ArticleApplied clinical informatics2015
Natural Language Processing Techniques for Extracting and Categorizing Finding Measurements in Narrative Radiology Reports.
Article in Applied clinical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 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
- Review
- Artificial Intelligence to Improve Patient Understanding of Radiology Reports.The Yale journal of biology and medicine · 2023Review
- Extracting Pain Care Quality Indicators from U.S. Veterans Health Administration Chiropractic Care Using Natural Language Processing.Applied clinical informatics · 2023Article
- Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame.JMIR medical informatics · 2022Article
- Automated Identification and Measurement Extraction of Pancreatic Cystic Lesions from Free-Text Radiology Reports Using Natural Language Processing.Radiology. Artificial intelligence · 2022Article
- A Data-Driven Iterative Approach for Semi-automatically Assessing the Correctness of Medication Value Sets: A Proof of Concept Based on Opioids.Methods of information in medicine · 2021Article
- Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants.Applied clinical informatics · 2021Article
- Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study.Journal of clinical medicine · 2021Article
- A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning.Applied clinical informatics · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- tbiExtractor: A framework for extracting traumatic brain injury common data elements from radiology reports.PloS one · 2020Article
- Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm.Journal of digital imaging · 2019Article
- Automated Generation of Radiologic Descriptions on Brain Volume Changes From T1-Weighted MR Images: Initial Assessment of Feasibility.Frontiers in neurology · 2019Article
- Autonomous detection, grading, and reporting of postoperative complications using natural language processing.Surgery · 2018Article
- Review
- Clinical information extraction applications: A literature review.Journal of biomedical informatics · 2018Review
- Automatic Lung-RADS™ classification with a natural language processing system.Journal of thoracic disease · 2017Article
- ENABLE (Exportable Notation and Bookmark List Engine): an Interface to Manage Tumor Measurement Data from PACS to Cancer Databases.Journal of digital imaging · 2017Article
- Identification of Long Bone Fractures in Radiology Reports Using Natural Language Processing to support Healthcare Quality Improvement.Applied clinical informatics · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccumulating quantitative outcome parameters may contribute to constructing a healthcare organization in which outcomes of clinical procedures are reproducible and predictable. In imaging studies, measurements are the principal category of quantitative para meters.
objectivesThe purpose of this work is to develop and evaluate two natural language processing engines that extract finding and organ measurements from narrative radiology reports and to categorize extracted measurements by their "temporality".
methodsThe measurement extraction engine is developed as a set of regular expressions. The engine was evaluated against a manually created ground truth. Automated categorization of measurement temporality is defined as a machine learning problem. A ground truth was manually developed based on a corpus of radiology reports. A maximum entropy model was created using features that characterize the measurement itself and its narrative context. The model was evaluated in a ten-fold cross validation protocol.
resultsThe measurement extraction engine has precision 0.994 and recall 0.991. Accuracy of the measurement classification engine is 0.960.
conclusionsThe work contributes to machine understanding of radiology reports and may find application in software applications that process medical data.
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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.