ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2008
NLP-based identification of pneumonia cases from free-text radiological reports.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it, 73 citations in OpenAlex.
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Comparison of Chest Radiograph Captions Based on Natural Language Processing vs Completed by Radiologists.JAMA network open · 2023Trial
- Community-acquired pneumonia identification from electronic health records in the absence of a gold standard: A Bayesian latent class analysis.PLOS digital health · 2025Article
- Uncertainty-aware automatic TNM staging classification for [BMC medical informatics and decision making · 2024Article
- Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays.Scientific reports · 2024Article
- Combining text mining with clinical decision support in clinical practice: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study.JMIR medical informatics · 2022Article
- A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language Processing.Methods of information in medicine · 2022Article
- A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.JAMIA open · 2021Article
- Large-scale identification of aortic stenosis and its severity using natural language processing on electronic health records.Cardiovascular digital health journal · 2021Article
- Neural classification of Norwegian radiology reports: using NLP to detect findings in CT-scans of children.BMC medical informatics and decision making · 2021Article
- Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review.Journal of medical Internet research · 2021Article
- A silver bullet? The role of radiology information system data mining in defining gunshot injury trends at a South African tertiary-level hospital.SA journal of radiology · 2021Article
- Identification of patients with carotid stenosis using natural language processing.European radiology · 2020Article
- Natural Language Processing and Machine Learning to Enable Clinical Decision Support for Treatment of Pediatric Pneumonia.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Identifying incidental findings from radiology reports of trauma patients: An evaluation of automated feature representation methods.International journal of medical informatics · 2019Article
- Integrating terminologies into standard SQL: a new approach for research on routine data.Journal of biomedical semantics · 2019Article
- Defining Phenotypes from Clinical Data to Drive Genomic Research.Annual review of biomedical data science · 2018Article
- Open Globe Injury Patient Identification in Warfare Clinical Notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
Abstract
Radiological reports are a rich source of clinical data which can be mined to assist with biosurveillance of emerging infectious diseases. In addition to biosurveillance, radiological reports are an important source of clinical data for health service research.Pneumonias and other radiological findings on chest x ray or chest computed tomography (CT) are one type of relevant finding to both biosurveillance and health services research. In this study we examined the ability of a Natural Language Processing system to accurately identify pneumonias and other lesions from within free text radiological reports. The system encoded the reports in the SNOMED CT Ontology and then a set of SNOMED CT based rules were created in our Health Archetype Language aimed at the identification of these radiological findings and diagnoses. The encoded rule was executed against the SNOMED CT encodings of the radiological reports. The accuracy of the reports was compared with a Clinician review of the Radiological Reports. The accuracy of the system in the identification of pneumonias was high with a Sensitivity (recall) of 100%, a specificity of 98%, and a positive predictive value (precision) of 97%. We conclude that SNOMED CT based computable rules are accurate enough for the automated biosurveillance of pneumonias from radiological reports.
Indexed as
Identifiers
18998791PMC2656026W2228821715What OpenQuestion holds
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.