Lee Kwang-Sig, Park Kun Woo
Center for Artificial Intelligence, Korea University College of Medicine, Seoul 02841, Korea.
Department of Neurology, Korea University College of Medicine, Seoul 02841, Korea.
Geriatrics (Basel). 2019 Mar 25;4(1):30. doi: 10.3390/geriatrics4010030.
This study introduces a new framework based on an artificial neural network (ANN) for testing whether social determinants are major determinants of association among diabetes mellitus, visual impairment and hearing loss in a middle-aged or old population.
The data came from the Korean Longitudinal Study of Aging (2014⁻2016), with 6120 participants aged 45 years or more. The association was divided into eight categories: one category for having no disease, three categories for having one, three categories for having two and one category for having three. Variable importance, the effect of a variable on model performance, was used to evaluate the hypothesis based on whether family support, socioeconomic status and social activity in Y2014 are among the top 10 determinants of the association in the year 2016 (Y2016).
Based on variable importance from the ANN, brothers/sisters cohabiting (0.0167), voluntary activity (0.0148), income (0.0125), family activity (0.0125), parents alive (0.0121), leisure activity (0.0095) and meeting with friends (0.0092) in Y2014 are the top-10 determinants of comorbidity in Y2016.
The findings of this study support the hypothesis, highlighting the importance of social determinants for the effective management of the comorbidities of the three diseases.
本研究引入了一种基于人工神经网络(ANN)的新框架,用于测试社会决定因素是否是中老年人群中糖尿病、视力障碍和听力损失之间关联的主要决定因素。
数据来自韩国老年纵向研究(2014 - 2016年),有6120名年龄在45岁及以上的参与者。这种关联分为八类:一类为无疾病,三类为患有一种疾病,三类为患有两种疾病,一类为患有三种疾病。变量重要性即变量对模型性能的影响,用于基于2014年的家庭支持、社会经济地位和社会活动是否是2016年关联的前10个决定因素来评估该假设。
基于人工神经网络的变量重要性,2014年的兄弟姐妹共同居住(0.0167)、志愿活动(0.0148)、收入(0.0125)、家庭活动(0.0125)、父母健在(0.0121)、休闲活动(0.0095)和与朋友见面(0.0092)是2016年共病的前10个决定因素。
本研究结果支持该假设,突出了社会决定因素对有效管理这三种疾病共病的重要性。