Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, 1716 9th Ave S, Birmingham, AL, 35233, USA.
Department of Biomedical Informatics, School of Medicine, Vanderbilt University, 1161 21st Ave S # D3300, Nashville, TN, 37232, USA.
BMC Med Inform Decis Mak. 2024 Oct 10;24(1):298. doi: 10.1186/s12911-024-02663-4.
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.
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.
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.
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.
机器学习 (ML) 在心理健康 (MH) 研究中的应用正在增加，尤其是随着新的、更复杂的数据类型可供分析。通过检查已发表的文献，本综述旨在探讨 ML 在 MH 研究中的当前应用，特别关注其在研究多样化和弱势群体中的应用，包括移民、难民、移民以及种族和族裔少数群体。
从 2022 年 10 月到 2024 年 3 月，我们在 Google Scholar、EMBASE 和 PubMed 上进行了查询。将 ML 相关、MH 相关和关注人群的搜索词与布尔运算符一起串联起来。还进行了回溯参考搜索。纳入的同行评议研究报告了在 MH 背景下使用 ML 方法或应用，并关注感兴趣的人群。我们没有截止日期。如果出版物是叙述性的，或者没有专门关注来自各自国家的少数民族群体，则将其排除在外。从每个出版物中提取数据，包括研究背景、心理健康护理重点、样本、数据类型、使用的 ML 算法类型以及算法性能。
最终纳入了 13 篇同行评议出版物。所有文章均在过去 6 年内发表，其中一半以上研究了美国的人群。大多数综述研究使用监督学习来解释或预测 MH 结果。有些出版物使用多达 16 个模型来确定最佳预测能力。近一半的纳入出版物没有讨论他们的交叉验证方法。
所纳入的研究为在这些特殊人群中使用 ML 算法解决 MH 问题提供了概念验证，尽管可能很少。我们的综述发现，这些模型在分类和预测 MH 障碍方面的临床应用仍在开发中。
