Applying Deep Learning to Understand Predictors of Tooth Mobility among Urban Latinos

Sunmoo Yoon, Michelle Odlum, Yeonsuk Lee, Thomas Choi, Ian M. Kronish, Karina W. Davidson, Joseph Finkelstein

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

8 Scopus citations


We applied deep learning algorithms to build correlate models that predict tooth mobility in a convenience sample of urban Latinos. Our application of deep learning identified age, general health, soda consumption, flossing, financial stress, and years living in the US as the strongest correlates of self-reported tooth mobility among 78 variables entered. The application of deep learning was useful for gaining insights into the most important modifiable and non-modifiable factors predicting tooth mobility, and maybe useful for guiding targeted interventions in urban Latinos.

Original languageEnglish
Title of host publicationData, Informatics and Technology
Subtitle of host publicationAn Inspiration for Improved Healthcare
EditorsJoseph Liaskos, Mowafa S. Househ, Parisis Gallos, Arie Hasman, John Mantas
PublisherIOS Press
Number of pages4
ISBN (Electronic)9781614998792
StatePublished - 2018
Externally publishedYes

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


  • Latinos
  • Tooth mobility
  • aging
  • deep learning
  • symptom science


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