Journal Article

Using Implicit Information to Identify Smoking Status in Smoke-blind Medical Discharge Summaries

Richard Wicentowski and Matthew R. Sydes

in Journal of the American Medical Informatics Association

Published on behalf of American Medical Informatics Association

Volume 15, issue 1, pages 29-31
Published in print January 2008 | ISSN: 1067-5027
Published online January 2008 | e-ISSN: 1527-974X | DOI: https://dx.doi.org/10.1197/jamia.M2440
Using Implicit Information to Identify Smoking Status in Smoke-blind Medical Discharge Summaries

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  • Medical Statistics and Methodology
  • Bioinformatics and Computational Biology
  • Biomathematics and Statistics

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Abstract

As part of the 2006 i2b2 NLP Shared Task, we explored two methods for determining the smoking status of patients from their hospital discharge summaries when explicit smoking terms were present and when those same terms were removed. We developed a simple keyword-based classifier to determine smoking status from de-identified hospital discharge summaries. We then developed a Naïve Bayes classifier to determine smoking status from the same records after all smoking-related words had been manually removed (the smoke-blind dataset). The performance of the Naïve Bayes classifier was compared with the performance of three human annotators on a subset of the same training dataset (n = 54) and against the evaluation dataset (n = 104 records). The rule-based classifier was able to accurately extract smoking status from hospital discharge summaries when they contained explicit smoking words. On the smoke-blind dataset, where explicit smoking cues are not available, two Naïve Bayes systems performed less well than the rule-based classifier, but similarly to three expert human annotators.

Journal Article.  2219 words. 

Subjects: Medical Statistics and Methodology ; Bioinformatics and Computational Biology ; Biomathematics and Statistics

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