AIMC Topic: Depression

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I, robot: depression plays different roles in human-human and human-robot interactions.

Translational psychiatry
Socially engaging robots have been increasingly applied to alleviate depressive symptoms and to improve the quality of social life among different populations. Seeing that depression negatively influences social reward processing in everyday interact...

Deep graph neural network-based prediction of acute suicidal ideation in young adults.

Scientific reports
Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general popu...

Multi-Modal Adaptive Fusion Transformer Network for the Estimation of Depression Level.

Sensors (Basel, Switzerland)
Depression is a severe psychological condition that affects millions of people worldwide. As depression has received more attention in recent years, it has become imperative to develop automatic methods for detecting depression. Although numerous mac...

Effectiveness of robot therapy in the management of behavioural and psychological symptoms for individuals with dementia: A systematic review and meta-analysis.

Journal of psychiatric research
Robot therapy presents a promising alternative in dementia care. However, its effectiveness has not been verified comprehensively. This systematic review and meta-analysis aim at evaluating the effectiveness of robot therapy in the management of beha...

Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning.

Clinical EEG and neuroscience
Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for early and accurate detection of mood disorders. EEG signals are highly irregular, nonlinear, and nonstationary in nature and are traditionally studied fro...

Simple action for depression detection: using kinect-recorded human kinematic skeletal data.

BMC psychiatry
BACKGROUND: Depression, a common worldwide mental disorder, which brings huge challenges to family and social burden around the world is different from fluctuant emotion and psychological pressure in their daily life. Although body signs have been sh...

"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction.

PloS one
With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provi...

Artificial Intelligence, Social Media and Depression. A New Concept of Health-Related Digital Autonomy.

The American journal of bioethics : AJOB
The development of artificial intelligence (AI) in medicine raises fundamental ethical issues. As one example, AI systems in the field of mental health successfully detect signs of mental disorders, such as depression, by using data from social media...

Artificial neural networks for simultaneously predicting the risk of multiple co-occurring symptoms among patients with cancer.

Cancer medicine
Patients with cancer often exhibit multiple co-occurring symptoms which can impact the type of treatment received, recovery, and long-term health. We aim to simultaneously predict the risk of three symptoms: severe pain, moderate-severe depression, a...

Deep learning for the prediction of treatment response in depression.

Journal of affective disorders
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-specific biomarker may be a considerable step towards personalized ps...