This paper is in the following e-collection/theme issue:

Reviews in Medical Informatics (85) Machine Learning (1440) Methods and New Tools in Mental Health Research (431) LGBTQ Issues (427) Substance Abuse (393) Mental Health Surveillance and Epidemiology (56) New Methods (927)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28962, first published .
Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review

Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review

Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review

Authors of this article:

Anasua Kundu1 Author Orcid Image ;   Michael Chaiton1, 2 Author Orcid Image ;   Rebecca Billington3 Author Orcid Image ;   Daniel Grace2 Author Orcid Image ;   Rui Fu4 Author Orcid Image ;   Carmen Logie3, 5 Author Orcid Image ;   Bruce Baskerville6, 7 Author Orcid Image ;   Christina Yager1 Author Orcid Image ;   Nicholas Mitsakakis2, 8 Author Orcid Image ;   Robert Schwartz1, 2, 4 Author Orcid Image
Article Authors Cited by (10) Tweetations (5) Metrics

Journals

  1. Albert K, Delano M. Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records. Patterns 2022;3(8):100534 View
  2. Kundu A, Fu R, Grace D, Logie C, Abramovich A, Baskerville B, Yager C, Schwartz R, Mitsakakis N, Planinac L, Chaiton M, Siau C. Correlates of wanting to seek help for mental health and substance use concerns by sexual and gender minority young adults during the COVID-19 pandemic: A machine learning analysis. PLOS ONE 2022;17(11):e0277438 View
  3. Kundu A, Kouzoukas E, Zawertailo L, Fougere C, Dragonetti R, Selby P, Schwartz R. Scoping review of guidance on cessation interventions for electronic cigarettes and dual electronic and combustible cigarettes use. CMAJ Open 2023;11(2):E336 View
  4. Tao X, Fisher C. Associations Among Web-Based Civic Engagement and Discrimination, Web-Based Social Support, and Mental Health and Substance Use Risk Among LGBT Youth: Cross-Sectional Survey Study. Journal of Medical Internet Research 2023;25:e46604 View
  5. Dharma C, Fu R, Chaiton M. Table 2 Fallacy in Descriptive Epidemiology: Bringing Machine Learning to the Table. International Journal of Environmental Research and Public Health 2023;20(13):6194 View
  6. Richardson M. Predicting Violent Extremism with Machine Learning: A Scoping Review. SN Computer Science 2023;5(1) View
  7. Ma S, Jiang S, Yang O, Zhang X, Fu Y, Zhang Y, Kaareen A, Ling M, Chen J, Shang C. Use of Machine Learning Tools in Evidence Synthesis of Tobacco Use Among Sexual and Gender Diverse Populations: Algorithm Development and Validation. JMIR Formative Research 2024;8:e49031 View
  8. Acharya N, Kar P, Ally M, Soar J. Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach. Applied Sciences 2024;14(4):1630 View
  9. Henry K, Stanley L, Swaim R. Risk and Promotive Factors Related to Cannabis Use Among American Indian Adolescents. Prevention Science 2024;25(5):734 View
  10. Yin Y, Workman T, Blosnich J, Brandt C, Skanderson M, Shao Y, Goulet J, Zeng-Treitler Q. Sexual and Gender Minority Status and Suicide Mortality: An Explainable Artificial Intelligence Analysis. International Journal of Public Health 2024;69 View