The ngram chief complaint classifier: a novel method of automatically creating chief complaint classifiers based on
Philip Brown1, Sylvia Halász, Colin Goodall
1AT&T Labs-Research, Florham Park, NJ, USA.
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.
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.
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