ArticleBMC bioinformatics2014
Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 3 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 3 syntheses or guidelines pooled it, 108 citations in OpenAlex.
- Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.Blood advances · 2024Pooled it
- Extracting cancer concepts from clinical notes using natural language processing: a systematic review.BMC bioinformatics · 2023Pooled it
- Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.F1000Research · 2019Pooled it
- Integrating artificial intelligence into venous thromboembolism care: Predictive models, implementation challenges, and future directions.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Development and validation of venous thromboembolism-bidirectional encoder representations from transformers (VTE-BERT) natural language processing model.Journal of thrombosis and haemostasis : JTH · 2026Article
- A workflow utilizing general-purpose large language models for efficient structuring and data mining of bone scintigraphy narratives.Scientific reports · 2026Article
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- Extracting Pulmonary Embolism Diagnoses From Radiology Impressions Using GPT-4o: Large Language Model Evaluation Study.JMIR medical informatics · 2025Article
- Evaluation of GPT-4 ability to identify and generate patient instructions for actionable incidental radiology findings.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- An Enhanced Mask R-CNN Approach for Pulmonary Embolism Detection and Segmentation.Diagnostics (Basel, Switzerland) · 2024Article
- LNDb v4: pulmonary nodule annotation from medical reports.Scientific data · 2024Article
- Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot.Bleeding, thrombosis and vascular biology · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- An Alternative Application of Natural Language Processing to Express a Characteristic Feature of Diseases in Japanese Medical Records.Methods of information in medicine · 2023Article
- Artificial Intelligence to Improve Patient Understanding of Radiology Reports.The Yale journal of biology and medicine · 2023Review
- Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study.Thrombosis and haemostasis · 2023Article
- BERT-based Transfer Learning in Sentence-level Anatomic Classification of Free-Text Radiology Reports.Radiology. Artificial intelligence · 2023Article
- Natural Language Processing Model for Identifying Critical Findings-A Multi-Institutional Study.Journal of digital imaging · 2023Article
- DeepMPM: a mortality risk prediction model using longitudinal EHR data.BMC bioinformatics · 2022Article
- Automatic detection of actionable findings and communication mentions in radiology reports using natural language processing.European radiology · 2022Article
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNatural Language Processing (NLP) has been shown effective to analyze the content of radiology reports and identify diagnosis or patient characteristics. We evaluate the combination of NLP and machine learning to detect thromboembolic disease diagnosis and incidental clinically relevant findings from angiography and venography reports written in French. We model thromboembolic diagnosis and incidental findings as a set of concepts, modalities and relations between concepts that can be used as features by a supervised machine learning algorithm. A corpus of 573 radiology reports was de-identified and manually annotated with the support of NLP tools by a physician for relevant concepts, modalities and relations. A machine learning classifier was trained on the dataset interpreted by a physician for diagnosis of deep-vein thrombosis, pulmonary embolism and clinically relevant incidental findings. Decision models accounted for the imbalanced nature of the data and exploited the structure of the reports.
resultsThe best model achieved an F measure of 0.98 for pulmonary embolism identification, 1.00 for deep vein thrombosis, and 0.80 for incidental clinically relevant findings. The use of concepts, modalities and relations improved performances in all cases.
conclusionsThis study demonstrates the benefits of developing an automated method to identify medical concepts, modality and relations from radiology reports in French. An end-to-end automatic system for annotation and classification which could be applied to other radiology reports databases would be valuable for epidemiological surveillance, performance monitoring, and accreditation in French hospitals.
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