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. 2016 May;23(5):628-36.
doi: 10.1111/acem.12925. Epub 2016 Apr 13.

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

Son Doan  1 , Cleo K Maehara  1 , Juan D Chaparro  1 , Sisi Lu  2 , Ruiling Liu  3 , Amanda Graham  4 , Erika Berry  5 , Chun-Nan Hsu  1 , John T Kanegaye  5   6 , David D Lloyd  4   7 , Lucila Ohno-Machado  1 , Jane C Burns  5   6 , Adriana H Tremoulet  5   6 ; Pediatric Emergency Medicine Kawasaki Disease Research Group
Affiliations

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

Son Doan et al. Acad Emerg Med. 2016 May.

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.

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.

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Conflict of interest statement

The authors have no potential confiicts to disclose. Supervising Editor: Damon Kuehl, MD.

Figures

Figure 1
Figure 1
Algorithm followed by the KD-NLP tool. Preprocessing: This module includes converting text from MS Word format into plain-text files and a sentence splitter that breaks text into individual sentences. KD tagger: The KD tagger recognizes fever and KD signs from clinical text. KD classifier: This classifies a subject as high suspicion for KD if the number of KD signs detected by the KD tagger is at least three in addition to fever; otherwise, it assigns the subject as a low suspicion for KD. KD-NLP = Kawasaki disease natural language processing tool; MS = Microsoft; UMLS = Unified Medical Language System.
See this image and copyright information in PMC

References

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