ArticleApplied clinical informatics2016
Identification of Long Bone Fractures in Radiology Reports Using Natural Language Processing to support Healthcare Quality Improvement.
Article in Applied clinical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Accuracy and Disparities in Pediatric Emergency Triage: A Multicenter Retrospective Cohort Study.Hospital pediatrics · 2026Article
- Text mining approach for feature extraction and cartilage disease grade classification using knee MRI radiology reports.Computational and structural biotechnology journal · 2024Article
- Advanced Sampling Technique in Radiology Free-Text Data for Efficiently Building Text Mining Models by Deep Learning in Vertebral Fracture.Diagnostics (Basel, Switzerland) · 2024Article
- Approach to the Patient With Bone Fracture: Making the First Fracture the Last.The Journal of clinical endocrinology and metabolism · 2023Article
- Automated Classification of Free-Text Radiology Reports: Using Different Feature Extraction Methods to Identify Fractures of the Distal Fibula.RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin · 2023Article
- Machine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospital.BMC medical informatics and decision making · 2023Article
- Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame.JMIR medical informatics · 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 to investigate the effects of the COVID-19 pandemic on the incidence and age distribution of fractures.Skeletal radiology · 2022Article
- Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants.Applied clinical informatics · 2021Article
- Natural language processing of radiology reports for the identification of patients with fracture.Archives of osteoporosis · 2021Article
- Opioid Prescription Patterns at Emergency Department Discharge for Children with Fractures.Pain medicine (Malden, Mass.) · 2020Article
- A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning.Applied clinical informatics · 2020Article
- Racial and Ethnic Differences in Emergency Department Pain Management of Children With Fractures.Pediatrics · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Interactive NLP in Clinical Care: Identifying Incidental Findings in Radiology Reports.Applied clinical informatics · 2019Article
- Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.BMC medical informatics and decision making · 2019Article
- Natural language processing of radiology reports for identification of skeletal site-specific fractures.BMC medical informatics and decision making · 2019Article
- Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke.PloS one · 2019Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundImportant information to support healthcare quality improvement is often recorded in free text documents such as radiology reports. Natural language processing (NLP) methods may help extract this information, but these methods have rarely been applied outside the research laboratories where they were developed.
objectiveTo implement and validate NLP tools to identify long bone fractures for pediatric emergency medicine quality improvement.
methodsUsing freely available statistical software packages, we implemented NLP methods to identify long bone fractures from radiology reports. A sample of 1,000 radiology reports was used to construct three candidate classification models. A test set of 500 reports was used to validate the model performance. Blinded manual review of radiology reports by two independent physicians provided the reference standard. Each radiology report was segmented and word stem and bigram features were constructed. Common English "stop words" and rare features were excluded. We used 10-fold cross-validation to select optimal configuration parameters for each model. Accuracy, recall, precision and the F1 score were calculated. The final model was compared to the use of diagnosis codes for the identification of patients with long bone fractures.
resultsThere were 329 unique word stems and 344 bigrams in the training documents. A support vector machine classifier with Gaussian kernel performed best on the test set with accuracy=0.958, recall=0.969, precision=0.940, and F1 score=0.954. Optimal parameters for this model were cost=4 and gamma=0.005. The three classification models that we tested all performed better than diagnosis codes in terms of accuracy, precision, and F1 score (diagnosis code accuracy=0.932, recall=0.960, precision=0.896, and F1 score=0.927).
conclusionsNLP methods using a corpus of 1,000 training documents accurately identified acute long bone fractures from radiology reports. Strategic use of straightforward NLP methods, implemented with freely available software, offers quality improvement teams new opportunities to extract information from narrative documents.
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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.