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. 2024 Oct 10;24(1):298.
doi: 10.1186/s12911-024-02663-4.

Machine learning applications in studying mental health among immigrants and racial and ethnic minorities: an exploratory scoping review

Khushbu Khatri Park  1 , Mohammad Saleem  1 , Mohammed Ali Al-Garadi  2 , Abdulaziz Ahmed  3   4
Affiliations

Machine learning applications in studying mental health among immigrants and racial and ethnic minorities: an exploratory scoping review

Khushbu Khatri Park et al. BMC Med Inform Decis Mak. .

Abstract

Background: The use of machine learning (ML) in mental health (MH) research is increasing, especially as new, more complex data types become available to analyze. By examining the published literature, this review aims to explore the current applications of ML in MH research, with a particular focus on its use in studying diverse and vulnerable populations, including immigrants, refugees, migrants, and racial and ethnic minorities.

Methods: From October 2022 to March 2024, Google Scholar, EMBASE, and PubMed were queried. ML-related, MH-related, and population-of-focus search terms were strung together with Boolean operators. Backward reference searching was also conducted. Included peer-reviewed studies reported using a method or application of ML in an MH context and focused on the populations of interest. We did not have date cutoffs. Publications were excluded if they were narrative or did not exclusively focus on a minority population from the respective country. Data including study context, the focus of mental healthcare, sample, data type, type of ML algorithm used, and algorithm performance were extracted from each.

Results: Ultimately, 13 peer-reviewed publications were included. All the articles were published within the last 6 years, and over half of them studied populations within the US. Most reviewed studies used supervised learning to explain or predict MH outcomes. Some publications used up to 16 models to determine the best predictive power. Almost half of the included publications did not discuss their cross-validation method.

Conclusions: The included studies provide proof-of-concept for the potential use of ML algorithms to address MH concerns in these special populations, few as they may be. Our review finds that the clinical application of these models for classifying and predicting MH disorders is still under development.

Keywords: Machine learning; Mental health; Minorities, disparities, review.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Study selection
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References

    1. Steel Z, et al. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980–2013. Int J Epidemiol. 2014;43(2):476–93. - PMC - PubMed
    1. Eylem O, et al. Stigma for common mental disorders in racial minorities and majorities a systematic review and meta-analysis. BMC Public Health. 2020;20(1):1–20. - PMC - PubMed
    1. Nochaiwong S, et al. Global prevalence of mental health issues among the general population during the coronavirus disease-2019 pandemic: a systematic review and meta-analysis. Sci Rep. 2021;11(1):1–18. - PMC - PubMed
    1. Organization WH. Wake-up call to all countries to step up mental health services and support. 2022 2 March 2022; https://www.who.int/news/item/02-03-2022-covid-19-pandemic-triggers-25-i...
    1. Bas-Sarmiento P, et al. Mental health in immigrants versus native population: a systematic review of the literature. Arch Psychiatr Nurs. 2017;31(1):111–21. - PubMed

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