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Deep learning in mental health outcome research: a scoping review.

ABSTRACT: Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.

SUBMITTER: Su C

PROVIDER: S-EPMC7293215 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Deep learning in mental health outcome research: a scoping review.

Su Chang C Xu Zhenxing Z Pathak Jyotishman J Wang Fei F

Translational psychiatry 20200422 1

Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior perfor ...[more]

PMID: 32532967

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Project description:BACKGROUND:Mental health issues among farmers are identified population health concerns. While one systematic review focused on suicide in farming populations in the United States, there have been no scoping studies examining mental health in farming communities worldwide. The objectives of this scoping review were to: provide a descriptive analysis of the literature pertaining to mental health outcomes in farming populations; describe the international scope of the research; and highlight published mental health services and interventions that have been evaluated. METHODS:Following Arksey and O'Malley's scoping review framework, five major health and science platforms were used to identify studies examining mental health outcomes in farming populations, worldwide. Studies that met a priori inclusion criteria that were published prior to December 31, 2017 were included in this review. Data synthesis and descriptive statistics were conducted using STATA 15® software; proportions were calculated by country. RESULTS:The initial literature search yielded 9,906 records. After title and abstract screening, 676 articles were reviewed in-full. Of these, 341 met a priori inclusion criteria. Studies included were conducted between 1979 and 2017; the majority (265; 77.7%) were published between 2002-2017. The most frequently measured outcomes were stress (41.9%), suicide (33.1%), and depression (32.6%). Over 70% of studies that examined stress described using quantitative research methods, most predominantly, cross-sectional designs (42.7%). Approximately 64% of studies that measured suicide reported using a quantitative approach; the largest proportion of included suicide studies (33.6%) described using cohort designs. Approximately 84% of studies that measured depression described using quantitative approaches; sixty percent of these studies reported using a cross-sectional study design. Twenty included studies described a mental health service or intervention (5.9%). CONCLUSIONS:This scoping review provides a critical overview of the literature examining mental health outcomes in farming populations worldwide. Given the importance of farming and agriculture, this review can be used to ensure future research complements existing work, avoids unnecessary overlap, and begins to tackle the less-studied mental health outcomes amongst farmers. These results can guide researchers to identified gaps in research and services, leading to a more informed approach to future work, and ultimately, a more comprehensive understanding of mental health among farmers worldwide.

Project description:Introduction:Globally, 10-20% of children and adolescents suffer from mental disorders, with half of all them starting by the age of 14 and three-quarters before the age of 25. In Nepal, 40% of the population is younger than 18 years of age, and as such there is a large proportion of the population that is at risk of developing a mental disorder. There has been a recent recognition of child and adolescent mental health problems in Nepal, although prior to this it had remained almost invisible on the health agenda. In response to growing concern, there is a need to conduct a review on children and adolescent mental health problems in Nepal. Objective:To review the existing studies on child and adolescent mental health problems in Nepal. Methodology:A scoping review approach was used to identify studies on child and adolescent mental problems in Nepal. A search of Medline and PubMed databases for articles published from the database inception to August 2018 was conducted. Results:Ten papers were identified, and they all together included 7876 participants. Two studies reported on Post traumatic Stress Symptoms (PTSS) and described a prevalence of 10.7% to 51% of earthquake-affected children and adolescents in the Kathmandu district of Nepal. Another study reported that 53.2% of former child soldiers met the cut-off score for PTSS. Two school surveys found that the prevalence of emotional and behavioural problems in school children ranged between 12.9 and 17.03%, whereas a study on emotional and behavioural disorders in homeless children reported a prevalence of 28.6%. The prevalence of Autism Spectrum Disorder (ASD) was estimated to be as high as three in every 1000 persons in Nepal by one study. The clinical prevalence of anxiety disorders was reported ranging from 18.8% to 24.4% while that of Attention Deficit Hyperactivity Disorder (ADHD) was 10-11.7% in various clinical samples of children and adolescents. Conclusion:Only a few studies on the prevalence of child and adolescent mental health in Nepal have been conducted. Clearly, there is a need for better study design and larger studies to understand more fully the prevalence of child and adolescent mental health disorders in Nepal, in order to adequately plan public health services accordingly.

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Project description:Mental health conditions can have significant negative impacts on wellbeing and healthcare systems. Despite their high prevalence worldwide, there is still insufficient recognition and accessible treatments. Many mobile apps are available to the general population that aim to support mental health needs; however, there is limited evidence of their effectiveness. Mobile apps for mental health are beginning to incorporate artificial intelligence and there is a need for an overview of the state of the literature on these apps. The purpose of this scoping review is to provide an overview of the current research landscape and knowledge gaps regarding the use of artificial intelligence in mobile health apps for mental health. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and Population, Intervention, Comparator, Outcome, and Study types (PICOS) frameworks were used to structure the review and the search. PubMed was systematically searched for randomised controlled trials and cohort studies published in English since 2014 that evaluate artificial intelligence- or machine learning-enabled mobile apps for mental health support. Two reviewers collaboratively screened references (MMI and EM), selected studies for inclusion based on the eligibility criteria and extracted the data (MMI and CL), which were synthesised in a descriptive analysis. 1,022 studies were identified in the initial search and 4 were included in the final review. The mobile apps investigated incorporated different artificial intelligence and machine learning techniques for a variety of purposes (risk prediction, classification, and personalisation) and aimed to address a wide range of mental health needs (depression, stress, and suicide risk). The studies' characteristics also varied in terms of methods, sample size, and study duration. Overall, the studies demonstrated the feasibility of using artificial intelligence to support mental health apps, but the early stages of the research and weaknesses in the study designs highlight the need for more research into artificial intelligence- and machine learning-enabled mental health apps and stronger evidence of their effectiveness. This research is essential and urgent, considering the easy availability of these apps to a large population.

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Deep learning in mental health outcome research: a scoping review.

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Deep learning in mental health outcome research: a scoping review.

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