ArticleAcademic emergency medicine : official journal of the Society for Academic Emergency Medicine2016
Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes.
Article in Academic emergency medicine : official journal of the Society for Academic Emergency Medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
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- Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study.JMIR medical informatics · 2022Article
- Development and Validation of a Natural Language Processing Tool to Identify Injuries in Infants Associated With Abuse.Academic pediatrics · 2022Article
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- The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis.JMIR medical informatics · 2021Review
- Privacy-preserving model learning on a blockchain network-of-networks.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Identifying Emergency Department Symptom-Based Diagnoses with the Unified Medical Language System.The western journal of emergency medicine · 2019Article
- Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview.The western journal of emergency medicine · 2019Review
- Identification of Inpatient Allergic Drug Reactions Using ICD-9-CM Codes.The journal of allergy and clinical immunology. In practice · 2019Article
- Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke.PloS one · 2019Article
- Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.PloS one · 2019Article
- "Minimally invasive research?" Use of the electronic health record to facilitate research in pediatric urology.Journal of pediatric urology · 2018Review
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- Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression.Evidence-based mental health · 2017Article
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Authors and funding
14 authors at 6 institutions in 1 country.
Funding
Abstract
objectiveDelayed diagnosis of Kawasaki disease (KD) may lead to serious cardiac complications. We sought to create and test the performance of a natural language processing (NLP) tool, the KD-NLP, in the identification of emergency department (ED) patients for whom the diagnosis of KD should be considered.
methodsWe developed an NLP tool that recognizes the KD diagnostic criteria based on standard clinical terms and medical word usage using 22 pediatric ED notes augmented by Unified Medical Language System vocabulary. With high suspicion for KD defined as fever and three or more KD clinical signs, KD-NLP was applied to 253 ED notes from children ultimately diagnosed with either KD or another febrile illness. We evaluated KD-NLP performance against ED notes manually reviewed by clinicians and compared the results to a simple keyword search.
resultsKD-NLP identified high-suspicion patients with a sensitivity of 93.6% and specificity of 77.5% compared to notes manually reviewed by clinicians. The tool outperformed a simple keyword search (sensitivity = 41.0%; specificity = 76.3%).
conclusionsKD-NLP showed comparable performance to clinician manual chart review for identification of pediatric ED patients with a high suspicion for KD. This tool could be incorporated into the ED electronic health record system to alert providers to consider the diagnosis of KD. KD-NLP could serve as a model for decision support for other conditions in the ED.
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