A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects
Reviewartikel, 2021

Background: Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH). Methods: Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report. Results: Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness). Conclusions: While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

Machine learning


Social determinants of health


Shiho Kino

Harvard School of Public Health

Kyoto University

Yu Tien Hsu

Harvard School of Public Health

Koichiro Shiba

Harvard School of Public Health

Yung Shin Chien

Harvard School of Public Health

Carol Mita

Harvard University

Ichiro Kawachi

Harvard School of Public Health

Adel Daoud

Göteborgs universitet

Linköpings universitet

Chalmers, Data- och informationsteknik, Data Science

Harvard University

SSM - Population Health

2352-8273 (eISSN)

Vol. 15 100836



Bioinformatik (beräkningsbiologi)




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