AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Depression

Showing 191 to 200 of 351 articles

Clear Filters

A Model of Normality Inspired Deep Learning Framework for Depression Relapse Prediction Using Audiovisual Data.

Computer methods and programs in biomedicine
BACKGROUND: Depression (Major Depressive Disorder) is one of the most common mental illnesses. According to the World Health Organization, more than 300 million people in the world are affected. A first depressive episode can be solved by a spontaneo...

Comparison of three machine learning models to predict suicidal ideation and depression among Chinese adolescents: A cross-sectional study.

Journal of affective disorders
BACKGROUND: Machine learning (ML) algorithms based on various clinicodemographic, psychometric, and biographic factors have been used to predict depression, suicidal ideation, and suicide attempt in adolescents, but there is still a need for more acc...

The Impact of Engagement with the PARO Therapeutic Robot on the Psychological Benefits of Older Adults with Dementia.

Clinical gerontologist
OBJECTIVES: This study aimed to examine the effect of 8-weeks of a 60-minute PARO intervention to reduce depressive symptoms and loneliness in older adults with dementia and investigated changes in their emotional or behavioral expressions and level ...

Adolescent Depression Detection Model Based on Multimodal Data of Interview Audio and Text.

International journal of neural systems
Depression is a common mental disease that has a tendency to develop at a younger age. Early detection of depression with psychological intervention may effectively prevent youth suicide. The establishment of the computer-aided model may be efficient...

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...