTH2026
Development and validation of venous thromboembolism-bidirectional encoder representations from transformers (VTE-BERT) natural language processing model.
Article in Journal of thrombosis and haemostasis : JTH, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- 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
- Multisite Real-World Validation of an Electronic Health Record-Integrated Generative Artificial Intelligence Tool for Venous Thromboembolism Risk Stratification.medRxiv : the preprint server for health sciences · 2026Article
- Temporal validation of the electronic health record cancer-associated thrombosis model in patients with low bleeding risk.Research and practice in thrombosis and haemostasis · 2026Article
- Automated Identification of Cancer-Associated Thrombosis Events via Natural Language Processing: A Systematic Review of the Literature.Bleeding, thrombosis and vascular biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
backgroundAccurate and rapid phenotyping of venous thromboembolism (VTE) in longitudinal studies is important. A natural language processing (NLP) tool externally validated in representative patients is lacking.
objectivesTo train and validate an efficient NLP model to detect incident VTE event.
methodsWe designed a novel NLP platform, NLPMed, to assist thrombosis researchers with data preprocessing, phenotype annotation, language model finetuning, and NLP application. Using clinical notes, discharge summaries, and radiology reports from patients with cancer at 2 healthcare institutions, we finetuned Bio_Clinical Bidirectional Encoder Representations from Transformers (BERT) to develop VTE-BERT. The new model was trained to detect acute VTE events and their anatomical locations longitudinally. We internally and externally validated the model's performance in 2 randomly sampled cohorts of patients with advanced cancer.
resultsThe training cohort consisted of 715 patients and 14 013 annotated notes with ≥1 VTE keyword from the Harris Health System. The internal validation cohort included 400 additional patients with 7190 VTE keyword-containing notes from Harris Health System. The external validation cohort included 400 patients with 7371 VTE keyword-containing notes from the national Veterans Affairs healthcare system. VTE-BERT was trained until it reached a precision of 95% and recall of 98% on the patient level. Using independent datasets, the model achieved precision and recall of 95% and 91% in internal validation and of 85% and 92% in external validation.
conclusionWe trained and externally validated an efficient NLP model to detect incident VTE events longitudinally. We believe its adoption will accelerate thrombosis research by improving VTE detection at scale and decreasing the time and expense involved with manual chart review in big data epidemiological studies.
Indexed as
Identifiers
What OpenQuestion holds
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.