Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges

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Applied Computational Intelligence and Soft Com...(2022)

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TL;DR

A recent systematic review of machine learning approaches in predicting mental health problems with concrete recommendations on the potential future research and development of applying machine learning in the mental health field is presented.

Abstract

The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied in mental health problems. This paper presents a recent systematic review of machine learning approaches in predicting mental health problems. Furthermore, we will discuss the challenges, limitations, and future directions for the application of machine learning in the mental health field. We collect research articles and studies that are related to the machine learning approaches in predicting mental health problems by searching reliable databases. Moreover, we adhere to the PRISMA methodology in conducting this systematic review. We include a total of 30 research articles in this review after the screening and identification processes. Then, we categorize the collected research articles based on the mental health problems such as schizophrenia, bipolar disorder, anxiety and depression, posttraumatic stress disorder, and mental health problems among children. Discussing the findings, we reflect on the challenges and limitations faced by the researchers on machine learning in mental health problems. Additionally, we provide concrete recommendations on the potential future research and development of applying machine learning in the mental health field.

Authors

Jetli Chung

1 papers

J. Teo

1 papers

References62 items

Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI

A Machine Learning Approach to detect Depression and Anxiety using Supervised Learning

Predicting mental health problems in adolescence using machine learning techniques

Diagnosing schizophrenia with network analysis and a machine learning method

Machine learning algorithm validation with a limited sample size

Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges

Published in

Applied Computational Intelligence and Soft Com...(2022)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Jetli Chung

1 papers

J. Teo

1 papers

References62 items

Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI

A Machine Learning Approach to detect Depression and Anxiety using Supervised Learning

Predicting mental health problems in adolescence using machine learning techniques

Diagnosing schizophrenia with network analysis and a machine learning method

Machine learning algorithm validation with a limited sample size

Artificial Intelligence for Mental Health and Mental Illnesses: an Overview

Psycho Web: A Machine Learning Platform for the Diagnosis and Classification of Mental Disorders

Methodological Gaps in Predicting Mental Health States from Social Media: Triangulating Diagnostic Signals

AIOps: Real-World Challenges and Research Innovations

Speech‐based markers for posttraumatic stress disorder in US veterans

Review of Machine Learning Algorithms for Diagnosing Mental Illness

Machine learning and big data in psychiatry: toward clinical applications

A deep learning approach for diagnosing schizophrenic patients

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large‐scale multi‐sample study

Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization.

A review of machine learning prediction methods for anxiety disorders

Predicting anxiety and depression in elderly patients using machine learning technology

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity‐based single‐subject classification

Measuring Catastrophic Forgetting in Neural Networks

Compromised hippocampus‐striatum pathway as a potential imaging biomarker of mild‐traumatic brain injury and posttraumatic stress disorder

Functional Dysconnection of the Inferior Frontal Gyrus in Young People With Bipolar Disorder or at Genetic High Risk

Machine Learning for Medical Imaging.

Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study

Does trauma event type matter in the assessment of traumatic load?

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

An efficient data mining classification approach for detecting lung cancer disease

Forecasting the onset and course of mental illness with Twitter data

Cross-trial prediction of treatment outcome in depression: a machine learning approach.

Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning.

Evaluating the Visualization of What a Deep Neural Network Has Learned

Predicting mood changes in bipolar disorder through heartbeat nonlinear dynamics: A preliminary study

A Neural Network Based Model for Predicting Psychological Conditions

Machine learning: Trends, perspectives, and prospects

Teaching Machines to Read and Comprehend

Practical Predictive Analytics and Decisioning Systems for Medicine: Informatics Accuracy and Cost-Effectiveness for Healthcare Administration and Delivery Including Medical Research

Diagnostic and Statistical Manual of Mental Disorders

Discriminating unipolar and bipolar depression by means of fMRI and pattern classification: a pilot study

Examination of the predictive value of structural magnetic resonance scans in bipolar disorder: a pattern classification approach

Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression

Using Multivariate Machine Learning Methods and Structural MRI to Classify Childhood Onset Schizophrenia and Healthy Controls

A video based eye detection system for bipolar disorder diagnosis

An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders

EEG complexity as a biomarker for autism spectrum disorder risk

A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia

Towards the Mental Health Ontology

Data mining in bioinformatics: report on BIOKDD'03

The prevalence, clinical relevance, and public health significance of subthreshold depressions.

Randomized controlled trial of interventions designed to reduce the risk of progression to first-episode psychosis in a clinical sample with subthreshold symptoms.

Historical perspectives and natural history of bipolar disorder

Recurrence after recovery from major depressive disorder during 15 years of observational follow-up.

Psychiatric disorder onset and first treatment contact in the United States and Ontario.

