scholarly journals Machine Learning Applications in Mental health and Substance Use Research Among Lesbian, Gay, Bisexual, Transgender, Queer or Questioning and Two-spirit Population: Scoping Review (Preprint)

10.2196/28962 ◽  
2021 ◽  
Author(s):  
Anasua Kundu ◽  
Michael Chaiton ◽  
Rebecca Billington ◽  
Daniel Grace ◽  
Rui Fu ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Substance Use ◽  
Scoping Review ◽  
Machine Learning Applications ◽  
Two Spirit

Machine Learning Applications in Mental health and Substance Use Research Among Lesbian, Gay, Bisexual, Transgender, Queer or Questioning and Two-spirit Population: Scoping Review (Preprint)

2021 ◽  
Author(s):  
Anasua Kundu ◽  
Michael Chaiton ◽  
Rebecca Billington ◽  
Daniel Grace ◽  
Rui Fu ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Substance Use ◽  
Sexual Minorities ◽  
Model Development ◽  
Imaging Study ◽  
Future Research ◽  
Sexual Minority Women ◽  
Substance Addiction ◽  
Two Spirit

BACKGROUND People at high risk of mental health or substance addiction issues among sexual and gender minorities may have more nuanced characteristics that may not be easily discovered by traditional statistical methods. OBJECTIVE This review aimed at identifying literature that used machine learning to investigate mental health or substance use concerns among lesbian, gay, bisexual, transgender, queer or questioning and two-spirit (LGBTQ2S+) population as well as directing future research in this field. METHODS MEDLINE, EMBASE, PubMed, CINAHL Plus, PsycINFO and IEEE Xplore, Summon databases were searched from November to December 2020. We included original studies which used machine learning to explore mental health and/or substance use among LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development and discussion of study findings. RESULTS We included 11 studies in this review, of which 9 (82%) studies were on mental health and only 2 (18%) studies were on substance use concerns. All studies were published within last 2 years and majority were conducted in the Unites States. Among mutually non-exclusive population categories, sexual minorities male were the most commonly studied subgroup (n=5, 45%), while sexual minorities female were studied the least (n=2, 18%). Studies were categorized into 3 major domains: online content analysis (n=6, 55%), prediction modelling (n=4, 36%) and imaging study (n=1, 9%). CONCLUSIONS Machine learning can be a promising tool of capturing and analyzing hidden data of mental health and substance use concerns among LGBTQ2S+ people. In addition to conducting more research on sexual minority women, different mental health and substance use problems as well as outcomes, future research should explore newer environments and data sources and intersections with various social determinants of health.

scholarly journals Scoping review of stepped care interventions for mental health and substance use service delivery to youth and young adults

2021 ◽  
Author(s):  
Mai Berger ◽  
Saranee Fernando ◽  
AnnMarie Churchill ◽  
Peter Cornish ◽  
Joanna Henderson ◽  
...  
Keyword(s):  
Mental Health ◽  
Substance Use ◽  
Young Adults ◽  
Service Delivery ◽  
Scoping Review ◽  
Stepped Care ◽  
Youth And Young Adults

scholarly journals What makes mental health and substance use services youth friendly? A scoping review of literature

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Lisa D. Hawke ◽  
Kamna Mehra ◽  
Cara Settipani ◽  
Jaqueline Relihan ◽  
Karleigh Darnay ◽  
...  
Keyword(s):  
Mental Health ◽  
Substance Use ◽  
Scoping Review ◽  
Review Of Literature ◽  
Substance Use Services ◽  
Youth Friendly

Overview of the role of big data in mental health: A scoping review (Preprint)

2021 ◽  
Author(s):  
Arfan Ahmed ◽  
Sarah Aziz ◽  
Marco Angus ◽  
Mahmood Alzubaidi ◽  
Alaa Abd-Alrazaq ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Big Data ◽  
Scoping Review ◽  
Mental Health Disorders ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Great Effort ◽  
Scoping Reviews ◽  
Health Disorders

