Practical implementation of an existing smoking detection pipeline and reduced support vector machine training corpus requirements

Richard Khor, Wai-Kuan Yip, Matias Bressel, William Rose, Gillian Mary Duchesne, Farshad Foroudi

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)


This study aimed to reduce reliance on large training datasets in support vector machine (SVM)-based clinical text analysis by categorizing keyword features. An enhanced Mayo smoking status detection pipeline was deployed. We used a corpus of 709 annotated patient narratives. The pipeline was optimized for local data entry practice and lexicon. SVM classifier retraining used a grouped keyword approach for better efficiency. Accuracy, precision, and F-measure of the unaltered and optimized pipelines were evaluated using k-fold cross-validation. Initial accuracy of the clinical Text Analysis and Knowledge Extraction System (cTAKES) package was 0.69. Localization and keyword grouping improved system accuracy to 0.9 and 0.92, respectively. F-measures for current and past smoker classes improved from 0.43 to 0.81 and 0.71 to 0.91, respectively. Non-smoker and unknown-class F-measures were 0.96 and 0.98, respectively. Keyword grouping had no negative effect on performance, and decreased training time. Grouping keywords is a practical method to reduce training corpus size.
Original languageEnglish
Pages (from-to)27 - 30
Number of pages4
JournalJournal of the American Medical Informatics Association : JAMIA
Issue number1
Publication statusPublished - 2014

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