ArticleJAMIA open2022
Development and evaluation of an interoperable natural language processing system for identifying pneumonia across clinical settings of care and institutions.
Article in JAMIA open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- Using a large language model artificial intelligence agent to improve the efficiency of clinical quality measure evidence evaluation: a case study.BMJ health & care informatics · 2026Article
- Characterization and validation of electronic medical record data for pharmacoepidemiologic research.Frontiers in pharmacology · 2026Article
- Artificial Intelligence Applications in Pneumonia: Diagnosis and Outcome Prediction.Current pulmonology reports · 2026Review
- Utilizing large language models and natural language processing to classify ischemia status from cardiac stress tests in a large multicenter healthcare system.BMC research notes · 2025Article
- Pneumonia diagnosis performance in the emergency department: a mixed-methods study about clinicians' experiences and exploration of individual differences and response to diagnostic performance feedback.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- An Algorithm to Assess Guideline Concordance of Antibiotic Choice in Community-Acquired Pneumonia.Hospital pediatrics · 2024Article
- Incidence and Outcomes of Non-Ventilator-Associated Hospital-Acquired Pneumonia in 284 US Hospitals Using Electronic Surveillance Criteria.JAMA network open · 2023Article
- Using natural language processing to study homelessness longitudinally with electronic health record data subject to irregular observations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Assessing longitudinal housing status using Electronic Health Record data: a comparison of natural language processing, structured data, and patient-reported history.Frontiers in artificial intelligence · 2023Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To evaluate the feasibility, accuracy, and interoperability of a natural language processing (NLP) system that extracts diagnostic assertions of pneumonia in different clinical notes and institutions. Materials and Methods: A rule-based NLP system was designed to identify assertions of pneumonia in 3 types of clinical notes from electronic health records (EHRs): emergency department notes, radiology reports, and discharge summaries. The lexicon and classification logic were tailored for each note type. The system was first developed and evaluated using annotated notes from the Department of Veterans Affairs (VA). Interoperability was assessed using data from the University of Utah (UU). Results: The NLP system was comprised of 782 rules and achieved moderate-to-high performance in all 3 note types in VA (precision/recall/f1: emergency = 88.1/86.0/87.1; radiology = 71.4/96.2/82.0; discharge = 88.3/93.0/90.1). When applied to UU data, performance was maintained in emergency and radiology but decreased in discharge summaries (emergency = 84.7/94.3/89.3; radiology = 79.7/100.0/87.9; discharge = 65.5/92.7/76.8). Customization with 34 additional rules increased performance for all note types (emergency = 89.3/94.3/91.7; radiology = 87.0/100.0/93.1; discharge = 75.0/95.1/83.4). Conclusion: NLP can be used to accurately identify the diagnosis of pneumonia across different clinical settings and institutions. A limited amount of customization to account for differences in lexicon, clinical definition of pneumonia, and EHR structure can achieve high accuracy without substantial modification.
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