Machine learning in mental health: a sco** review of methods and applications
BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …
data applications for mental health, highlighting current research and applications in …
Gamma oscillations as a biomarker for major depression: an emerging topic
Identifying biomarkers for major depression is of high importance for improving diagnosis
and treatment of this common and debilitating neuropsychiatric disorder, as the field seeks …
and treatment of this common and debilitating neuropsychiatric disorder, as the field seeks …
Improving mental health services: A 50-year journey from randomized experiments to artificial intelligence and precision mental health
L Bickman - Administration and Policy in Mental Health and Mental …, 2020 - Springer
This conceptual paper describes the current state of mental health services, identifies critical
problems, and suggests how to solve them. I focus on the potential contributions of artificial …
problems, and suggests how to solve them. I focus on the potential contributions of artificial …
Applications of machine learning algorithms to predict therapeutic outcomes in depression: a meta-analysis and systematic review
Background No previous study has comprehensively reviewed the application of machine
learning algorithms in mood disorders populations. Herein, we qualitatively and …
learning algorithms in mood disorders populations. Herein, we qualitatively and …
[HTML][HTML] Methodological and quality flaws in the use of artificial intelligence in mental health research: systematic review
R Tornero-Costa, A Martinez-Millana… - JMIR Mental …, 2023 - mental.jmir.org
Background: Artificial intelligence (AI) is giving rise to a revolution in medicine and health
care. Mental health conditions are highly prevalent in many countries, and the COVID-19 …
care. Mental health conditions are highly prevalent in many countries, and the COVID-19 …
Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment
Informatics paradigms for brain and mental health research have seen significant advances
in recent years. These developments can largely be attributed to the emergence of new …
in recent years. These developments can largely be attributed to the emergence of new …
[PDF][PDF] Subgenual functional connectivity predicts antidepressant treatment response to transcranial magnetic stimulation: independent validation and evaluation of …
Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex
(DLPFC) is an established therapy for refractory depression. However, treatment outcomes …
(DLPFC) is an established therapy for refractory depression. However, treatment outcomes …
Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis
Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-
standing clinical challenge has prompted an increased focus on predictive models of …
standing clinical challenge has prompted an increased focus on predictive models of …
Alterations in EEG functional connectivity in individuals with depression: A systematic review
The brain works as an organised, network-like structure of functionally interconnected
regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of …
regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of …
Near transfer to an unrelated N-back task mediates the effect of N-back working memory training on matrix reasoning
The extent to which working memory training improves performance on untrained tasks is
highly controversial. Here we address this controversy by testing the hypothesis that far …
highly controversial. Here we address this controversy by testing the hypothesis that far …