The ngram chief complaint classifier: A novel method of automatically creating chief complaint classifiers based on international classification of diseases groupings

Authors:
Philip Brown;Sylvia Halász;Colin Goodall;Dennis G. Cochrane;Peter Milano;John R. Allegra
Affiliations:
AT&T Labs - Research, Florham Park, NJ, USA;AT&T Labs - Research, Florham Park, NJ, USA;AT&T Labs - Research, Florham Park, NJ, USA;Emergency Medical Associates of New Jersey, Livingston, NJ, USA and Morristown Memorial Hospital Residency in Emergency Medicine, Morristown, NJ, USA;Emergency Medical Associates of New Jersey, Livingston, NJ, USA;Emergency Medical Associates of New Jersey, Livingston, NJ, USA and Morristown Memorial Hospital Residency in Emergency Medicine, Morristown, NJ, USA
Venue:
Journal of Biomedical Informatics
Year:
2010

Citing 2
Cited 2

Using nurses' natural language entries to build a concept-oriented terminology for patients' chief complaints in the emergency department

Journal of Biomedical Informatics - Special issue: Building nursing knowledge through infomatics: from concept representation to data mining
Classifying free-text triage chief complaints into syndromic categories with natural languages processing

Artificial Intelligence in Medicine

Automated syndrome classification using early phase emergency department data

Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Methodological Review: Using chief complaints for syndromic surveillance: A review of chief complaint based classifiers in North America

Journal of Biomedical Informatics

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Abstract

Introduction: The ngram classifier is created by using text fragments to measure associations between chief complaints (CC) and a syndromic grouping of ICD-9-CM codes. Objectives: For gastrointestinal (GI) syndrome to determine: (1) ngram CC classifier sensitivity/specificity. (2) Daily volumes for ngram CC and ICD-9-CM classifiers. Methods: Design: Retrospective cohort. Setting: 19 Emergency Departments. Participants: Consecutive visits (1/1/2000-12/31/2005). Protocol: (1) Used an existing ICD-9-CM filter for ''lower GI'' to create the ngram CC classifier from a training set and then measured sensitivity/specificity in a test set using an ICD-9-CM classifier as criterion. (2) Compare daily volumes based on ICD-9-CM with that predicted by the ngram classifier. Results: For a specificity of 0.96, sensitivity was 0.70. The daily volume correlation for ngram vs. ICD-9-CM was R=0.92. Conclusion: The ngram CC classifier performed similarly to manually developed CC classifiers and has advantages of rapid automated creation and updating, and may be used independent of language or dialect.