As mental health (MH) disorders become increasingly prevalent, their multifaceted
symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing …

Depression is one of the most common mental illnesses, but few of the currently proposed in-
depth models based on social media data take into account both temporal and spatial …

Currently, the integration of artificial intelligence (AI) techniques with multimodal
physiological signals represents a pivotal approach to detect affective disorders (ADs). With …

Depression is a prevalent mental health disorder. The absence of specific biomarkers
makes clinical diagnosis highly subjective. This makes it difficult to make a definitive …

The early screening of depression is highly beneficial for patients to obtain better diagnosis
and treatment. While the effectiveness of utilizing voice data for depression detection has …

The diagnosis of depression in individuals with Parkinson's Disease (PD) through the
utilization of multimodal fusion techniques represents a significant domain. The primary …

Depression, a prominent contributor to global disability, affects a substantial portion of the
population. Efforts to detect depression from social media texts have been prevalent, yet …

Automatic depression detection based on audio and text representations from participants'
interviews has attracted widespread attention. However, most of previous researches only …

Social media posts provide valuable insight into the narrative of users and their intentions,
including providing an opportunity to automatically model whether a social media user is …

Depression is a common mental health disorder. With current depression detection
methods, specialized physicians often engage in conversations and physiological …