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Leveraging Electronic Dental Record Data to Classify Patients Based on Their Smoking Intensity.

Methods Inf Med. 2018 Nov;57(5-06):253-260

Authors: Patel J, Siddiqui Z, Krishnan A, Thyvalikakath TP

BACKGROUND:  Smoking is an established risk factor for oral diseases and, therefore, dental clinicians routinely assess and record their patients’ detailed smoking status. Researchers have successfully extracted smoking history from electronic health records (EHRs) using text mining methods. However, they could not retrieve patients’ smoking intensity due to its limited availability in the EHR. The presence of detailed smoking information in the electronic dental record (EDR) often under a separate section allows retrieving this information with less preprocessing.
OBJECTIVE:  To determine patients’ detailed smoking status based on smoking intensity from the EDR.
METHODS:  First, the authors created a reference standard of 3,296 unique patients’ smoking histories from the EDR that classified patients based on their smoking intensity. Next, they trained three machine learning classifiers (support vector machine, random forest, and naïve Bayes) using the training set (2,176) and evaluated performances on test set (1,120) using precision (P), recall (R), and F-measure (F). Finally, they applied the best classifier to classify smoking status from an additional 3,114 patients’ smoking histories.
RESULTS:  Support vector machine performed best to classify patients into smokers, nonsmokers, and unknowns (P, R, F: 98%); intermittent smoker (P: 95%, R: 98%, F: 96%); past smoker (P, R, F: 89%); light smoker (P, R, F: 87%); smokers with unknown intensity (P: 76%, R: 86%, F: 81%), and intermediate smoker (P: 90%, R: 88%, F: 89%). It performed moderately to differentiate heavy smokers (P: 90%, R: 44%, F: 60%). EDR could be a valuable source for obtaining patients’ detailed smoking information.
CONCLUSION:  EDR data could serve as a valuable source for obtaining patients’ detailed smoking information based on their smoking intensity that may not be readily available in the EHR.

PMID: 30875704 [PubMed – in process]

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