Predicting incident cardiovascular disease among African-American adults: A deep learning approach to evaluate social determinants of health in the Jackson heart study.
Predicting incident cardiovascular disease among African-American adults: A deep learning approach to evaluate social determinants of health in the Jackson heart study.
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DOI：
10.1371/journal.pone.0294050
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发表时间：
2023
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
本研究试图利用机器学习方法来确定健康的社会决定因素是否能改善心血管疾病(CVD)的预测。在杰克逊心脏研究中，基线没有心血管病史的参与者被跟踪调查了10年，以确定首次心血管事件(即冠心病、中风、心力衰竭)。三种建模算法(即深度神经网络、随机生存森林、惩罚COX比例风险)被用来评估三个特征集(即人口学和标准/生物行为CVD危险因素[FS1]、FS1结合心理和社会经济CVD危险因素[FS2]和FS2结合环境特征[FS3])作为10年心血管疾病风险的预测因子。与假设相反，当加入健康的社会决定因素时，总体预测准确性并没有提高。然而，健康的社会决定因素包括首次心血管事件的前15个预测因素中的8个。健康指标的社会决定因素包括4个社会经济因素(保险状况和类型)、1个心理社会因素(歧视负担)和3个环境因素(户外体育活动资源密度，包括教学和水上活动；修正的不含酒精的零售食品环境指数；以及有利的食品店)。研究结果表明，虽然了解生物决定因素可以确定谁目前有患心血管疾病的风险和需要二级预防，但了解心血管疾病风险的上游社会决定因素可以通过确定政策和社区一级干预措施的目标位置和方式来指导一级预防工作，以促进个人健康行为的改变。
The present study sought to leverage machine learning approaches to determine whether social determinants of health improve prediction of incident cardiovascular disease (CVD). Participants in the Jackson Heart study with no history of CVD at baseline were followed over a 10-year period to determine first CVD events (i.e., coronary heart disease, stroke, heart failure). Three modeling algorithms (i.e., Deep Neural Network, Random Survival Forest, Penalized Cox Proportional Hazards) were used to evaluate three feature sets (i.e., demographics and standard/biobehavioral CVD risk factors [FS1], FS1 combined with psychosocial and socioeconomic CVD risk factors [FS2], and FS2 combined with environmental features [FS3]) as predictors of 10-year CVD risk. Contrary to hypothesis, overall predictive accuracy did not improve when adding social determinants of health. However, social determinants of health comprised eight of the top 15 predictors of first CVD events. The social determinates of health indicators included four socioeconomic factors (insurance status and types), one psychosocial factor (discrimination burden), and three environmental factors (density of outdoor physical activity resources, including instructional and water activities; modified retail food environment index excluding alcohol; and favorable food stores). Findings suggest that whereas understanding biological determinants may identify who is currently at risk for developing CVD and in need of secondary prevention, understanding upstream social determinants of CVD risk could guide primary prevention efforts by identifying where and how policy and community-level interventions could be targeted to facilitate changes in individual health behaviors.
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影响因子：
20.1
作者：
Ambale-Venkatesh B;Yang X;Wu CO;Liu K;Hundley WG;McClelland R;Gomes AS;Folsom AR;Shea S;Guallar E;Bluemke DA;Lima JAC
通讯作者：
Lima JAC
影响因子：
--
作者：
Davidson, K;Jonas, BS;Markovitz, JH
通讯作者：
Markovitz, JH
影响因子：
2.9
作者：
Berkowitz, Seth A.;Basu, Sanjay;Atlas, Steven J.
通讯作者：
Atlas, Steven J.
影响因子：
3.9
作者：
Barber, Sharrelle;Hickson, DeMarc A.;Earls, Felton
通讯作者：
Earls, Felton
影响因子：
2
作者：
Antolini, L;Boracchi, P;Biganzoli, E
通讯作者：
Biganzoli, E