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 Early intervention services for non-psychotic mental health disorders: a scoping review protocol

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e033656 ◽  
Author(s):  
Katie Richards ◽  
Amelia Austin ◽  
Karina Allen ◽  
Ulrike Schmidt
Keyword(s):  
Mental Health ◽  
Early Intervention ◽  
Scoping Review ◽  
Mental Health Disorders ◽  
Mental Health Disorder ◽  
Publication Type ◽  
Early Intervention Services ◽  
Scoping Reviews ◽  
Health Disorders ◽  
Intervention Services

IntroductionWorldwide mental health disorders are associated with a considerable amount of human suffering, disability and mortality. Yet, the provision of rapid evidence-based care to mitigate the human and economic costs of these disorders is limited. The greatest progress in developing and delivering early intervention services has occurred within psychosis. There is now growing support for and calls to extend such approaches to other diagnostic groups. The aim of this scoping review is to systematically map the emerging literature on early intervention services for non-psychotic mental health disorders, with a focus on outlining how services are structured, implemented and scaled.Methods and analysisThe protocol was developed using the guidance for scoping reviews in the Joanna Briggs Institute manual and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews checklist. A systematic search for published and unpublished literature will be conducted using the following databases: (1) MEDLINE, (2) PsycINFO, (3) HMIC, (4) EMBASE and (5) ProQuest. To be included, documents must describe and/or evaluate an early intervention service for adolescents or adults with a non-psychotic mental health disorder. There will be no restrictions on publication type, study design and date. Title and abstract, and full-text screening will be completed by one reviewer, with a proportion of articles screened in duplicate. Data analysis will primarily involve a qualitatively summary of the early intervention literature, the characteristics of early intervention services and key findings relating to their evaluation and implementation.Ethics and disseminationThe synthesis of published and unpublished articles will not require ethical approval. The results of this scoping review will be published in a peer-reviewed journal and disseminated via social media, conference presentations and other knowledge translation activities.

The physical literacy of children with behavioural and emotional mental health disorders: A scoping review

2018 ◽  
Vol 15 ◽  
pp. 95-131 ◽  
Author(s):  
Kathryn Fortnum ◽  
Bonnie Furzer ◽  
Siobhan Reid ◽  
Ben Jackson ◽  
Catherine Elliott
Keyword(s):  
Mental Health ◽  
Scoping Review ◽  
Mental Health Disorders ◽  
Physical Literacy ◽  
Health Disorders

The Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry – A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Victoria Welch ◽  
Tom Joshua Wy ◽  
Anna Ligezka ◽  
Leslie C. Hassett ◽  
Paul E. Croarkin ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Scoping Review ◽  
Adolescent Psychiatry ◽  
Child And Adolescent ◽  
Mental Health Disorders ◽  
Wearable Devices ◽  
Autism Spectrum ◽  
Child And Adolescent Psychiatry ◽  
Health Disorders

BACKGROUND Mental health disorders across the life span are a leading cause of medical disabilities. This burden is particularly significant in children and adolescents due to challenges in diagnoses and lack of precision medicine approaches. The advent and widespread adoption of wearable devices (e.g., smartwatches) that generate large volumes of passively collected data that are conducive for artificial intelligence applications to remotely diagnose and manage child and adolescent mental health disorders is promising. OBJECTIVE This study conducted a scoping review to study, characterize and identify areas of innovations with wearable devices that can augment current in-person physician assessments to individualize diagnosis and management of mental health disorders in child and adolescent psychiatry. METHODS This scoping review used PRISMA’s information as a guide. A comprehensive search of several databases from 2011 to June 25, 2021, limited to English language and excluding animal studies, was conducted. The databases included Ovid MEDLINE (R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily, Ovid Embase, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Web of Science, and Scopus. RESULTS The initial search yielded 344 articles. 19 articles were left on the final source list for this scoping review. Articles were divided into three main groups: Studies with the main focus on Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorders (ADHD) and Internalizing disorders such as anxiety disorders. Majority of the studies used either ECG strap or wrist worn biosensor. CONCLUSIONS Our scoping review found large heterogeneity of methods and findings in artificial intelligence studies in child psychiatry. Overall, the largest gaps identified in this scoping review are the lack of randomized control trials, most available studies are pilot feasibility trials.

scholarly journals Review on Machine Learning Techniques to predict Bipolar Disorder

