Multimodal Detection of Depression

Automatic detection of depression has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines. As a result, promising depression detection systems have been reported. Depression is a major mental health disorder that is rapidly affecting lives worldwide. With the increase in demand to detect depression automatically with the help of machine learning algorithms,We decided to find solution to detect depression at early stages.In this thesis, we address the Distress Analysis Interview Corpus-Wizard of Oz (DAIC- WOZ) database, comprising clinical interviews and questionnaire assessments of over a hundred individuals.

If these individuals can be identified, then we can develop ways to quickly mobilize resources to respond quickly to any increase in symptoms and methods to mitigate the short and long term effects of mental distress through ongoing baseline treatments.With the help of this thesis it is our aim to be able to determine who is at risk for distress before he or she shows the outward symptoms .The healthcare system is complex in its interconnections between patients, providers, pharmacies, and payers, with each entity having its own goals and data sources. By carefully considering each data source, we hope to move forward in the field of depression and anxiety research towards a better understanding of the disorders.