Depression diagnosis using multimodal machine learning

Depression is diagnosed by professional therapists through interviews involving a set of questions and analysis of responses and behaviors of individuals suspected to have depression. We propose a machine learning solution that analyses patterns in various modalities of input, such as video, audio, and text, from the interview of the individual to be diagnosed with depression, in order to generate a depression score.