Long Short-Term Memory

Development of a rating scale for primary depressive illness.

Screening of anxiety and depression among seafarers using machine learning technology

Prediction of Mental Health Problems Among Children Using Machine Learning Techniques

Speech-based assessment of PTSD in a military population using diverse feature classes

American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Association

Global burden of mental disorders and the need for a comprehensive, coordinated response from health and social workers at the country level

Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm

Field of Study

Computer Science

Journal Information

Name

Appl. Comput. Intell. Soft Comput.

Page

9970363:1-9970363:19

Volume

2022

Venue Information

Name

Applied Computational Intelligence and Soft Computing

Type

journal

URL

https://www.hindawi.com/journals/acisc/

Alternate Names

Appl Comput Intell Soft Comput

TL;DR

Abstract

Authors

References62 items

Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI

A Machine Learning Approach to detect Depression and Anxiety using Supervised Learning

Predicting mental health problems in adolescence using machine learning techniques

Diagnosing schizophrenia with network analysis and a machine learning method

Machine learning algorithm validation with a limited sample size

TL;DR

Abstract

Authors

References62 items

Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI

A Machine Learning Approach to detect Depression and Anxiety using Supervised Learning

Predicting mental health problems in adolescence using machine learning techniques

Diagnosing schizophrenia with network analysis and a machine learning method

Machine learning algorithm validation with a limited sample size

Artificial Intelligence for Mental Health and Mental Illnesses: an Overview

Ensemble Learning

Psycho Web: A Machine Learning Platform for the Diagnosis and Classification of Mental Disorders

Methodological Gaps in Predicting Mental Health States from Social Media: Triangulating Diagnostic Signals

AIOps: Real-World Challenges and Research Innovations

Speech‐based markers for posttraumatic stress disorder in US veterans

Review of Machine Learning Algorithms for Diagnosing Mental Illness

Machine learning and big data in psychiatry: toward clinical applications

A deep learning approach for diagnosing schizophrenic patients

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large‐scale multi‐sample study

Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization.

A review of machine learning prediction methods for anxiety disorders

Predicting anxiety and depression in elderly patients using machine learning technology

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity‐based single‐subject classification

Measuring Catastrophic Forgetting in Neural Networks

Compromised hippocampus‐striatum pathway as a potential imaging biomarker of mild‐traumatic brain injury and posttraumatic stress disorder

Functional Dysconnection of the Inferior Frontal Gyrus in Young People With Bipolar Disorder or at Genetic High Risk

Machine Learning for Medical Imaging.

Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study

Does trauma event type matter in the assessment of traumatic load?

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

An efficient data mining classification approach for detecting lung cancer disease

Forecasting the onset and course of mental illness with Twitter data

Cross-trial prediction of treatment outcome in depression: a machine learning approach.

Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning.

Evaluating the Visualization of What a Deep Neural Network Has Learned

Predicting mood changes in bipolar disorder through heartbeat nonlinear dynamics: A preliminary study

A Neural Network Based Model for Predicting Psychological Conditions

Machine learning: Trends, perspectives, and prospects

Teaching Machines to Read and Comprehend

Practical Predictive Analytics and Decisioning Systems for Medicine: Informatics Accuracy and Cost-Effectiveness for Healthcare Administration and Delivery Including Medical Research

Diagnostic and Statistical Manual of Mental Disorders

Discriminating unipolar and bipolar depression by means of fMRI and pattern classification: a pilot study

Examination of the predictive value of structural magnetic resonance scans in bipolar disorder: a pattern classification approach

Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression

Using Multivariate Machine Learning Methods and Structural MRI to Classify Childhood Onset Schizophrenia and Healthy Controls

A video based eye detection system for bipolar disorder diagnosis

An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders

EEG complexity as a biomarker for autism spectrum disorder risk

A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia

Towards the Mental Health Ontology

Data mining in bioinformatics: report on BIOKDD'03

The prevalence, clinical relevance, and public health significance of subthreshold depressions.

Randomized controlled trial of interventions designed to reduce the risk of progression to first-episode psychosis in a clinical sample with subthreshold symptoms.

Historical perspectives and natural history of bipolar disorder

Recurrence after recovery from major depressive disorder during 15 years of observational follow-up.

Psychiatric disorder onset and first treatment contact in the United States and Ontario.

Long Short-Term Memory

Development of a rating scale for primary depressive illness.

Screening of anxiety and depression among seafarers using machine learning technology

Prediction of Mental Health Problems Among Children Using Machine Learning Techniques

Speech-based assessment of PTSD in a military population using diverse feature classes

American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Association

Global burden of mental disorders and the need for a comprehensive, coordinated response from health and social workers at the country level

Field of Study

Journal Information

Name

Page

Volume

Venue Information