Depression diagnosis from patient interviews using multimodal machine learning.

ABSTRACT:

Background

Depression is a major public health concern, affecting an estimated five percent of the global population. Early and accurate diagnosis is essential to initiate effective treatment, yet recognition remains challenging in many clinical contexts. Speech, language, and behavioral cues collected during patient interviews may provide objective markers that support clinical assessment.

Methods

We developed a diagnostic approach that integrates features derived from patient interviews, including speech patterns, linguistic characteristics, and structured clinical information. Separate models were trained for each modality and subsequently combined through multimodal fusion to reflect the complexity of real-world psychiatric assessment. Model validity was assessed with es

SUBMITTER: Weber J 

PROVIDER: S-EPMC12696486 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature