Deep learning for detecting depression in individuals with and without alexithymia.

ABSTRACT:

Background

To accurately detect individuals' mental health issues using artificial intelligence and self-report scales, it is crucial to recognize how personal characteristics can affect the detection. This study focuses on the role of alexithymia-a condition where individuals struggle to recognize and articulate emotions and symptoms-in the detection of depression. We aimed to determine whether deep learning models could enhance the accuracy of depression detection in people with alexithymia compared to self-report scales.

Methods

We analyzed data from 194 patients with major depressive disorder and 105 community controls, employing eight large language models (LLMs) trained on transcript text from clinician-administered structured interviews using the Hamilton Depression R

SUBMITTER: Lam C 

PROVIDER: S-EPMC12917275 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature