Choosing the right treatment - combining clinicians’ expert knowledge with data-driven predictions
In 90.1% of cases, the hybrid model ranked the actual disorder treated as either the highest (67.3%) or second-highest (22.8%) on the test data. This emphasizes that instead of suggesting a single disorder to be treated, the model can offer the probabilities for multiple disorders. This allows individuals seeking treatment or their therapists to incorporate this information as an additional data-driven factor when collectively deciding on which treatment to prioritize.
Authors
- Eduardo Maekawa (ORCID: https://orcid.org/0009-0006-6657-9678)
- Pepijn W.J. van de Ven (ORCID: https://orcid.org/0000-0003-3321-450X)
- Esben Skov Jensen (ORCID: https://orcid.org/0000-0002-9689-3776)
- Kim Mathiasen (ORCID: https://orcid.org/0000-0001-6067-8866)
Institutions
- University of Southern Denmark (DK)
- University of Limerick (IE)
- Mental Health Services (DK)
- Region of Southern Denmark (DK)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2024-09-03
- DOI
- https://doi.org/10.3389/fpsyt.2024.1422587
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- Jascha Fonden
- Science Foundation Ireland