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Depression

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"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...

Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review.

Journal of medical Internet research
BACKGROUND: Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psych...

EEG-based deep learning model for the automatic detection of clinical depression.

Physical and engineering sciences in medicine
Clinical depression is a neurological disorder that can be identified by analyzing the Electroencephalography (EEG) signals. However, the major drawback in using EEG to accurately identify depression is the complexity and variation that exist in the ...

Deep neural networks detect suicide risk from textual facebook posts.

Scientific reports
Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56-0.58). In this study, Artificial Neural Network (ANN) models were constructed to pred...

Can machine learning be useful as a screening tool for depression in primary care?

Journal of psychiatric research
Depression is a widespread disease with a high economic burden and a complex pathophysiology disease that is still not wholly clarified, not to mention it usually is associated as a risk factor for absenteeism at work and suicide. Just 50% of patient...

Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood.

Psychological medicine
BACKGROUND: Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and s...

A deep learning model for detecting mental illness from user content on social media.

Scientific reports
Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identify a user's mental state based on his/her posting information. To this end, we collected posts from m...