Title
Faculty Advisor
Agu, Emmanuel O.
Faculty Advisor
Rundensteiner, Elke A.
Abstract
The hallmark indicator of depressive disorders is a presence of sad, empty, or irritable mood, accompanied by somatic and cognitive changes that significantly affect the individuals capacity to function. The overall goal of our project is to provide a tool for doctors to effortlessly detect depression, and in effect achieve greater coverage in detecting depression over the general population. We use machine learning techniques to create a mobile application that infers a smartphone users severity of depression from data scraped off their phone and social media websites. Through our study, we have demonstrated the feasibility of this approach to diagnosing depression, achieving an average testset RMSE of 5.67 across all modalities in the task of PHQ-9 score predictions.
Publisher
Worcester Polytechnic Institute
Date Accepted
March 2018
Major
Computer Science
Project Type
Major Qualifying Project
Copyright Statement
All authors have granted to WPI a nonexclusive royalty-free license to distribute copies of the work, subject to other agreements. Copyright is held by the author or authors, with all rights reserved, unless otherwise noted.
Accessibility
Unrestricted
Advisor Department
Computer Science