ArticleJournal of digital imaging2012
Automatically correlating clinical findings and body locations in radiology reports using MedLEE.
Article in Journal of digital imaging, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- 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
- Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model.Journal of digital imaging · 2023Article
- A scoping review of natural language processing of radiology reports in breast cancer.Frontiers in oncology · 2023Article
- Enhanced neurologic concept recognition using a named entity recognition model based on transformers.Frontiers in digital health · 2022Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Understanding spatial language in radiology: Representation framework, annotation, and spatial relation extraction from chest X-ray reports using deep learning.Journal of biomedical informatics · 2020Article
- Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions.Yearbook of medical informatics · 2020Article
- Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports.LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation · 2020Article
- The implementation of natural language processing to extract index lesions from breast magnetic resonance imaging reports.BMC medical informatics and decision making · 2019Article
- Automatic extraction of imaging observation and assessment categories from breast magnetic resonance imaging reports with natural language processing.Chinese medical journal · 2019Article
- Using automatically extracted information from mammography reports for decision-support.Journal of biomedical informatics · 2016Article
- Automatic abstraction of imaging observations with their characteristics from mammography reports.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Natural Language Processing Techniques for Extracting and Categorizing Finding Measurements in Narrative Radiology Reports.Applied clinical informatics · 2015Article
- Complex epilepsy phenotype extraction from narrative clinical discharge summaries.Journal of biomedical informatics · 2014Article
- Cross-sectional relatedness between sentences in breast radiology reports: development of an SVM classifier and evaluation against annotations of five breast radiologists.Journal of digital imaging · 2013Article
- Automated extraction of BI-RADS final assessment categories from radiology reports with natural language processing.Journal of digital imaging · 2013Article
- Automatically pairing measured findings across narrative abdomen CT reports.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2013Article
- Bridging the text-image gap: a decision support tool for real-time PACS browsing.Journal of digital imaging · 2012Article
- Automatic classification of mammography reports by BI-RADS breast tissue composition class.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we describe and evaluate a system that extracts clinical findings and body locations from radiology reports and correlates them. The system uses Medical Language Extraction and Encoding System (MedLEE) to map the reports' free text to structured semantic representations of their content. A lightweight reasoning engine extracts the clinical findings and body locations from MedLEE's semantic representation and correlates them. Our study is illustrative for research in which existing natural language processing software is embedded in a larger system. We manually created a standard reference based on a corpus of neuro and breast radiology reports. The standard reference was used to evaluate the precision and recall of the proposed system and its modules. Our results indicate that the precision of our system is considerably better than its recall (82.32-91.37% vs. 35.67-45.91%). We conducted an error analysis and discuss here the practical usability of the system given its recall and precision performance.
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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.