Student Work

Sensing Depression

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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 individual's 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 user's 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.

  • This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
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Identifier
  • E-project-032218-173040
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Year
  • 2018
Date created
  • 2018-03-22
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Permanent link to this page: https://digital.wpi.edu/show/rj4306059