Yoon Sunmoo, Odlum Michelle, Lee Yeonsuk, Choi Thomas, Kronish Ian M, Davidson Karina W, Finkelstein Joseph
School of Nursing, Columbia University, New York, NY, USA.
College of Dental Medicine, Columbia University, New York, NY, USA.
Stud Health Technol Inform. 2018;251:241-244.
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.
我们应用深度学习算法构建关联模型，以预测城市拉丁裔便利样本中的牙齿松动情况。我们对深度学习的应用确定了年龄、总体健康状况、汽水摄入量、使用牙线情况、经济压力以及在美国居住的年限，这些是在输入的78个变量中与自我报告的牙齿松动最强的关联因素。深度学习的应用有助于深入了解预测牙齿松动的最重要的可改变和不可改变因素，可能对指导针对城市拉丁裔的有针对性干预措施有用。
