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Identifying Smokers with a Medical Extraction System

By Cheryl Clark, Kathleen Good, Lesley Jezierny, Melissa Macpherson, Brian Wilson and Urszula Chajewska

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

Topics: Case Report
Publisher: American Medical Informatics Association
OAI identifier: oai:pubmedcentral.nih.gov:2274874
Provided by: PubMed Central
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