ArticleJournal of biomedical informatics2014
Classification of CT pulmonary angiography reports by presence, chronicity, and location of pulmonary embolism with natural language processing.
Article in Journal of biomedical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.Blood advances · 2024Pooled 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
- Development and validation of venous thromboembolism-bidirectional encoder representations from transformers (VTE-BERT) natural language processing model.Journal of thrombosis and haemostasis : JTH · 2026Article
- Extracting Pulmonary Embolism Diagnoses From Radiology Impressions Using GPT-4o: Large Language Model Evaluation Study.JMIR medical informatics · 2025Article
- Engineering of Generative Artificial Intelligence and Natural Language Processing Models to Accurately Identify Arrhythmia Recurrence.Circulation. Arrhythmia and electrophysiology · 2025Article
- Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame.JMIR medical informatics · 2022Article
- Saddle versus non-saddle pulmonary embolism: differences in the clinical, echocardiographic, and outcome characteristics.The Libyan journal of medicine · 2022Article
- Outcomes of Surgical and Mechanical Thrombectomy in Massive Saddle Pulmonary Embolism: A National Perspective.Cureus · 2022Article
- Performance of Multiple Pretrained BERT Models to Automate and Accelerate Data Annotation for Large Datasets.Radiology. Artificial intelligence · 2022Article
- High-throughput phenotyping with temporal sequences.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Characterizing DSM-5 and ICD-11 personality disorder features in psychiatric inpatients at scale using electronic health records.Psychological medicine · 2020Article
- Identification of patients with carotid stenosis using natural language processing.European radiology · 2020Article
- High-throughput multimodal automated phenotyping (MAP) with application to PheWAS.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.Artificial intelligence in medicine · 2019Article
- Feature extraction for phenotyping from semantic and knowledge resources.Journal of biomedical informatics · 2019Article
- High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records.Biological psychiatry · 2018Article
- Enabling phenotypic big data with PheNorm.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Surrogate-assisted feature extraction for high-throughput phenotyping.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Automated Outcome Classification of Computed Tomography Imaging Reports for Pediatric Traumatic Brain Injury.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2016Article
- Natural Language Processing Technologies in Radiology Research and Clinical Applications.Radiographics : a review publication of the Radiological Society of North America, IncReview
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
In this paper we describe an efficient tool based on natural language processing for classifying the detail state of pulmonary embolism (PE) recorded in CT pulmonary angiography reports. The classification tasks include: PE present vs. absent, acute PE vs. others, central PE vs. others, and subsegmental PE vs. others. Statistical learning algorithms were trained with features extracted using the NLP tool and gold standard labels obtained via chart review from two radiologists. The areas under the receiver operating characteristic curves (AUC) for the four tasks were 0.998, 0.945, 0.987, and 0.986, respectively. We compared our classifiers with bag-of-words Naive Bayes classifiers, a standard text mining technology, which gave AUC 0.942, 0.765, 0.766, and 0.712, respectively.
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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.