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Literature DB >> 31742353

Teaching yourself about structural racism will improve your machine learning.

Whitney R Robinson¹, Audrey Renson², Ashley I Naimi³.

Abstract

In this commentary, we put forth the following argument: Anyone conducting machine learning in a health-related domain should educate themselves about structural racism. We argue that structural racism is a critical body of knowledge needed for generalizability in almost all domains of health research.

Keywords: Causal inference; Directed acyclic graphs; Machine learning; Structural racism

Mesh：

Year: 2020 PMID： 31742353 PMCID： PMC7868043 DOI： 10.1093/biostatistics/kxz040

Source DB: PubMed Journal: Biostatistics ISSN： 1465-4644 Impact factor: 5.899

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Review 5. Structural racism and health inequities in the USA: evidence and interventions.

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7. Medical advances and racial/ethnic disparities in cancer survival.

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8. Machine learning in social epidemiology: Learning from experience.

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9. Race, ethnicity and lung function: A brief history.

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Journal: Am J Epidemiol Date: 2020-10-01 Impact factor: 4.897

Review 2. Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic.

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Journal: Curr HIV/AIDS Rep Date: 2020-06 Impact factor: 5.071

3. Racism and perinatal health inequities research: where we have been and where we should go.

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5. Conceptualizing, Contextualizing, and Operationalizing Race in Quantitative Health Sciences Research.

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6. Beyond Two Cultures: Cultural Infrastructure for Data-driven Decision Support.

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7. Measuring Structural Racism: A Guide for Epidemiologists and Other Health Researchers.

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Review 8. Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes.

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9. Identifying counties at risk of high overdose mortality burden during the emerging fentanyl epidemic in the USA: a predictive statistical modelling study.

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Review 10. The Role of Artificial Intelligence in Early Cancer Diagnosis.

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