AIMC Topic: Depression

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Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Depression is a common disease worldwide, with about 280 million people having depression. The unique facial features of depression provide a basis for automatic recognition of depression with deep convolutional neural networks. MATERIAL A...

EEG based depression recognition using improved graph convolutional neural network.

Computers in biology and medicine
Depression is a global psychological disease that does serious harm to people. Traditional diagnostic method of the doctor-patient communication, is not objective and accurate enough. Thus, a more accurate and objective method for depression detectio...

DAC Stacking: A Deep Learning Ensemble to Classify Anxiety, Depression, and Their Comorbidity From Reddit Texts.

IEEE journal of biomedical and health informatics
Depression is the most incapacitating disease worldwide, and it has an alarming comorbidity rate with anxiety. The use of social networks to expose personal difficulties has enabled works on the automatic identification of specific mental conditions,...

Deep learning-based integration of genetics with registry data for stratification of schizophrenia and depression.

Science advances
Currently, psychiatric diagnoses are, in contrast to most other medical fields, based on subjective symptoms and observable signs and call for new and improved diagnostics to provide the most optimal care. On the basis of a deep learning approach, we...

A novel machine learning approach to shorten depression risk assessment for convenient uses.

Journal of affective disorders
BACKGROUND: Depression is a mental disorder affecting many people worldwide which has been exacerbated by the current pandemic. There is an urgent need for a reliable yet short scale for individuals to self-assess the risk of depression conveniently ...

Arabic Speech Analysis for Classification and Prediction of Mental Illness due to Depression Using Deep Learning.

Computational intelligence and neuroscience
Depression is a global prevalent ailment for possible mental illness or mental disorder globally. Recognizing depressed early signs is critical for evaluating and preventing mental illness. With the progress of machine learning, it is possible to mak...

Development and validation of a meta-learner for combining statistical and machine learning prediction models in individuals with depression.

BMC psychiatry
BACKGROUND: The debate of whether machine learning models offer advantages over standard statistical methods when making predictions is ongoing. We discuss the use of a meta-learner model combining both approaches as an alternative.

Depression screening using a non-verbal self-association task: A machine-learning based pilot study.

Journal of affective disorders
BACKGROUND: Effective screening is important to combat the raising burden of depression and opens a critical time window for early intervention. Clinical use of non-verbal depression screening is nascent, yet a promising and viable candidate to suppl...

Polysomnographic identification of anxiety and depression using deep learning.

Journal of psychiatric research
Anxiety and depression are common psychiatric conditions associated with significant morbidity and healthcare costs. Sleep is an evolutionarily conserved health state. Anxiety and depression have a bidirectional relationship with sleep. This study re...

Automated detection of clinical depression based on convolution neural network model.

Biomedizinische Technik. Biomedical engineering
As a common mental disorder, depression is placing an increasing burden on families and society. However, the current methods of depression detection have some limitations, and it is essential to find an objective and efficient method. With the devel...