Evaluation of preprocessing techniques for chief complaint classification.
OBJECTIVE
To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance.


METHODS
We preprocessed chief complaints using two preprocessors (CCP and EMT-P) and evaluated whether classification performance increased for a probabilistic classifier (CoCo) or for a keyword-based classifier (modification of the NYC Department of Health and Mental Hygiene chief complaint coder (KC)).


RESULTS
CCP exhibited high accuracy (85%) in preprocessing chief complaints but only slightly improved CoCo's classification performance for a few syndromes. EMT-P, which splits chief complaints into multiple problems, substantially increased CoCo's sensitivity for all syndromes. Preprocessing with CCP or EMT-P only improved KC's sensitivity for the Constitutional syndrome.


CONCLUSIONS
Evaluation of preprocessing systems should not be limited to accuracy of the preprocessor but should include the effect of preprocessing on syndromic classification. Splitting chief complaints into multiple problems before classification is important for CoCo, but other preprocessing steps only slightly improved classification performance for CoCo and a keyword-based classifier.
Dara
Jagan
J
Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Avenue, VALE M-183, Pittsburgh, PA 15260, USA.
Dowling
John N
JN
Travers
Debbie
D
Cooper
Gregory F
GF
Chapman
Wendy W
WW
D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*; D013577:Syndrome; D009626:Terminology as Topic