Department of Epidemiology, NYU School of Global Public Health, New York University, New York, New York.
Department of Social and Behavioral Sciences, NYU School of Global Public Health, New York University, New York, New York.
Am J Prev Med. 2021 Oct;61(4):596-605. doi: 10.1016/j.amepre.2021.04.016. Epub 2021 Jul 27.
Cardiovascular disease is the leading cause of death worldwide, and cardiovascular disease burden is increasing in low-resource settings and for lower socioeconomic groups. Machine learning algorithms are being developed rapidly and incorporated into clinical practice for cardiovascular disease prediction and treatment decisions. Significant opportunities for reducing death and disability from cardiovascular disease worldwide lie with accounting for the social determinants of cardiovascular outcomes. This study reviews how social determinants of health are being included in machine learning algorithms to inform best practices for the development of algorithms that account for social determinants.
A systematic review using 5 databases was conducted in 2020. English language articles from any location published from inception to April 10, 2020, which reported on the use of machine learning for cardiovascular disease prediction that incorporated social determinants of health, were included.
Most studies that compared machine learning algorithms and regression showed increased performance of machine learning, and most studies that compared performance with or without social determinants of health showed increased performance with them. The most frequently included social determinants of health variables were gender, race/ethnicity, marital status, occupation, and income. Studies were largely from North America, Europe, and China, limiting the diversity of the included populations and variance in social determinants of health.
Given their flexibility, machine learning approaches may provide an opportunity to incorporate the complex nature of social determinants of health. The limited variety of sources and data in the reviewed studies emphasize that there is an opportunity to include more social determinants of health variables, especially environmental ones, that are known to impact cardiovascular disease risk and that recording such data in electronic databases will enable their use.
心血管疾病是全球范围内的主要死因，心血管疾病负担在资源匮乏的环境中以及社会经济水平较低的人群中不断增加。机器学习算法正在迅速发展并被纳入临床实践，用于心血管疾病的预测和治疗决策。在全球范围内，利用机器学习算法来解决心血管结局的社会决定因素，为降低心血管疾病的死亡率和残疾率提供了重要机会。本研究综述了健康的社会决定因素是如何被纳入机器学习算法的，以指导制定考虑社会决定因素的算法的最佳实践。
2020 年进行了一项系统综述，使用了 5 个数据库。纳入了从成立之初到 2020 年 4 月 10 日发表的、报告了使用机器学习进行心血管疾病预测并纳入健康社会决定因素的英文文章，不论其发表地点。
大多数比较机器学习算法和回归的研究表明，机器学习的性能有所提高，而大多数比较有或没有社会决定因素的性能的研究表明，纳入社会决定因素可以提高性能。最常纳入的健康社会决定因素变量是性别、种族/民族、婚姻状况、职业和收入。研究主要来自北美、欧洲和中国，这限制了纳入人群的多样性和社会决定因素的差异。
鉴于机器学习方法的灵活性，它们可能为纳入健康社会决定因素的复杂性质提供了机会。在综述研究中，来源和数据的多样性有限，这强调了有机会纳入更多的社会决定因素健康变量，特别是那些已知会影响心血管疾病风险的环境因素，并且在电子数据库中记录这些数据将使其能够被使用。
