Exploring the Intersection between Social Determinants of Health and Unmet Dental Care Needs Using Deep Learning

Man Hung, Eric S. Hon, Bianca Ruiz-Negron, Evelyn Lauren, Ryan Moffat, Weicong Su, Julie Xu, Jungweon Park, David Prince, Joseph Cheever and Frank W. Licari
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Man Hung: College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA
Eric S. Hon: Department of Economics, University of Chicago, Chicago, IL 60637, USA
Bianca Ruiz-Negron: Department of Orthopaedic Surgery Operations, University of Utah, Salt Lake City, UT 84108, USA
Evelyn Lauren: Department of Biostatistics, Boston University, Boston, MA 02115, USA
Ryan Moffat: College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA
Weicong Su: Department of Mathematics, University of Utah, Salt Lake City, UT 84112, USA
Julie Xu: College of Nursing, University of Utah, Salt Lake City, UT 84112, USA
Jungweon Park: College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA
David Prince: College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA
Joseph Cheever: College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA
Frank W. Licari: College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA

IJERPH, 2020, vol. 17, issue 19, 1-13

Abstract: The goals of this study were to develop a risk prediction model in unmet dental care needs and to explore the intersection between social determinants of health and unmet dental care needs in the United States. Data from the 2016 Medical Expenditure Panel Survey were used for this study. A chi-squared test was used to examine the difference in social determinants of health between those with and without unmet dental needs. Machine learning was used to determine top predictors of unmet dental care needs and to build a risk prediction model to identify those with unmet dental care needs. Age was the most important predictor of unmet dental care needs. Other important predictors included income, family size, educational level, unmet medical needs, and emergency room visit charges. The risk prediction model of unmet dental care needs attained an accuracy of 82.6%, sensitivity of 77.8%, specificity of 87.4%, precision of 82.9%, and area under the curve of 0.918. Social determinants of health have a strong relationship with unmet dental care needs. The application of deep learning in artificial intelligence represents a significant innovation in dentistry and enables a major advancement in our understanding of unmet dental care needs on an individual level that has never been done before. This study presents promising findings and the results are expected to be useful in risk assessment of unmet dental care needs and can guide targeted intervention in the general population of the United States.

Keywords: unmet dental care need; artificial intelligence; deep learning; data science; machine learning; social determinants of health; precision dentistry; oral health outcomes (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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