ArticlePloS one2013
Using the electronic medical record to identify community-acquired pneumonia: toward a replicable automated strategy.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- 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
- Extracting information from the text of electronic medical records to improve case detection: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2016Pooled it
- Symptom Recognition in Medical Conversations Via multi- Instance Learning and Prompt.Journal of medical systems · 2025Article
- Community-acquired pneumonia identification from electronic health records in the absence of a gold standard: A Bayesian latent class analysis.PLOS digital health · 2025Article
- Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model.Scientific reports · 2022Article
- Development of an Electronic Algorithm to Identify in Real Time Adults Hospitalized With Suspected Community-Acquired Pneumonia.Open forum infectious diseases · 2021Article
- Host genetic effects in pneumonia.American journal of human genetics · 2021Article
- Identifying Breast Cancer Distant Recurrences from Electronic Health Records Using Machine Learning.Journal of healthcare informatics research · 2019Article
- Machine learning in pain research.Pain · 2018Review
- Development and Validation of a Natural Language Processing Tool to Identify Patients Treated for Pneumonia across VA Emergency Departments.Applied clinical informatics · 2018Article
- Article
- Toward Electronic Surveillance of Invasive Mold Diseases in Hematology-Oncology Patients: An Expert System Combining Natural Language Processing of Chest Computed Tomography Reports, Microbiology, and Antifungal Drug Data.JCO clinical cancer informatics · 2017Article
- Enhancing Comparative Effectiveness Research With Automated Pediatric Pneumonia Detection in a Multi-Institutional Clinical Repository: A PHIS+ Pilot Study.Journal of medical Internet research · 2017Article
- The power of data mining in diagnosis of childhood pneumonia.Journal of the Royal Society, Interface · 2016Article
- How next-generation sequencing and multiscale data analysis will transform infectious disease management.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2015Article
- Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain.Pain medicine (Malden, Mass.) · 2015Article
- Development and application of a Chinese webpage suicide information mining system (sims).Journal of medical systems · 2014Article
- Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.PloS one · 2014Article
- Natural Language Processing for EHR-Based Computational Phenotyping.IEEE/ACM transactions on computational biology and bioinformaticsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundTimely information about disease severity can be central to the detection and management of outbreaks of acute respiratory infections (ARI), including influenza. We asked if two resources: 1) free text, and 2) structured data from an electronic medical record (EMR) could complement each other to identify patients with pneumonia, an ARI severity landmark.
methodsA manual EMR review of 2747 outpatient ARI visits with associated chest imaging identified x-ray reports that could support the diagnosis of pneumonia (kappa score = 0.88 (95% CI 0.82∶0.93)), along with attendant cases with Possible Pneumonia (adds either cough, sputum, fever/chills/night sweats, dyspnea or pleuritic chest pain) or with Pneumonia-in-Plan (adds pneumonia stated as a likely diagnosis by the provider). The x-ray reports served as a reference to develop a text classifier using machine-learning software that did not require custom coding. To identify pneumonia cases, the classifier was combined with EMR-based structured data and with text analyses aimed at ARI symptoms in clinical notes.
results370 reference cases with Possible Pneumonia and 250 with Pneumonia-in-Plan were identified. The x-ray report text classifier increased the positive predictive value of otherwise identical EMR-based case-detection algorithms by 20-70%, while retaining sensitivities of 58-75%. These performance gains were independent of the case definitions and of whether patients were admitted to the hospital or sent home. Text analyses seeking ARI symptoms in clinical notes did not add further value.
conclusionSpecialized software development is not required for automated text analyses to help identify pneumonia patients. These results begin to map an efficient, replicable strategy through which EMR data can be used to stratify ARI severity.
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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.