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Artificial intelligence in psychiatry research, diagnosis, and therapy
Psychiatric disorders are now responsible for the largest proportion of the global burden of
disease, and even more challenges have been seen during the COVID-19 pandemic …
disease, and even more challenges have been seen during the COVID-19 pandemic …
Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, develo** drugs for central nervous system (CNS) disorders remains the most …
However, develo** drugs for central nervous system (CNS) disorders remains the most …
[HTML][HTML] Methodological and quality flaws in the use of artificial intelligence in mental health research: systematic review
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 …
Functional neuroimaging in psychiatry and the case for failing better
Psychiatric disorders encompass complex aberrations of cognition and affect and are
among the most debilitating and poorly understood of any medical condition. Current …
among the most debilitating and poorly understood of any medical condition. Current …
Sparse Bayesian learning for end-to-end EEG decoding
Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-
computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG …
computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG …
Modern views of machine learning for precision psychiatry
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC),
the advent of functional neuroimaging, novel technologies and methods provide new …
the advent of functional neuroimaging, novel technologies and methods provide new …
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 …
Cognitive workload recognition using EEG signals and machine learning: A review
Machine learning and its subfield deep learning techniques provide opportunities for the
development of operator mental state monitoring, especially for cognitive workload …
development of operator mental state monitoring, especially for cognitive workload …
Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography
The understanding and treatment of psychiatric disorders, which are known to be
neurobiologically and clinically heterogeneous, could benefit from the data-driven …
neurobiologically and clinically heterogeneous, could benefit from the data-driven …
Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: a systematic review
Reliable and objective biomarkers promise to improve the assessment and treatment of
chronic pain. Resting-state electroencephalography (EEG) is broadly available, easy to use …
chronic pain. Resting-state electroencephalography (EEG) is broadly available, easy to use …