ArticleJournal of biomedical informatics2019
Automatic inference of BI-RADS final assessment categories from narrative mammography report findings.
Article in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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
9 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
- TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines.BMC medical informatics and decision making · 2024Article
- BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study.Radiology · 2024Article
- Construction of a knowledge graph for breast cancer diagnosis based on Chinese electronic medical records: development and usability study.BMC medical informatics and decision making · 2023Article
- A scoping review of natural language processing of radiology reports in breast cancer.Frontiers in oncology · 2023Article
- Expanding the Secondary Use of Prostate Cancer Real World Data: Automated Classifiers for Clinical and Pathological Stage.Frontiers in digital health · 2022Article
- Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings.Scientific reports · 2021Article
- A decision support system for mammography reports interpretation.Health information science and systems · 2020Article
- The Growing Impact of Natural Language Processing in Healthcare and Public Health.Inquiry : a journal of medical care organization, provision and financingReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
We propose an efficient natural language processing approach for inferring the BI-RADS final assessment categories by analyzing only the mammogram findings reported by the mammographer in narrative form. The proposed hybrid method integrates semantic term embedding with distributional semantics, producing a context-aware vector representation of unstructured mammography reports. A large corpus of unannotated mammography reports (300,000) was used to learn the context of the key-terms using a distributional semantics approach, and the trained model was applied to generate context-aware vector representations of the reports annotated with BI-RADS category (22,091). The vectorized reports were utilized to train a supervised classifier to derive the BI-RADS assessment class. Even though the majority of the proposed embedding pipeline is unsupervised, the classifier was able to recognize substantial semantic information for deriving the BI-RADS categorization not only on a holdout internal testset and also on an external validation set (1900 reports). Our proposed method outperforms a recently published domain-specific rule-based system and could be relevant for evaluating concordance between radiologists. With minimal requirement for task specific customization, the proposed method can be easily transferable to a different domain to support large scale text mining or derivation of patient phenotype.
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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.