BACKGROUND Big Data offers promise in the field of mental health and plays an important part when it comes to automation, analysis and prevention of mental health disorders OBJECTIVE The purpose of this scoping review is to explore how big data was exploited in mental health. This review specifically addresses both the volume, velocity, veracity and variety of collected data as well as how data was attained, stored, managed, and kept private and secure. METHODS Six databases were searched to find relevant articles. PRISMA Extension for Scoping Reviews (PRISMA-ScR) was used as a guideline methodology to develop a comprehensive scoping review. RESULTS General and Big Data features were extracted from the studies reviewed. Various technologies were noted when it comes to using Big Data in mental health with depression and anxiety being the focus of most of the studies. Some of these included Machine Learning (ML) models in 22 studies of which Random Forest (RF) was the most widely used. Logistic Regression (LR) was used in 4 studies, and Support Vector Machine (SVM) was used in 3 studies. CONCLUSIONS In order to utilize Big Data as a way to mitigate mental health disorders and prevent their appearance altogether a great effort is still needed. Integration and analysis of Big Data, doctors and researchers alike can find patterns in otherwise difficult to identify data by making use of AI and Machine Learning techniques. Similarly, machine learning and artificial intelligence can be used to automate the analytical process.

scholarly journals School-based interventions for preventing substance use in indigenous children ages 7–13: a scoping review protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. e034032
Author(s):  
Geoffrey Maina ◽  
Taryn Phaneuf ◽  
Megan Kennedy ◽  
Maeve Mclean ◽  
Ann Gakumo ◽  
...  
Keyword(s):  
Mental Health ◽  
Substance Use ◽  
Scoping Review ◽  
Research Question ◽  
Physical And Mental Health ◽  
Substance Use Prevention ◽  
School Based ◽  
Scoping Reviews ◽  
Indigenous Children ◽  
School Based Interventions

IntroductionThroughout the world, indigenous peoples share traumatic colonial experiences that have caused gross inequalities for them and continue to impact every aspect of their lives. The effect of intergenerational trauma and other health disparities have been remarkable for Indigenous children and adolescents, who are at a greater risk of adverse mental health and addiction outcomes compared with non-indigenous people of the same age. Most indigenous children are exposed to addictive substances at an early age, which often leads to early initiation of substance use and is associated with subsequent physical and mental health issues, poor social and relational functioning, and occupational and legal problems. The aim of this paper is to report the protocol for the scoping review of school-based interventions for substance use prevention in Indigenous children ages 7–13 living in Canada, the USA, Australia and New Zealand. This scoping review seeks to answer the following questions: (1) What is known about indigenous school-based interventions for preventing substance use and (2) What are the characteristics and outcomes of school-based interventions for preventing substance use?Methods and analysisThis scoping review will use steps described by Arksey and O’Malley and Levac: (1) identifying the research question(s); (2) identifying relevant studies; (3) selecting the studies; (4) charting the data; (5) collating, summarising and reporting the results and (6) consulting with experts. Our findings will be reported according to the guidelines set by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.Ethics and disseminationEthics review approval is not required for this project. Findings from this study will be presented to lay public, at scientific conferences and published in a peer-reviewed journal.

scholarly journals Pragmatic neuroscience for clinical psychiatry

2019 ◽  
Vol 215 (01) ◽  
pp. 404-408 ◽  
Author(s):  
J. Douglas Steele ◽  
Martin P. Paulus
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Substance Use ◽  
Clinical Practice ◽  
Routine Clinical Practice ◽  
Pragmatic Approach ◽  
And Training ◽  
Program Grant ◽  
Significant Mortality

SummaryMental health and substance use disorders are the leading cause of long-term disability and a cause of significant mortality, worldwide. However, it is widely recognised that clinical practice in psychiatry has not fundamentally changed for over half a century. The Royal College of Psychiatrists is reviewing its trainee curriculum to identify neuroscience that relates to psychiatric practice. To date though, neuroscience has had very little impact on routine clinical practice. We discuss how a pragmatic approach to neuroscience can address this problem together with a route to implementation in National Health Service care. This has implications for altered funding priorities and training future psychiatrists. Five training recommendations for psychiatrists are identified.Declaration of interestJ.D.S. receives direct funding from MRC Program Grant MR/S010351/1 aimed at developing machine learning-based methods for routinely acquired NHS data and indirect funding from the Wellcome Trust STRADL study. M.P.P. receives payments for an UpToDate chapter on methamphetamine and is principal investigator on the following grants: NIGMS P20GM121312 and NIDA U01 DA041089 and receives support from the William K. Warren Foundation.

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