Methods Inf Med 2018; 57(05/06): 253-260
DOI: 10.1055/s-0039-1681088
Original Article
Georg Thieme Verlag KG Stuttgart · New York

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

J. Patel
1  Dental Informatics Core Division, Department of Cariology, Operative Dentistry, and Dental Public Health, Indiana University School of Dentistry, Indiana University – Purdue University Indianapolis, Indianapolis, Indiana, United States
2  Department of Bio-Health Informatics, School of Informatics and Computing, Indiana University – Purdue University Indianapolis, Indianapolis, Indiana, United States
,
Z. Siddiqui
1  Dental Informatics Core Division, Department of Cariology, Operative Dentistry, and Dental Public Health, Indiana University School of Dentistry, Indiana University – Purdue University Indianapolis, Indianapolis, Indiana, United States
,
A. Krishnan
1  Dental Informatics Core Division, Department of Cariology, Operative Dentistry, and Dental Public Health, Indiana University School of Dentistry, Indiana University – Purdue University Indianapolis, Indianapolis, Indiana, United States
,
T. P. Thyvalikakath
1  Dental Informatics Core Division, Department of Cariology, Operative Dentistry, and Dental Public Health, Indiana University School of Dentistry, Indiana University – Purdue University Indianapolis, Indianapolis, Indiana, United States
2  Department of Bio-Health Informatics, School of Informatics and Computing, Indiana University – Purdue University Indianapolis, Indianapolis, Indiana, United States
3  Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States
› Institutsangaben
Funding This study was funded by Dr. Thankam Paul Thyvalikakath’s start-up funds from Indiana University School of Dentistry.
Weitere Informationen

Publikationsverlauf

08. Mai 2018

11. Oktober 2018

Publikationsdatum:
15. März 2019 (online)

Abstract

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.

Human Subjects Protections

This study was reviewed and approved by our institutional review board (IRB: 1310579094) and granted authorization.