AIMC Topic: Mental Health

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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...

The potential of socially assistive robots during infectious disease outbreaks.

Science robotics
Robots have a role in addressing the secondary impacts of infectious disease outbreaks by helping us sustain social distancing, monitoring and improving mental health, supporting education, and aiding in economic recovery.

Can robotic gait rehabilitation plus Virtual Reality affect cognitive and behavioural outcomes in patients with chronic stroke? A randomized controlled trial involving three different protocols.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND: The rehabilitation of cognitive and behavioral abnormalities in individuals with stroke is essential for promoting patient's recovery and autonomy. The aim of our study is to evaluate the effects of robotic neurorehabilitation using Lokom...

AI in mental health.

Current opinion in psychology
With the advent of digital approaches to mental health, modern artificial intelligence (AI), and machine learning in particular, is being used in the development of prediction, detection and treatment solutions for mental health care. In terms of tre...

A Systematic Review of Health Dialog Systems.

Methods of information in medicine
BACKGROUND:  Health dialog systems have seen increased adoption by patients, hospitals, and universities due to the confluence of advancements in machine learning and the ubiquity of high-performance hardware that supports real-time speech recognitio...

Deep learning in mental health outcome research: a scoping review.

Translational psychiatry
Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists a...

Predicting mental health problems in adolescence using machine learning techniques.

PloS one
BACKGROUND: Predicting which children will go on to develop mental health symptoms as adolescents is critical for early intervention and preventing future, severe negative outcomes. Although many aspects of a child's life, personality, and symptoms h...

Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models.

Psychological medicine
BACKGROUND: This study evaluated whether natural language processing (NLP) of psychotherapy note text provides additional accuracy over and above currently used suicide prediction models.