liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects
Harvard TH Chan Sch Publ Hlth, MA USA; Kyoto Univ, Japan.
Harvard TH Chan Sch Publ Hlth, MA USA.
Harvard TH Chan Sch Publ Hlth, MA USA.
Harvard TH Chan Sch Publ Hlth, MA USA.
Show others and affiliations
2021 (English)In: SSM - Population Health, ISSN 2352-8273, Vol. 15, article id 100836Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier Science , 2021. Vol. 15, article id 100836
Keywords [en]
Review; Machine learning; Social determinants of health
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:liu:diva-179824DOI: 10.1016/j.ssmph.2021.100836ISI: 000697998100019PubMedID: 34169138OAI: oai:DiVA.org:liu-179824DiVA, id: diva2:1600234
Note

Funding Agencies| [JP20J01910]

Available from: 2021-10-04 Created: 2021-10-04 Last updated: 2021-10-26

Open Access in DiVA

fulltext(967 kB)180 downloads
File information
File name FULLTEXT01.pdfFile size 967 kBChecksum SHA-512
2028dbc5ccb212294ef90af67e3fee5f465cc3caabf11ddf7c45b29ac0b2ab53d08ed2b01f68a92b5ccd24ec02472eae1512e33fe3345fe5292ccf7eae0d5f0d
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Daoud, Adel
By organisation
The Institute for Analytical Sociology, IASFaculty of Arts and Sciences
In the same journal
SSM - Population Health
Bioinformatics (Computational Biology)

Search outside of DiVA

GoogleGoogle Scholar
Total: 180 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 95 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf