Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom
Artificial intelligence (AI) is increasingly employed in health care fields such as oncology,
radiology, and dermatology. However, the use of AI in mental health care and …
radiology, and dermatology. However, the use of AI in mental health care and …
Computational psychiatry as a bridge from neuroscience to clinical applications
Translating advances in neuroscience into benefits for patients with mental illness presents
enormous challenges because it involves both the most complex organ, the brain, and its …
enormous challenges because it involves both the most complex organ, the brain, and its …
Machine learning approaches for clinical psychology and psychiatry
Machine learning approaches for clinical psychology and psychiatry explicitly focus on
learning statistical functions from multidimensional data sets to make generalizable …
learning statistical functions from multidimensional data sets to make generalizable …
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 …
Correlation analysis to identify the effective data in machine learning: Prediction of depressive disorder and emotion states
S Kumar, I Chong - International journal of environmental research and …, 2018 - mdpi.com
Correlation analysis is an extensively used technique that identifies interesting relationships
in data. These relationships help us realize the relevance of attributes with respect to the …
in data. These relationships help us realize the relevance of attributes with respect to the …
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 …
The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database
In neuroscience, electroencephalography (EEG) data is often used to extract features
(biomarkers) to identify neurological or psychiatric dysfunction or to predict treatment …
(biomarkers) to identify neurological or psychiatric dysfunction or to predict treatment …
Electroencephalographic biomarkers for treatment response prediction in major depressive illness: a meta-analysis
AS Widge, MT Bilge, R Montana… - American Journal of …, 2019 - Am Psychiatric Assoc
Objective: Reducing unsuccessful treatment trials could improve depression treatment.
Quantitative EEG (QEEG) may predict treatment response and is being commercially …
Quantitative EEG (QEEG) may predict treatment response and is being commercially …
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 …
Internet-based cognitive-behavioral therapy for depression: current progress and future directions
Abstract The World Health Organization estimates that during a given 12-month period,
approximately 34 million people suffering from major depressive disorder go untreated in …
approximately 34 million people suffering from major depressive disorder go untreated in …