Machine learning-based behavioral diagnostic tools for depression: advances, challenges, and future directions
The psychiatric diagnostic procedure is currently based on self-reports that are subject to
personal biases. Therefore, the diagnostic process would benefit greatly from data-driven …
personal biases. Therefore, the diagnostic process would benefit greatly from data-driven …
A systematic review on automated clinical depression diagnosis
Assessing mental health disorders and determining treatment can be difficult for a number of
reasons, including access to healthcare providers. Assessments and treatments may not be …
reasons, including access to healthcare providers. Assessments and treatments may not be …
An insight into diagnosis of depression using machine learning techniques: a systematic review
Background In this modern era, depression is one of the most prevalent mental disorders
from which millions of individuals are affected today. The symptoms of depression are …
from which millions of individuals are affected today. The symptoms of depression are …
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 …
Machine learning algorithms for depression: diagnosis, insights, and research directions
Over the years, stress, anxiety, and modern-day fast-paced lifestyles have had immense
psychological effects on people's minds worldwide. The global technological development …
psychological effects on people's minds worldwide. The global technological development …
Machine learning approaches for diagnosing depression using EEG: A review
Y Liu, C Pu, S Xia, D Deng, X Wang… - Translational Neuroscience, 2022 - degruyter.com
Depression has become one of the most crucial public health issues, threatening the quality
of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of …
of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of …
Integrating neurobiological markers of depression
Objective: To integrate neuroimaging data associated with multiple symptom-related neural
processes and demonstrate their utility in the context of depression by deriving a predictive …
processes and demonstrate their utility in the context of depression by deriving a predictive …
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 …
Machine learning-based definition of symptom clusters and selection of antidepressants for depressive syndrome
IB Kim, SC Park - Diagnostics, 2021 - mdpi.com
The current polythetic and operational criteria for major depression inevitably contribute to
the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has …
the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has …
Detecting neuroimaging biomarkers for depression: a meta-analysis of multivariate pattern recognition studies
Background Multiple studies have examined functional and structural brain alteration in
patients diagnosed with major depressive disorder (MDD). The introduction of multivariate …
patients diagnosed with major depressive disorder (MDD). The introduction of multivariate …