FEIT Research Project Database

Digital technology and artificial intelligence for mental health

Project Leader: Simon D'Alfonso
Collaborators: Mario Alvarez-Jimenez (Orygen and the University of Melbourne Centre for Youth Mental Health)
Primary Contact: Simon D'Alfonso (dalfonso@unimelb.edu.au)
Keywords: artificial intelligence; mobile phones; natural language processing; social media; ubiquitous computing
Disciplines: Computing and Information Systems

This project aims to build upon our existing work on systems for digital mental health therapy. We have been working for some years now with Orygen Digital (the digital mental health division of Orygen Youth Mental Health) on designing, building and testing digital/online technologies for young people with mental health conditions, as well as their parents and carers. The flagship result of this work has been the Moderated Online Social Therapy (MOST) web platform. This program was initiated with the aim of investigating two questions, one based in psychology and the other based in human-computer interaction:

  • Efficacy of online therapy: do the clinical benefits of specialised face-to-face youth mental health programmes extend into long-term improvements through online psychosocial intervention?
  • Technology design: how can we best design and implement engaging technology for young people with mental-ill health?

Having successfully established this program, we are now starting to investigate how the latest digital technologies and artificial intelligence methods can be employed in this space to deliver cutting-edge solutions for mental health. Some example ideas are:

  1. Intelligent data-driven recommendation systems that can deliver relevant online therapy modules to a user, based on their psychological profile and other pertinent data.
  2. The prediction/detection of mental health conditions based on a person’s online usage patterns and computational linguistic analysis of the content of their posts and comments.
  3. The use of smartphone passive and active sensing data to detect a person’s psychological status (or determine their digital phenotype) and deliver contextually and clinically relevant (digital) therapy.
  4. Chatbots to assist users in finding useful information on mental health, performing psychometric testing and carrying out therapeutic conversations.

Further information: https://mdhs.unimelb.edu.au/our-organisation/institutes-centres-departments/cymh/research/research-groups/digital-mental-health