scholarly journals Machine learning in mental health: A systematic scoping review of methods and applications

2018 ◽  
Author(s):  
Adrian Shatte ◽  
Delyse Hutchinson ◽  
Samantha Teague
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Big Data ◽  
Psychological Functioning ◽  
Latent Dirichlet Allocation ◽  
Health Conditions ◽  
Support Vector ◽  
Mental Health Conditions ◽  
Study Results ◽  
Detection And Diagnosis

Objective This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. Materials and MethodsEight health and information technology research databases were searched using the terms “big data” or “machine learning” and “mental health”. Articles were assessed by two reviewers, and data were extracted on the article’s mental health application, ML technique, data type and size, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health; and, (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer’s Disease. ML techniques used included support vector machines, decision trees, neural networks, latent dirichlet allocation, and clustering.Discussion and ConclusionOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to improve other areas of psychological functioning. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

scholarly journals Machine learning in mental health: a scoping review of methods and applications

2019 ◽  
Vol 49 (09) ◽  
pp. 1426-1448 ◽  
Author(s):  
Adrian B. R. Shatte ◽  
Delyse M. Hutchinson ◽  
Samantha J. Teague
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Scoping Review ◽  
Latent Dirichlet Allocation ◽  
Health Conditions ◽  
Support Vector ◽  
Mental Health Conditions ◽  
Big Data Applications ◽  
Study Results ◽  
Detection And Diagnosis

AbstractBackgroundThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.MethodsWe employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.ConclusionsOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

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 Prevalence of mental health conditions and relationship with general health in a whole-country population of people with intellectual disabilities compared with the general population

BJPsych Open ◽  
2017 ◽  
Vol 3 (5) ◽  
pp. 243-248 ◽  
Author(s):  
Laura A. Hughes-McCormack ◽  
Ewelina Rydzewska ◽  
Angela Henderson ◽  
Cecilia MacIntyre ◽  
Julie Rintoul ◽  
...  
Keyword(s):  
Mental Health ◽  
General Population ◽  
Intellectual Disabilities ◽  
General Health ◽  
Large Scale ◽  
Health Conditions ◽  
Mental Health Conditions ◽  
Study Results ◽  
Country Study ◽  
People With Intellectual Disabilities

BackgroundThere are no previous whole-country studies on mental health and relationships with general health in intellectual disability populations; study results vary.AimsTo determine the prevalence of mental health conditions and relationships with general health in a total population with and without intellectual disabilities.MethodNinety-four per cent completed Scotland's Census 2011. Data on intellectual disabilities, mental health and general health were extracted, and the association between them was investigated.ResultsA total of 26 349/5 295 403 (0.5%) had intellectual disabilities. In total, 12.8% children, 23.4% adults and 27.2% older adults had mental health conditions compared with 0.3, 5.3 and 4.5% of the general population. Intellectual disabilities predicted mental health conditions; odds ratio (OR)=7.1 (95% CI 6.8–7.3). General health was substantially poorer and associated with mental health conditions; fair health OR=1.8 (95% CI 1.7–1.9), bad/very bad health OR=4.2 (95% CI 3.9–4.6).ConclusionsThese large-scale, whole-country study findings are important, given the previously stated lack of confidence in comparative prevalence results, and the need to plan services accordingly.

Machine learning methods for predicting postpartum depression: A scoping review (Preprint)

2021 ◽  
Author(s):  
Kiran Saqib ◽  
Amber Fozia Khan ◽  
Zahid Ahmad Butt
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Postpartum Depression ◽  
Scoping Review ◽  
Early Stage ◽  
Maternal Mental Health ◽  
Gradient Boosting ◽  
Support Vector ◽  
Study Results ◽  
Extreme Gradient Boosting

