ArticleJournal of digital imaging2019
Automatic Normalization of Anatomical Phrases in Radiology Reports Using Unsupervised Learning.
Article in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Standardizing DICOM annotation: deep learning enhances body part description in X-ray image retrieval for clinical research.BMC medical imaging · 2025Article
- Use of Natural Language Processing to Infer Sites of Metastatic Disease From Radiology Reports at Scale.JCO clinical cancer informatics · 2024Article
- ESR paper on structured reporting in radiology-update 2023.Insights into imaging · 2023Article
- Artificial intelligence in lung cancer: Application and future thinking.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2022Article
- Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review.Journal of medical Internet research · 2021Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- RadLex Normalization in Radiology Reports.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Context-Driven Concept Annotation in Radiology Reports: Anatomical Phrase Labeling.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Classification of Pulmonary Nodular Findings based on Characterization of Change using Radiology Reports.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Unsupervised SapBERT-based bi-encoders for medical concept annotation of clinical narratives with SNOMED CT.Digital healthArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In today's radiology workflow, free-text reporting is established as the most common medium to capture, store, and communicate clinical information. Radiologists routinely refer to prior radiology reports of a patient to recall critical information for new diagnosis, which is quite tedious, time consuming, and prone to human error. Automatic structuring of report content is desired to facilitate such inquiry of information. In this work, we propose an unsupervised machine learning approach to automatically structure radiology reports by detecting and normalizing anatomical phrases based on the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) ontology. The proposed approach combines word embedding-based semantic learning with ontology-based concept mapping to derive the desired concept normalization. The word embedding model was trained using a large corpus of unlabeled radiology reports. Fifty-six anatomical labels were extracted from SNOMED CT as class labels of the whole human anatomy. The proposed framework was compared against a number of state-of-the-art supervised and unsupervised approaches. Radiology reports from three different clinical sites were manually labeled for testing. The proposed approach outperformed other techniques yielding an average precision of 82.6%. The proposed framework boosts the coverage and performance of conventional approaches for concept normalization, by applying word embedding techniques in semantic learning, while avoiding the challenge of having access to a large amount of annotated data, which is typically required for training classifiers.
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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.