Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study.

ABSTRACT:

Background

Vascular depression (VaDep) is a prevalent affective disorder in older adults that significantly impacts functional status and quality of life. Early identification and intervention are crucial but largely insufficient in clinical practice due to inconspicuous depressive symptoms mostly, heterogeneous imaging manifestations, and the lack of definitive peripheral biomarkers. This study aimed to develop and validate an interpretable machine learning (ML) model for VaDep to serve as a clinical support tool.

Methods

This study included 602 participants from Wuhan in China divided into 236 VaDep patients and 366 controls for training and internal validation from July 2020 to October 2023. An independent dataset of 171 participants from surrounding areas was used for ex

SUBMITTER: Zhang R 

PROVIDER: S-EPMC12315376 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature