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, developing drugs for central nervous system (CNS) disorders remains the most …
However, developing drugs for central nervous system (CNS) disorders remains the most …
Artificial intelligence in psychiatry research, diagnosis, and therapy
J Sun, QX Dong, SW Wang, YB Zheng, XX Liu… - Asian Journal of …, 2023 - Elsevier
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 …
[HTML][HTML] 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 …
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 …
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 …
The state of our understanding of the pathophysiology and optimal treatment of depression: glass half full or half empty?
CB Nemeroff - American Journal of Psychiatry, 2020 - Am Psychiatric Assoc
Major depressive disorder is a remarkably common and often severe psychiatric disorder
associated with high levels of morbidity and mortality. Patients with major depression are …
associated with high levels of morbidity and mortality. Patients with major depression are …
[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 …
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review
Mental disorders represent critical public health challenges as they are leading contributors
to the global burden of disease and intensely influence social and financial welfare of …
to the global burden of disease and intensely influence social and financial welfare of …
[HTML][HTML] 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 …
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 …