[HTML][HTML] AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges
Background Mental health disorders are currently the main contributor to poor quality of life
and years lived with disability. Symptoms common to many mental health disorders lead to …
and years lived with disability. Symptoms common to many mental health disorders lead to …
Artificial intelligence for analyzing mental health disorders in social media: a quarter-century narrative review of progress and challenges
Background: Mental health disorders are currently the main contributor to poor quality of life
and years lived with disability. Symptoms common to many mental health disorders lead to …
and years lived with disability. Symptoms common to many mental health disorders lead to …
Sentiment-guided Transformer with Severity-aware Contrastive Learning for Depression Detection on Social Media
Early identification of depression is beneficial to public health surveillance and disease
treatment. There are many models that mainly treat the detection as a binary classification …
treatment. There are many models that mainly treat the detection as a binary classification …
DAC stacking: A deep learning ensemble to classify anxiety, depression, and their comorbidity from Reddit texts
VB de Souza, JC Nobre… - IEEE Journal of Biomedical …, 2022 - ieeexplore.ieee.org
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 …
comorbidity rate with anxiety. The use of social networks to expose personal difficulties has …
Convolution neural network having multiple channels with own attention layer for depression detection from social data
People share textual posts about their interests, routines, and moods on social platforms,
which can be targeted to evaluate their mental state using diverse techniques such as …
which can be targeted to evaluate their mental state using diverse techniques such as …
Analyzing reddit data: Hybrid model for depression sentiment using FastText embedding
Depression, a prevalent mental condition worldwide, exerts a substantial influence on
various aspects of human cognition, emotions, and behavior. The alarming increase in …
various aspects of human cognition, emotions, and behavior. The alarming increase in …
Interpretable hierarchical deep learning model for noninvasive Alzheimer's disease diagnosis
M Zokaeinikoo, P Kazemian… - INFORMS Journal on …, 2023 - pubsonline.informs.org
Alzheimer's disease is one of the leading causes of death in the world. Alzheimer's is
typically diagnosed through expensive imaging methods, such as positron emission …
typically diagnosed through expensive imaging methods, such as positron emission …
Deep knowledge-infusion for explainable depression detection
Discovering individuals depression on social media has become increasingly important.
Researchers employed ML/DL or lexicon-based methods for automated depression …
Researchers employed ML/DL or lexicon-based methods for automated depression …
Investigating machine learning and natural language processing techniques applied for detecting eating disorders: a systematic literature review
Recent developments in the fields of natural language processing (NLP) and machine
learning (ML) have shown significant improvements in automatic text processing. At the …
learning (ML) have shown significant improvements in automatic text processing. At the …
Review of Advancements in Depression Detection Using Social Media Data
A large population embraced social media to share thoughts, emotions, and daily
experiences through text, images, audio, or video posts. This user-generated content (UGC) …
experiences through text, images, audio, or video posts. This user-generated content (UGC) …