Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2009 Nov 14:2009:619-23.

Mayo clinic smoking status classification system: extensions and improvements

Affiliations

Mayo clinic smoking status classification system: extensions and improvements

Sunghwan Sohn et al. AMIA Annu Symp Proc. .

Abstract

This paper describes improvements of and extensions to the Mayo Clinic 2006 smoking status classification system. The new system aims at addressing some of the limitations of the previous one. The performance improvements were mainly achieved through remodeling the negation detection for non-smoker, temporal resolution to distinguish a past and current smoker, and improved detection of the smoking status category of unknown. In addition, we introduced a rule-based component for patient-level smoking status assignments in which the individual smoking statuses of all clinical documents for a given patient are aggregated and analyzed to produce the final patient smoking status. The enhanced system builds upon components from Mayo's clinical Text Analysis and Knowledge Extraction System developed within IBM's Unstructured Information Management Architecture framework. This reusability minimized the development effort. The extended system is in use to identify smoking status risk factors for a peripheral artery disease NHGRI study.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
High level architecture of sentence-level classification.

References

    1. Uzuner O, Szolovits PS, Kohane I. i2b2 workshop on natural language processing challenges for clinical records. Proceedings of the Fall Symposium of the American Medical Informatics Association; 2006.
    1. Uzuner O, Goldstein I, Luo Y, Kohane I. Identifying patient smoking status from medical discharge records. J Am Med Inform Assoc. 2008;15:14–24. - PMC - PubMed
    1. Aramaki E, Imai T, Miyo K, Ohe K. Patient status classification by using rule based sentence extraction and BM25 kNN-based classifier. i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data; 2006.
    1. Clark C, Good K, Jezierny L, Macpherson M, Wilson B, Chajewska U. Identifying smokers with a medical extraction system. J Am Med Inform Assoc. 2008;15:36–9. - PMC - PubMed
    1. Cohen A. Five-way smoking status classification using text hot-spot identification and error-correcting output codes. J Am Med Inform Assoc. 2008;15:32–5. - PMC - PubMed

Publication types

LinkOut - more resources