2021 ◽  
Author(s):  
Nisha Agnihotri
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Bipolar Disorder ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Brain Disorder ◽  
Research Papers ◽  
Learning Techniques

<i>Bipolar disorder, a complex disorder in brain has affected many millions of people around the world. This brain disorder is identified by the occurrence of the oscillations of the patient’s changing mood. The mood swing between two states i.e. depression and mania. This is a result of different psychological and physical features. A set of psycholinguistic features like behavioral changes, mood swings and mental illness are observed to provide feedback on health and wellness. The study is an objective measure of identifying the stress level of human brain that could improve the harmful effects associated with it considerably. In the paper, we present the study prediction of symptoms and behavior of a commonly known mental health illness, bipolar disorder using Machine Learning Techniques. Therefore, we extracted data from articles and research papers were studied and analyzed by using statistical analysis tools and machine learning (ML) techniques. Data is visualized to extract and communicate meaningful information from complex datasets on predicting and optimizing various day to day analyses. The study also includes the various research papers having machine Learning algorithms and different classifiers like Decision Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression and K- Nearest Neighbor are studied and analyzed for identifying the mental state in a target group. The purpose of the paper is mainly to explore the challenges, adequacy and limitations in detecting the mental health condition using Machine Learning Techniques</i>

scholarly journals Review on Machine Learning Techniques to predict Bipolar Disorder

2021 ◽  
Author(s):  
Nisha Agnihotri
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Bipolar Disorder ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Brain Disorder ◽  
Research Papers ◽  
Learning Techniques

<i>Bipolar disorder, a complex disorder in brain has affected many millions of people around the world. This brain disorder is identified by the occurrence of the oscillations of the patient’s changing mood. The mood swing between two states i.e. depression and mania. This is a result of different psychological and physical features. A set of psycholinguistic features like behavioral changes, mood swings and mental illness are observed to provide feedback on health and wellness. The study is an objective measure of identifying the stress level of human brain that could improve the harmful effects associated with it considerably. In the paper, we present the study prediction of symptoms and behavior of a commonly known mental health illness, bipolar disorder using Machine Learning Techniques. Therefore, we extracted data from articles and research papers were studied and analyzed by using statistical analysis tools and machine learning (ML) techniques. Data is visualized to extract and communicate meaningful information from complex datasets on predicting and optimizing various day to day analyses. The study also includes the various research papers having machine Learning algorithms and different classifiers like Decision Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression and K- Nearest Neighbor are studied and analyzed for identifying the mental state in a target group. The purpose of the paper is mainly to explore the challenges, adequacy and limitations in detecting the mental health condition using Machine Learning Techniques</i>

Male farmers with mental health disorders: A scoping review

2013 ◽  
Vol 21 (1) ◽  
pp. 3-7 ◽  
Author(s):  
Philippe Roy ◽  
Gilles Tremblay ◽  
John L. Oliffe ◽  
Jalila Jbilou ◽  
Steve Robertson
Keyword(s):  
Mental Health ◽  
Scoping Review ◽  
Mental Health Disorders ◽  
Health Disorders

Acoustic Diversity Classification Using Machine Learning Techniques: Towards Automated Marine Big Data Analysis

2020 ◽  
Vol 29 (03n04) ◽  
pp. 2060011
Author(s):  
Emna Hachicha Belghith ◽  
François Rioult ◽  
Medjber Bouzidi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Acoustic Diversity ◽  
Marine Data

During the last years, big data has become the new emerging trend that increasingly attracting the attention of the R&D community in several fields (e.g., image processing, database engineering, data mining, artificial intelligence). Marine data is part of these fields which accommodates this growth, hence the appearance of marine big data paradigm that monitoring advocates the assessment of human impact on marine data. Nonetheless, supporting acoustic sounds classification is missing in such environment, with taking into account the diversity of such data (i.e., sounds of living undersea species, sounds of human activities, and sounds of environmental effects). To overcome this issue, we propose in this paper an approach that efficiently allowing acoustic diversity classification using machine learning techniques. The aim is to reach an automated support of marine big data analysis. We have conducted a set of experiments, using a real marine dataset, in order to validate our approach and show its effectiveness and efficiency. To do so, three machine learning techniques are employed: (i) classic machine learning models (i.e., k-nearest neighbor and support vector machine), (ii) deep learning based on convolutional neural networks, and (iii) transfer learning based on the reuse of pretrained models.

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