title: Identifying smokers with a medical extraction system.
abstract: The Clinical Language Understanding group at Nuance Communications has developed a medical information extraction system that combines a rule-based extraction engine with machine learning algorithms to identify and categorize references to patient smoking in clinical reports. The extraction engine identifies smoking references; documents that contain no smoking references are classified as UNKNOWN. For the remaining documents, the extraction engine uses linguistic analysis to associate features such as status and time to smoking mentions. Machine learning is used to classify the documents based on these features. This approach shows overall accuracy in the 90s on all data sets used. Classification using engine-generated and word-based features outperforms classification using only word-based features for all data sets, although the difference gets smaller as the data set size increases. These techniques could be applied to identify other risk factors, such as drug and alcohol use, or a family history of a disease.
authors.affiliation: The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. cclark@mitre.org
authors.forename: Cheryl
authors.initials: C
authors.lastname: Clark
authors.forename: Kathleen
authors.initials: K
authors.lastname: Good
authors.forename: Lesley
authors.initials: L
authors.lastname: Jezierny
authors.forename: Melissa
authors.initials: M
authors.lastname: Macpherson
authors.forename: Brian
authors.initials: B
authors.lastname: Wilson
authors.forename: Urszula
authors.initials: U
authors.lastname: Chajewska
mesh_terms: D001185:Artificial Intelligence; D002965:Classification / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D012907:Smoking