5 papers with code • 2 benchmarks • 1 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.
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.
Ranked #2 on Depression Detection on eRisk 2017
Depression is a large-scale mental health problem and a challenging area for machine learning researchers in detection of depression.
In this work we propose a machine learning model for depression detection from transcribed clinical interviews.