Applying Deep Learning to Understand Predictors of Tooth Mobility Among Urban Latinos
Applying Deep Learning to Understand Predictors of Tooth Mobility Among Urban Latinos
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DOI：
10.3233/978-1-61499-880-8-241
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发表时间：
2018
影响因子：
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通讯作者：
Sunmoo Yoon;M. Odlum;Yeonsu Lee;T. Choi;I. Kronish;K. Davidson;J. Finkelstein
中科院分区：
文献类型：
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作者：
Sunmoo Yoon;M. Odlum;Yeonsu Lee;T. Choi;I. Kronish;K. Davidson;J. Finkelstein
我们应用深度学习算法建立相关模型，预测城市拉丁美洲人的牙齿移动情况。我们的深度学习应用程序在输入的78个变量中确定了年龄、总体健康状况、苏打水消费量、使用牙线、经济压力和在美国生活的年数是自我报告的牙齿活动性的最强相关性。深度学习的应用有助于深入了解预测牙齿移动的最重要的可修改和不可修改的因素，并可能有助于指导城市拉丁美洲人的针对性干预。
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.