Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis
This work illustrates the use of causality and decision-theoretic principles to personalize interventions in digital mental health tools.
Authors
- Adam Poulsen (ORCID: https://orcid.org/0000-0002-0001-3894)
- Bridianne O’Dea (ORCID: https://orcid.org/0000-0003-1731-210X)
- Frank Iorfino (ORCID: https://orcid.org/0000-0003-1109-0972)
- Jan L. Scott (ORCID: https://orcid.org/0000-0002-7203-8601)
- Mathew Varidel (ORCID: https://orcid.org/0000-0002-1648-8317)
- Ian Bernard Hickie (ORCID: https://orcid.org/0000-0001-8832-9895)
- Jacob J. Crouse (ORCID: https://orcid.org/0000-0002-3805-2936)
- Román Marchant (ORCID: https://orcid.org/0000-0002-0969-2352)
- Sarah McKenna (ORCID: https://orcid.org/0000-0002-8560-6399)
- Sally Cripps
- Victor An
Institutions
- University of Technology Sydney (AU)
- The University of Sydney (AU)
- Flinders University (AU)
- Adelaide Institute for Sleep Health (AU)
- Newcastle University (GB)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-06-19
- DOI
- https://doi.org/10.2196/71305
- Citations
- 3
- Primary Topic
- Mental Health Research Topics
- Type
- article
- Field-Weighted Citation Impact
- 6.35