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Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes
- Doan, Son;
- Maehara, Cleo K;
- Chaparro, Juan D;
- Lu, Sisi;
- Liu, Ruiling;
- Graham, Amanda;
- Berry, Erika;
- Hsu, Chun‐Nan;
- Kanegaye, John T;
- Lloyd, David D;
- Ohno‐Machado, Lucila;
- Burns, Jane C;
- Tremoulet, Adriana H;
- Group, the Pediatric Emergency Medicine Kawasaki Disease Research
- Editor(s): Kuehl, Damon
- et al.
Published Web Location
https://doi.org/10.1111/acem.12925Abstract
Objective
Delayed 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.Methods
We 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.Results
KD-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%).Conclusions
KD-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.Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.
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