[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 …
Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis
A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2022 - Elsevier
Depression is one of the significant contributors to the global burden disease, affecting
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …
[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 …
Challenges and future prospects of precision medicine in psychiatry
M Manchia, C Pisanu, A Squassina… - Pharmacogenomics …, 2020 - Taylor & Francis
Precision medicine is increasingly recognized as a promising approach to improve disease
treatment, taking into consideration the individual clinical and biological characteristics …
treatment, taking into consideration the individual clinical and biological characteristics …
[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 …
[HTML][HTML] Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning
C Liu, X Wang, C Liu, Q Sun, W Peng - Biomedical engineering online, 2020 - Springer
Background Chest CT screening as supplementary means is crucial in diagnosing novel
coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning …
coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning …
Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on EEG
Abstract Major Depressive Disorder (MDD) is one of the leading causes of disability
worldwide. Prediction of response to Selective Serotonin Reuptake Inhibitors (SSRIs) …
worldwide. Prediction of response to Selective Serotonin Reuptake Inhibitors (SSRIs) …
[HTML][HTML] Prediction of remission among patients with a major depressive disorder based on the resting-state functional connectivity of emotion regulation networks
H Wu, R Liu, J Zhou, L Feng, Y Wang, X Chen… - Translational …, 2022 - nature.com
The prediction of antidepressant response is critical for psychiatrists to select the initial
antidepressant drug for patients with major depressive disorders (MDD). The implicated …
antidepressant drug for patients with major depressive disorders (MDD). The implicated …
[HTML][HTML] Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning
M Xu, X Zhang, Y Li, S Chen, Y Zhang, Z Zhou… - Translational …, 2022 - nature.com
Major depressive disorder (MDD) is a severe brain disease associated with a significant risk
of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to …
of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to …
Classification of Parkinson's disease using a region-of-interest-and resting-state functional magnetic resonance imaging-based radiomics approach
To investigate the value of combining amplitude of low-frequency fluctuations-based
radiomics and the support vector machine classifier method in distinguishing patients with …
radiomics and the support vector machine classifier method in distinguishing patients with …