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Translating Intersectionality to Fair Machine Learning in Health Sciences

RePEc: Research Papers in Economics(2023)10.31235/osf.io/gu7yhSource: DataRank Database

Translating Intersectionality to Fair Machine Learning in Health Sciences is a research paper published in RePEc: Research Papers in Economics (2023). On theSindex it has a DataRank of 0. It has been cited 3 times.

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Open Access3 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (2 grants)OA: goldTopics (31)IDs (PubMed, OpenAlex)SDGs (2)

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Authors (2)

William La Cava ORCID,Elle Lett ORCID

Live enrichment

PubMed:19162232 OpenAlex:https://openalex.org/W4322630701

National Institutes of Health

Grant: 5T32HD040128-15

Research Training in Pediatric Emergency Medicine

National Institutes of Health

Grant: 5R00LM012926-04

Multi-objective representation learning methods for interpetable predictions of patient outcomesusing electronic health records

goldLicense: cc-by

https://osf.io/gu7yh/download

https://doi.org/10.31235/osf.io/gu7yh

repositoryhttps://osf.io/download/63fd1bd940cecd079876f20c/

repositoryhttp://osf.io/gu7yh/

Fields of Study

Sex and Gender in HealthcareClimate Change and Health ImpactsGender Politics and Representation03 medical and health sciences0305 other medical science

Keywords

IntersectionalityCognitive reframingOppressionHealth equityEconomic JusticePsychological interventionComputer scienceRacismProcess (computing)Field (mathematics)Artificial intelligenceSociologyMachine learningPsychologySocial psychologyPolitical sciencePoliticsEconomicsGender studiesHealth careMicroeconomicsMathematicsSocial StatisticsPublic Affairs, Public Policy and Public AdministrationSocial and Behavioral SciencesRace, Gender, and Class

Sustainable Development Goals

SDG 5: 5. Gender equalitySDG 10: 10. No inequality