ArticleResearch and practice in thrombosis and haemostasis2025
Using a transformer language model to curate a pulmonary embolism dataset from the Medical Information Mart for Intensive Care IV: MIMIC-IV-Ext-PE.
Article in Research and practice in thrombosis and haemostasis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Large language models for pneumonia detection in radiology reports via text analysis.PLOS digital health · 2026Article
- Automated Identification of Cancer-Associated Thrombosis Events via Natural Language Processing: A Systematic Review of the Literature.Bleeding, thrombosis and vascular biology · 2026Article
- ICU mortality prediction via multimodal learning on MIMIC-IV clinical data.Bioinformatics advances · 2026Article
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7 authors.
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Abstract
Background: Pulmonary embolism (PE) is a leading cause of preventable in-hospital mortality. Advances in diagnosis, risk stratification, and prevention can improve outcomes. Large, publicly available datasets are needed to move research forward, but are lacking in the field of hemostasis and thrombosis. Objectives: In this study, we experiment using a machine learning language model to automatically add PE labels to a large dataset. Methods: We extracted all computed tomography pulmonary angiography radiology reports ( Results: Venous thromboembolism-BERT had a sensitivity of 92.4% and a positive predictive value of 87.8% in all 19,942 computed tomography pulmonary angiography reports. Diagnosis codes had a sensitivity of 95.4% and a positive predictive value of 83.8% in the subset of 11,990 reports with an associated discharge diagnosis code. Conclusion: We successfully added nearly 20,000 PE labels to the publicly available Medical Information Mart for Intensive Care IV database and demonstrated how a transformer language model can automate and accelerate hematologic research.
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