11 papers with code • 3 benchmarks • 3 datasets
Depression Detection is the problem of identifying signs of depression in individuals. These signs might be identified in peoples’ speech, facial expressions and in the use of language.
Depression is a large-scale mental health problem and a challenging area for machine learning researchers in detection of depression.
A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams
With the rise of the Internet, there is a growing need to build intelligent systems that are capable of efficiently dealing with early risk detection (ERD) problems on social media, such as early depression detection, early rumor detection or identification of sexual predators.
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year.
Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews
In this work we propose a machine learning model for depression detection from transcribed clinical interviews.
Looking At The Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress
Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse.
Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical practice and pathological research.
In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach.
Depression is a prominent health challenge to the world, and early risk detection (ERD) of depression from online posts can be a promising technique for combating the threat.