Shiho Kino|Yu-Tien Hsu|Koichiro Shiba|Yung-Shin Chien|Carol Mita|Ichiro Kawachi|Adel Daoud
A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects
Kyoto University|Harvard University|Harvard University|Harvard University|Harvard University|Boston Public Library|Harvard University|Harvard University|Chalmers University of Technology|Harvard University|University of Gothenburg|Linköping University
machine learning ml spread rapidly computer science several disciplines given predictive capacity ml offers new opportunities health behavioral social scientists however remains unclear extent ml used studies social determinants health sdhusing four search engines conducted scoping review studies used ml study sdh published may 1 2020 two independent reviewers analyzed relevant studies study identified research questions results data algorithms synthesized findings narrative reportof initial 8097 hits identified 82 relevant studies number publications risen past decade half studies n 46 used us data 80 n 66 utilized surveys 70 n 57 employed ml common prediction tasks although number studies ml sdh growing rapidly studies used ml improve causal inference curate data identify social bias predictions ie algorithmic fairnesswhile ml equips researchers new ways measure health outcomes determinants nonconventional sources text audio image data studies still rely traditional surveys although guarantees ml lead better social epidemiological research potential innovation sdh research evident result harnessing predictive power ml causality data curation algorithmic fairness