Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes.

Journal: Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
PMID: 26826020

Abstract

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.

Authors

  • Son Doan
    University of California San Diego, La Jolla, CA.
  • Cleo K Maehara
    Department of Biomedical Informatics, University of California, San Diego, CA.
  • Juan D Chaparro
    University of California San Diego, La Jolla, CA.
  • Sisi Lu
    Department of Computer Science, University of Pittsburgh, Pittsburgh, PA.
  • Ruiling Liu
    School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China.
  • Amanda Graham
    Children's Healthcare of Atlanta, Atlanta, GA.
  • Erika Berry
    Department of Pediatrics, University of California at San Diego, La Jolla, CA.
  • Chun-Nan Hsu
    University of California San Diego, La Jolla, CA.
  • John T Kanegaye
    Department of Pediatrics, University of California at San Diego, La Jolla, CA.
  • David D Lloyd
    Children's Healthcare of Atlanta, Atlanta, GA.
  • Lucila Ohno-Machado
    University of California San Diego, La Jolla, CA.
  • Jane C Burns
    Department of Pediatrics, University of California at San Diego, La Jolla, CA.
  • Adriana H Tremoulet
    Department of Clinical Pharmacy (GJK, GR, AHT), Rady Children's Hospital San Diego, California, Department of Pediatrics (EC, AHT), University of California, San Diego, Clinical and Translational Research Institute (JAP), University of California, San Diego.

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