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Mayo clinic smoking status classification system: extensions and improvements
Sunghwan Sohn1, Guergana K Savova
1Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|March 31, 2010
Summary
This study enhances the Mayo Clinic smoking status classification system for better accuracy in identifying current, past, and unknown smoking habits. The improved system aids in research, like identifying smoking risk factors for peripheral artery disease.
Area of Science:
- Natural Language Processing
- Clinical Informatics
- Public Health
Background:
- The Mayo Clinic 2006 smoking status classification system had limitations in accurately classifying patient smoking habits.
- Accurate smoking status is crucial for clinical research and patient risk stratification.
Purpose of the Study:
- To describe improvements and extensions to the Mayo Clinic 2006 smoking status classification system.
- To address limitations in negation detection, temporal resolution, and unknown status classification.
- To introduce a rule-based component for patient-level smoking status aggregation.
Main Methods:
- Remodeled negation detection for non-smokers.
- Enhanced temporal resolution to differentiate past and current smokers.
- Developed a rule-based system for aggregating individual document smoking statuses to determine patient-level status.
- Leveraged existing components from Mayo's clinical Text Analysis and Knowledge Extraction System within IBM's UIMA framework.
Main Results:
- The enhanced system demonstrates improved performance in classifying smoking status.
- The new system accurately distinguishes between past and current smokers.
- The rule-based aggregation effectively determines patient-level smoking status from multiple documents.
Conclusions:
- The improved smoking status classification system offers greater accuracy and utility.
- The system's enhancements address key limitations of the previous version.
- The extended system is successfully applied in identifying smoking risk factors for peripheral artery disease research.
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