[HTML][HTML] The role of alpha oscillations among the main neuropsychiatric disorders in the adult and developing human brain: evidence from the last 10 years of …
Alpha oscillations (7–13 Hz) are the dominant rhythm in both the resting and active brain.
Accordingly, translational research has provided evidence for the involvement of aberrant …
Accordingly, translational research has provided evidence for the involvement of aberrant …
Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect
Mobile and wearable devices embedded with multiple sensors for health monitoring and
disease diagnosis are growing fields with the potential to provide efficient means for remote …
disease diagnosis are growing fields with the potential to provide efficient means for remote …
The promise of machine learning in predicting treatment outcomes in psychiatry
For many years, psychiatrists have tried to understand factors involved in response to
medications or psychotherapies, in order to personalize their treatment choices. There is …
medications or psychotherapies, in order to personalize their treatment choices. There is …
An electroencephalographic signature predicts antidepressant response in major depression
Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part
because the clinical diagnosis of major depression encompasses biologically …
because the clinical diagnosis of major depression encompasses biologically …
[HTML][HTML] AI-assisted prediction of differential response to antidepressant classes using electronic health records
Antidepressant selection is largely a trial-and-error process. We used electronic health
record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants …
record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants …
Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis
Background Multiple treatments are effective for major depressive disorder (MDD), but the
outcomes of each treatment vary broadly among individuals. Accurate prediction of …
outcomes of each treatment vary broadly among individuals. Accurate prediction of …
[HTML][HTML] Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis
D Watts, RF Pulice, J Reilly, AR Brunoni… - Translational …, 2022 - nature.com
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 …
[HTML][HTML] 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 …
Major depressive disorder classification based on different convolutional neural network models: Deep learning approach
The human brain is characterized by complex structural, functional connections that
integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation …
integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation …
[HTML][HTML] Neuroimaging biomarkers for predicting treatment response and recurrence of major depressive disorder
SG Kang, SE Cho - International journal of molecular sciences, 2020 - mdpi.com
The acute treatment duration for major depressive disorder (MDD) is 8 weeks or more.
Treatment of patients with MDD without predictors of treatment response and future …
Treatment of patients with MDD without predictors of treatment response and future …