Use of semantic features to classify patient smoking status.
The recent i2b2 NLP Challenge smoking classification task offers a rare chance to compare different natural language processing techniques on actual clinical data. We compare the performance of a classifier which relies on semantic features generated by an unmodified version of MedLEE, a clinical NLP engine, to one using lexical features. We also compare the performance of supervised classifiers to rule-based symbolic classifiers. Our baseline supervised classifier with lexical features yields a microaveraged F-measure of 0.81. Our rule-based classifier using MedLEE semantic features is superior, with an F-measure of 0.83. Our supervised classifier trained with semantic MedLEE features is competitive with the top-performing smoking classifier in the i2b2 NLP Challenge, with microaveraged precision of 0.90, recall of 0.89, and F-measure of 0.89.
McCormick
Patrick J
PJ
College of Physicians & Surgeons, Columbia University, New York, NY, USA.
Elhadad
Noémie
N
Stetson
Peter D
PD
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008487:Medical History Taking / Q000706:statistics & numerical data*; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010351:Patient Discharge / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012660:Semantics; D012907:Smoking / Q000453:epidemiology*; D009626:Terminology as Topic; D014481:United States