ArticleJournal of biomedical informatics2018
Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort.
Article in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 81 citations in OpenAlex.
- Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.Blood advances · 2024Pooled it
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Review
- Development and validation of venous thromboembolism-bidirectional encoder representations from transformers (VTE-BERT) natural language processing model.Journal of thrombosis and haemostasis : JTH · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Radiology Report Annotation Using Generative Large Language Models: Comparative Analysis.International journal of biomedical imaging · 2025Article
- ESR paper on structured reporting in radiology-update 2023.Insights into imaging · 2023Article
- Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification?The British journal of radiology · 2023Article
- Natural Language Processing for Breast Imaging: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Predicting decompression surgery by applying multimodal deep learning to patients' structured and unstructured health data.BMC medical informatics and decision making · 2023Article
- A Comparison of Natural Language Processing Methods for the Classification of Lumbar Spine Imaging Findings Related to Lower Back Pain.Academic radiology · 2022Article
- Predicting pulmonary embolism among hospitalized patients with machine learning algorithms.Pulmonary circulation · 2022Article
- Neural Network Assisted Pathology Case Identification.Journal of pathology informatics · 2022Article
- Generalized Zero-Shot Chest X-Ray Diagnosis Through Trait-Guided Multi-View Semantic Embedding With Self-Training.IEEE transactions on medical imaging · 2021Article
- Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings.Scientific reports · 2021Article
- Domain specific word embeddings for natural language processing in radiology.Journal of biomedical informatics · 2021Article
- Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection.Scientific reports · 2020Article
- Deep learning in clinical natural language processing: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- Self-Supervised Contextual Language Representation of Radiology Reports to Improve the Identification of Communication Urgency.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Applying Artificial Intelligence Methods for the Estimation of Disease Incidence: The Utility of Language Models.Frontiers in digital health · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
We proposed an unsupervised hybrid method - Intelligent Word Embedding (IWE) that combines neural embedding method with a semantic dictionary mapping technique for creating a dense vector representation of unstructured radiology reports. We applied IWE to generate embedding of chest CT radiology reports from two healthcare organizations and utilized the vector representations to semi-automate report categorization based on clinically relevant categorization related to the diagnosis of pulmonary embolism (PE). We benchmark the performance against a state-of-the-art rule-based tool, PeFinder and out-of-the-box word2vec. On the Stanford test set, the IWE model achieved average F1 score 0.97, whereas the PeFinder scored 0.9 and the original word2vec scored 0.94. On UPMC dataset, the IWE model's average F1 score was 0.94, when the PeFinder scored 0.92 and word2vec scored 0.85. The IWE model had lowest generalization error with highest F1 scores. Of particular interest, the IWE model (trained on the Stanford dataset) outperformed PeFinder on the UPMC dataset which was used originally to tailor the PeFinder model.
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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.