ArticleMedical care2018
Improved Identification of Venous Thromboembolism From Electronic Medical Records Using a Novel Information Extraction Software Platform.
Article in Medical care, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Derivation and external validation of a portable method to identify patients with pulmonary embolism from radiology reports: The READ-PE algorithm.Thrombosis research · 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
- A systematic approach to venous thromboembolism prevention: a focus on UK experience.Research and practice in thrombosis and haemostasis · 2023Article
- Racial differences in venous thromboembolism: A surveillance program in Durham County, North Carolina.Research and practice in thrombosis and haemostasis · 2022Article
- 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
- Natural language processing in low back pain and spine diseases: A systematic review.Frontiers in surgery · 2022Review
- Integrated diagnostics: the future of laboratory medicine?Biochemia medica · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
introductionThe United States federally mandated reporting of venous thromboembolism (VTE), defined by Agency for Healthcare Research & Quality Patient Safety Indicator 12 (AHRQ PSI-12), is based on administrative data, the accuracy of which has not been consistently demonstrated. We used IDEAL-X, a novel information extraction software system, to identify VTE from electronic medical records and evaluated its accuracy.
methodsMedical records for 13,248 patients admitted to an orthopedic specialty hospital from 2009 to 2014 were reviewed. Patient encounters were defined as a hospital admission where both surgery (of the spine, hip, or knee) and a radiology diagnostic study that could detect VTE was performed. Radiology reports were both manually reviewed by a physician and analyzed by IDEAL-X.
resultsAmong 2083 radiology reports, IDEAL-X correctly identified 176/181 VTE events, achieving a sensitivity of 97.2% [95% confidence interval (CI), 93.7%-99.1%] and specificity of 99.3% (95% CI, 98.9%-99.7%) when compared with manual review. Among 422 surgical encounters with diagnostic radiographic studies for VTE, IDEAL-X correctly identified 41 of 42 VTE events, achieving a sensitivity of 97.6% (95% CI, 87.4%-99.6%) and specificity of 99.8% (95% CI, 98.7%-100.0%). The performance surpassed that of AHRQ PSI-12, which had a sensitivity of 92.9% (95% CI, 80.5%-98.4%) and specificity of 92.9% (95% CI, 89.8%-95.3%), though only the difference in specificity was statistically significant (P<0.01).
conclusionIDEAL-X, a novel information extraction software system, identified VTE from radiology reports with high accuracy, with specificity surpassing AHRQ PSI-12. IDEAL-X could potentially improve detection and surveillance of many medical conditions from free text of electronic medical records.
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.