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. a A description of related work is included in the full paper published as the online data supplement at www.jamia.org. b Details of Smoking Challenge results are included in the full paper published as the online supplement at www.jamia.org. 36 CLARK et al., Identifying Smokers with Medical Extraction by guest on January 21, 2016 http://jamia.oxfordjournals.org/ Downloaded from F i g u r e 1. Effect of data set size on classification accuracydirect approach F i g u r e 2. Effect of data set size on classification accuracymediated approach
In REF they developed a medical information extraction system that combines a rule-based extraction engine with machine learning algorithms to identify and categorize references in clinical reports to patients who smoke.
Identifying Smokers with a Medical Extraction System
Journal of the American Medical Informatics Association : JAMIA
Journal of the American Medical Informatics Association : JAMIA
Computer Science Medicine