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A scoping review on...
A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects
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- Kino, S. (författare)
- Harvard School of Public Health, United States,Kyoto University,Harvard TH Chan Sch Publ Hlth, MA USA; Kyoto Univ, Japan
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- Hsu, Y. T. (författare)
- Harvard School of Public Health, United States,Harvard TH Chan Sch Publ Hlth, MA USA
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- Shiba, K. (författare)
- Harvard School of Public Health, United States,Harvard TH Chan Sch Publ Hlth, MA USA
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- Chien, Y. S. (författare)
- Harvard School of Public Health, United States,Harvard TH Chan Sch Publ Hlth, MA USA
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- Mita, C. (författare)
- Harvard University,Harvard Univ, MA 02115 USA
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- Kawachi, I. (författare)
- Harvard School of Public Health, United States,Harvard TH Chan Sch Publ Hlth, MA USA
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- Daoud, Adel, 1981 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för sociologi och arbetsvetenskap,Department of Sociology and Work Science,University of Gothenburg,Linköpings universitet,Linköping University,Harvard University,Institutet för analytisk sociologi, IAS,Filosofiska fakulteten,Harvard Univ, MA 02115 USA; Univ Gothenburg, Sweden; Chalmers Univ Technol, Sweden
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(creator_code:org_t)
- Elsevier BV, 2021
- 2021
- Engelska.
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Ingår i: SSM - Population Health. - : Elsevier BV. - 2352-8273. ; 15
- Relaterad länk:
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https://doi.org/10.1...
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Abstract
Ämnesord
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- 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. © 2021
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
- SAMHÄLLSVETENSKAP -- Sociologi -- Socialpsykologi (hsv//swe)
- SOCIAL SCIENCES -- Sociology -- Social Psychology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Annan teknik -- Mediateknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Other Engineering and Technologies -- Media Engineering (hsv//eng)
Nyckelord
- Machine learning
- Review
- Social determinants of health
- algorithm
- human
- narrative
- prediction
- search engine
- systematic review
- Machine learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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