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 …
Machine learning in major depression: From classification to treatment outcome prediction
S Gao, VD Calhoun, J Sui - CNS neuroscience & therapeutics, 2018 - Wiley Online Library
Aims Major depression disorder (MDD) is the single greatest cause of disability and
morbidity, and affects about 10% of the population worldwide. Currently, there are no …
morbidity, and affects about 10% of the population worldwide. Currently, there are no …
Applications of machine learning algorithms to predict therapeutic outcomes in depression: a meta-analysis and systematic review
Y Lee, RM Ragguett, RB Mansur, JJ Boutilier… - Journal of affective …, 2018 - Elsevier
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 …
Analysis of features selected by a deep learning model for differential treatment selection in depression
J Mehltretter, C Rollins, D Benrimoh… - Frontiers in Artificial …, 2020 - frontiersin.org
Background: Deep learning has utility in predicting differential antidepressant treatment
response among patients with major depressive disorder, yet there remains a paucity of …
response among patients with major depressive disorder, yet there remains a paucity of …
Predicting treatment outcome in depression: an introduction into current concepts and challenges
Improving response and remission rates in major depressive disorder (MDD) remains an
important challenge. Matching patients to the treatment they will most likely respond to …
important challenge. Matching patients to the treatment they will most likely respond to …
Deep learning for the prediction of treatment response in depression
L Squarcina, FM Villa, M Nobile, E Grisan… - Journal of affective …, 2021 - Elsevier
Background Mood disorders are characterized by heterogeneity in severity, symptoms and
treatment response. The possibility of selecting the correct therapy on the basis of patient …
treatment response. The possibility of selecting the correct therapy on the basis of patient …
Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation.
Abstract Machine learning (ML) has been introduced into the medical field as a means to
provide diagnostic tools capable of enhancing accuracy and precision while minimizing …
provide diagnostic tools capable of enhancing accuracy and precision while minimizing …
The potential of predictive analytics to provide clinical decision support in depression treatment planning
RC Kessler - Current opinion in psychiatry, 2018 - journals.lww.com
Recommendations include using large prospective observational studies followed by
pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary …
pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary …
Deep learning in mental health outcome research: a scoping review
Mental illnesses, such as depression, are highly prevalent and have been shown to impact
an individual's physical health. Recently, artificial intelligence (AI) methods have been …
an individual's physical health. Recently, artificial intelligence (AI) methods have been …
Machine learning and big data in psychiatry: toward clinical applications
Highlights•The combination of data-driven machine learning and theory-driven
computational models holds great promise for psychiatry.•Machine-learning analyses of …
computational models holds great promise for psychiatry.•Machine-learning analyses of …