BACKGROUND Machine learning (ML) offers vigorous statistical and probabilistic techniques that can successfully predict certain clinical conditions using large volumes of data. A review of ML and big data research analytics in maternal depression is pertinent and timely given the rapid technological developments in recent years. OBJECTIVE This paper aims to synthesize the literature on machine learning and big data analytics for maternal mental health, particularly the prediction of postpartum depression (PPD). METHODS A scoping review methodology using the Arksey and O’Malley framework was employed to rapidly map the research activity in the field of ML for predicting PPD. A literature search was conducted through health and IT research databases, including PsycInfo, PubMed, IEEE Xplore and the ACM Digital Library from Sep 2020 till Jan 2021. Data were extracted on the article’s ML model, data type, and study results. RESULTS A total of fourteen (14) studies were identified. All studies reported the use of supervised learning techniques to predict PPD. Support vector machine (SVM) and random forests (RF) were the most commonly employed algorithms in addition to naïve Bayes, regression, artificial neural network, decision trees and extreme gradient boosting. There was considerable heterogeneity in the best performing ML algorithm across selected studies. The area under the receiver-operating-characteristic curve (AUC) values reported for different algorithms were SVM (Range: 0.78-0.86); RF method (0.88); extreme gradient boosting (0.80); logistic regression (0.93); and extreme gradient boosting (0.71) respectively. CONCLUSIONS ML algorithms are capable of analyzing larger datasets and performing more advanced computations, that can significantly improve the detection of PPD at an early stage. Further clinical-research collaborations are required to fine-tune ML algorithms for prediction and treatments. ML might become part of evidence-based practice, in addition to clinical knowledge and existing research evidence.

Machine learning methods for predicting postpartum depression: A scoping review (Preprint)

2021 ◽  
Author(s):  
Kiran Saqib ◽  
Amber Fozia Khan ◽  
Zahid Ahmad Butt
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Postpartum Depression ◽  
Scoping Review ◽  
Early Stage ◽  
Maternal Mental Health ◽  
Gradient Boosting ◽  
Support Vector ◽  
Study Results ◽  
Extreme Gradient Boosting

BACKGROUND Machine learning (ML) offers vigorous statistical and probabilistic techniques that can successfully predict certain clinical conditions using large volumes of data. A review of ML and big data research analytics in maternal depression is pertinent and timely given the rapid technological developments in recent years. OBJECTIVE This paper aims to synthesize the literature on machine learning and big data analytics for maternal mental health, particularly the prediction of postpartum depression (PPD). METHODS A scoping review methodology using the Arksey and O’Malley framework was employed to rapidly map the research activity in the field of ML for predicting PPD. Two independent researchers searched PsycInfo, PubMed, IEEE Xplore and the ACM Digital Library in September 2020 to identify relevant publications in the past 12 years. Data were extracted on the article’s ML model, data type, and study results. RESULTS A total of fourteen (14) studies were identified. All studies reported the use of supervised learning techniques to predict PPD. Support vector machine (SVM) and random forests (RF) were the most commonly employed algorithms in addition to naïve Bayes, regression, artificial neural network, decision trees and extreme gradient boosting. There was considerable heterogeneity in the best performing ML algorithm across selected studies. The area under the receiver-operating-characteristic curve (AUC) values reported for different algorithms were SVM (Range: 0.78-0.86); RF method (0.88); extreme gradient boosting (0.80); logistic regression (0.93); and extreme gradient boosting (0.71) respectively. CONCLUSIONS ML algorithms are capable of analyzing larger datasets and performing more advanced computations, that can significantly improve the detection of PPD at an early stage. Further clinical-research collaborations are required to fine-tune ML algorithms for prediction and treatments. ML might become part of evidence-based practice, in addition to clinical knowledge and existing research evidence.

70-LB: Fat Mass and Obesity-Associated rs9939609 Gene Variant Is Related to Fasting Glucose in Children with Mental Health Conditions

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 70-LB
Author(s):  
ALEJANDRA M. WIEDEMAN ◽  
YING FAI NGAI ◽  
AMANDA M. HENDERSON ◽  
CONSTADINA PANAGIOTOPOULOS ◽  
ANGELA M. DEVLIN
Keyword(s):  
Mental Health ◽  
Fat Mass ◽  
Fasting Glucose ◽  
Health Conditions ◽  
Gene Variant ◽  
Mental Health Conditions

Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

2019 ◽  
Vol 19 (25) ◽  
pp. 2301-2317 ◽  
Author(s):  
Ruirui Liang ◽  
Jiayang Xie ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Hai Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Big Data ◽  
Human Genome Project ◽  
Genome Project ◽  
Support Vector ◽  
Successful Implementation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

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