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- W3167179910 endingPage "100836" @default.
- W3167179910 startingPage "100836" @default.
- W3167179910 abstract "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).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.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).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." @default.
- W3167179910 created "2021-06-22" @default.
- W3167179910 creator A5014833581 @default.
- W3167179910 creator A5030510688 @default.
- W3167179910 creator A5056849976 @default.
- W3167179910 creator A5066731285 @default.
- W3167179910 creator A5082161404 @default.
- W3167179910 creator A5085896707 @default.
- W3167179910 creator A5090745602 @default.
- W3167179910 date "2021-09-01" @default.
- W3167179910 modified "2023-10-15" @default.
- W3167179910 title "A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects" @default.
- W3167179910 cites W1779879527 @default.
- W3167179910 cites W1898484762 @default.
- W3167179910 cites W1901616594 @default.
- W3167179910 cites W1939947548 @default.
- W3167179910 cites W1979067514 @default.
- W3167179910 cites W1986264790 @default.
- W3167179910 cites W1990486276 @default.
- W3167179910 cites W1994797259 @default.
- W3167179910 cites W2001277958 @default.
- W3167179910 cites W2007720560 @default.
- W3167179910 cites W2008920949 @default.
- W3167179910 cites W2020428231 @default.
- W3167179910 cites W2033261174 @default.
- W3167179910 cites W2048565577 @default.
- W3167179910 cites W2058248640 @default.
- W3167179910 cites W2087323818 @default.
- W3167179910 cites W2097064938 @default.
- W3167179910 cites W2106439732 @default.
- W3167179910 cites W2112746818 @default.
- W3167179910 cites W2113002312 @default.
- W3167179910 cites W2124645154 @default.
- W3167179910 cites W2128248134 @default.
- W3167179910 cites W2130474389 @default.
- W3167179910 cites W2132917208 @default.
- W3167179910 cites W2133144164 @default.
- W3167179910 cites W2139748299 @default.
- W3167179910 cites W2144189101 @default.
- W3167179910 cites W2145980258 @default.
- W3167179910 cites W2149604945 @default.
- W3167179910 cites W2150184534 @default.
- W3167179910 cites W2158844733 @default.
- W3167179910 cites W2159821265 @default.
- W3167179910 cites W2164184454 @default.
- W3167179910 cites W2275902735 @default.
- W3167179910 cites W2311653427 @default.
- W3167179910 cites W2326660675 @default.
- W3167179910 cites W2400851407 @default.
- W3167179910 cites W2414367335 @default.
- W3167179910 cites W2435232717 @default.
- W3167179910 cites W2463672120 @default.
- W3167179910 cites W2473581867 @default.
- W3167179910 cites W2507703898 @default.
- W3167179910 cites W2513506629 @default.
- W3167179910 cites W2515855575 @default.
- W3167179910 cites W2528497093 @default.
- W3167179910 cites W2536458098 @default.
- W3167179910 cites W2541219905 @default.
- W3167179910 cites W2551317447 @default.
- W3167179910 cites W2560774707 @default.
- W3167179910 cites W2562251009 @default.
- W3167179910 cites W2585093195 @default.
- W3167179910 cites W2588753285 @default.
- W3167179910 cites W2607328784 @default.
- W3167179910 cites W2610330682 @default.
- W3167179910 cites W2610332124 @default.
- W3167179910 cites W2610886376 @default.
- W3167179910 cites W2624816748 @default.
- W3167179910 cites W2734648346 @default.
- W3167179910 cites W2737657685 @default.
- W3167179910 cites W2753226723 @default.
- W3167179910 cites W2756750766 @default.
- W3167179910 cites W2765944559 @default.
- W3167179910 cites W2767551651 @default.
- W3167179910 cites W2770256320 @default.
- W3167179910 cites W2789453180 @default.
- W3167179910 cites W2790360578 @default.
- W3167179910 cites W2791280249 @default.
- W3167179910 cites W2799832321 @default.
- W3167179910 cites W2806993107 @default.
- W3167179910 cites W2810021747 @default.
- W3167179910 cites W2811060145 @default.
- W3167179910 cites W2890655815 @default.
- W3167179910 cites W2890921814 @default.
- W3167179910 cites W2891378911 @default.
- W3167179910 cites W2892458505 @default.
- W3167179910 cites W2908608914 @default.
- W3167179910 cites W2915093990 @default.
- W3167179910 cites W2921319512 @default.
- W3167179910 cites W2921495327 @default.
- W3167179910 cites W2926702249 @default.
- W3167179910 cites W2932104007 @default.
- W3167179910 cites W2939078500 @default.
- W3167179910 cites W2940494117 @default.
- W3167179910 cites W2946322287 @default.
- W3167179910 cites W2948579453 @default.
- W3167179910 cites W2954466797 @default.