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Stud Health Technol Inform
; 310: 1442-1443, 2024 Jan 25.
Article
in English
| MEDLINE
| ID: mdl-38269687
ABSTRACT
Digital tools for mental health show great promise, but concerns arise when they fail to recognize the user state. We train a classifier to detect the emotional context of dialogs among 6 categories, achieving 78% accuracy on top choice. Importantly greatest areas of confusion (excited-hopeful, angry-sad) are not of the most unsafe kind. Such a classifier could serve as a resource to the dialog managers of future digital mental health agents.