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

被引:44
作者
Kino, Shiho [1 ,2 ]
Hsu, Yu-Tien [1 ]
Shiba, Koichiro [3 ]
Chien, Yung-Shin [1 ]
Mita, Carol [4 ]
Kawachi, Ichiro [1 ]
Daoud, Adel [5 ,6 ,7 ,8 ]
机构
[1] Harvard TH Chan Sch Publ Hlth, Dept Social & Behav Sci, Boston, MA USA
[2] Kyoto Univ, Dept Social Epidemiol, Kyoto, Japan
[3] Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Boston, MA USA
[4] Harvard Univ, Countway Lib Med, Boston, MA 02115 USA
[5] Harvard Univ, Harvard TH Chan Sch Publ Hlth, Ctr Populat & Dev Studies, Boston, MA 02115 USA
[6] Univ Gothenburg, Dept Sociol & Work Sci, Gothenburg, Sweden
[7] Chalmers Univ Technol, Div Data Sci & Artificial Intelligence, Dept Comp Sci & Engn, Gothenburg, Sweden
[8] Linkoping Univ, Inst Analyt Sociol, Linkoping, Sweden
关键词
Review; Machine learning; Social determinants of health; QUALITY-OF-LIFE; SOCIOECONOMIC-STATUS; DISEASE RISK; CAUSAL INFERENCE; TREE ANALYSIS; PREDICTORS; CLASSIFICATION; INDICATORS; NETWORKS; OPPORTUNITIES;
D O I
10.1016/j.ssmph.2021.100836
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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.
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页数:20
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