ArticleClinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
Natural Language Processing Performance for the Identification of Venous Thromboembolism in an Integrated Healthcare System.
Article in Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 28 citations in OpenAlex.
- Integrating artificial intelligence into venous thromboembolism care: Predictive models, implementation challenges, and future directions.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Automated natural language processing to identify venous thromboembolism from diagnostic imaging reports.Blood vessels, thrombosis & hemostasis · 2026Article
- Risk of venous thromboembolism associated with short-term exposure to fine particulate matter (PMResearch and practice in thrombosis and haemostasis · 2025Article
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- Role of Artificial Intelligence in the Diagnosis and Management of Pulmonary Embolism: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Methods of a cluster-randomized, type II hybrid implementation effectiveness trial to prospectively assess extended-duration thromboprophylaxis for at-risk medical patients being discharged to prevent hospital-associated venous thromboembolism.Research and practice in thrombosis and haemostasis · 2024Article
- ClotCatcher: a novel natural language model to accurately adjudicate venous thromboembolism from radiology reports.BMC medical informatics and decision making · 2023Article
- 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
- A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.NPJ digital medicine · 2022Review
- Synoptic reporting accuracy for computed tomography pulmonary arteriography among patients suspected of pulmonary embolism.Journal of the American College of Emergency Physicians open · 2022Article
- Biomarker derived risk scores predict venous thromboembolism and major bleeding among patients with COVID-19.Research and practice in thrombosis and haemostasis · 2022Article
- Comparative Frequency of Venous Thromboembolism in Patients Admitted to the Hospital with SARS-CoV-2 Infection versus Community-acquired Pneumonia.Annals of the American Thoracic Society · 2022Article
- Post-discharge thrombosis and bleeding in medical patients: A novel risk score derived from ubiquitous biomarkers.Research and practice in thrombosis and haemostasis · 2021Article
- Article
- D-dimer Thresholds to Exclude Pulmonary Embolism among COVID-19 Patients in the Emergency Department: Derivation with Independent Validation.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
- Accuracy of Various Methods for the Surveillance of Hospital-acquired Venous Thromboembolism in a Children's Hospital.Pediatric quality & safetyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Real-time identification of venous thromboembolism (VTE), defined as deep vein thrombosis (DVT) and pulmonary embolism (PE), can inform a healthcare organization's understanding of these events and be used to improve care. In a former publication, we reported the performance of an electronic medical record (EMR) interrogation tool that employs natural language processing (NLP) of imaging studies for the diagnosis of venous thromboembolism. Because we transitioned from the legacy electronic medical record to the Cerner product, iCentra, we now report the operating characteristics of the NLP EMR interrogation tool in the new EMR environment. Two hundred randomly selected patient encounters for which the imaging report assessed by NLP that revealed VTE was present were reviewed. These included one hundred imaging studies for which PE was identified. These included computed tomography pulmonary angiography-CTPA, ventilation perfusion-V/Q scan, and CT angiography of the chest/ abdomen/pelvis. One hundred randomly selected comprehensive ultrasound (CUS) that identified DVT were also obtained. For comparison, one hundred patient encounters in which PE was suspected and imaging was negative for PE (CTPA or V/Q) and 100 cases of suspected DVT with negative CUS as reported by NLP were also selected. Manual chart review of the 400 charts was performed and we report the sensitivity, specificity, positive and negative predictive values of NLP compared with manual chart review. NLP and manual review agreed on the presence of PE in 99 of 100 cases, the presence of DVT in 96 of 100 cases, the absence of PE in 99 of 100 cases and the absence of DVT in all 100 cases. When compared with manual chart review, NLP interrogation of CUS, CTPA, CT angiography of the chest, and V/Q scan yielded a sensitivity = 93.3%, specificity = 99.6%, positive predictive value = 97.1%, and negative predictive value = 99%.
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.