ArticleBMC medical informatics and decision making2019
Natural language processing of radiology reports for identification of skeletal site-specific fractures.
Article in BMC medical informatics and decision making, 2019. 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, 42 citations in OpenAlex.
- Artificial intelligence in virtual fracture clinics: a systematic review of imaging and clinical-text tools.Journal of orthopaedic surgery and research · 2026Pooled it
- High-Performance Prompting for LLM Extraction of Compression Fracture Findings from Radiology Reports.Journal of imaging informatics in medicine · 2026Article
- Advances in Artificial Intelligence for Wrist Joint Injury Diagnosis.International journal of medical sciences · 2026Review
- Technical requirements and optimization strategies for home-based teleradiology workstations: a review article.Insights into imaging · 2025Review
- Diagnostic report generation for macular diseases by natural language processing algorithms.The British journal of ophthalmology · 2025Article
- Paraspinal Muscle Fat Infiltration as a Key Predictor of Symptomatic Intravertebral Vacuum Cleft: A Machine Learning Approach.Journal of clinical medicine · 2025Article
- Text mining approach for feature extraction and cartilage disease grade classification using knee MRI radiology reports.Computational and structural biotechnology journal · 2024Article
- Automatic text classification of prostate cancer malignancy scores in radiology reports using NLP models.Medical & biological engineering & computing · 2024Article
- Review
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Approach to the Patient With Bone Fracture: Making the First Fracture the Last.The Journal of clinical endocrinology and metabolism · 2023Article
- Performance of ChatGPT, human radiologists, and context-aware ChatGPT in identifying AO codes from radiology reports.Scientific reports · 2023Article
- Automatic Diagnosis of Spinal Disorders on Radiographic Images: Leveraging Existing Unstructured Datasets With Natural Language Processing.Global spine journal · 2023Article
- Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study.JMIR medical informatics · 2023Article
- Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model.Journal of digital imaging · 2023Article
- Natural Language Processing for Information Extraction of Gastric Diseases and Its Application in Large-Scale Clinical Research.Journal of clinical medicine · 2022Article
- Natural language processing of radiology reports to investigate the effects of the COVID-19 pandemic on the incidence and age distribution of fractures.Skeletal radiology · 2022Article
- Electronic search programs are effective in identifying patients with minimal trauma fractures.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2022Article
- Natural language processing of radiology reports for the identification of patients with fracture.Archives of osteoporosis · 2021Article
- Special issue of BMC medical informatics and decision making on health natural language processing.BMC medical informatics and decision making · 2019Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundOsteoporosis has become an important public health issue. Most of the population, particularly elderly people, are at some degree of risk of osteoporosis-related fractures. Accurate identification and surveillance of patient populations with fractures has a significant impact on reduction of cost of care by preventing future fractures and its corresponding complications.
methodsIn this study, we developed a rule-based natural language processing (NLP) algorithm for identification of twenty skeletal site-specific fractures from radiology reports. The rule-based NLP algorithm was based on regular expressions developed using MedTagger, an NLP tool of the Apache Unstructured Information Management Architecture (UIMA) pipeline to facilitate information extraction from clinical narratives. Radiology notes were retrieved from the Mayo Clinic electronic health records data warehouse. We developed rules for identifying each fracture type according to physicians' knowledge and experience, and refined these rules via verification with physicians. This study was approved by the institutional review board (IRB) for human subject research.
resultsWe validated the NLP algorithm using the radiology reports of a community-based cohort at Mayo Clinic with the gold standard constructed by medical experts. The micro-averaged results of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score of the proposed NLP algorithm are 0.930, 1.0, 1.0, 0.941, 0.961, respectively. The F1-score is 1.0 for 8 fractures, and above 0.9 for a total of 17 out of 20 fractures (85%).
conclusionsThe results verified the effectiveness of the proposed rule-based NLP algorithm in automatic identification of osteoporosis-related skeletal site-specific fractures from radiology reports. The NLP algorithm could be utilized to accurately identify the patients with fractures and those who are also at high risk of future fractures due to osteoporosis. Appropriate care interventions to those patients, not only the most at-risk patients but also those with emerging risk, would significantly reduce future fractures.
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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.