Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation.

ABSTRACT:

Background

Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area; however, studies often fall short in demonstrating the practical benefits of using these models and fail to provide tangible real-world applications.

Objective

This study aims to establish a novel methodology for identifying individuals likely to exhibit DS, identify the most influential features in a more explainable way via probabilistic measures, and propose tools that can be used in real-world applications.

Methods

The study used 3 data sets: PROACTIVE, the Brazilian National Health Survey (Pesquisa Nacional de Saúde [PNS]) 2013, and PNS 2019, comprising s

SUBMITTER: Maekawa E 

PROVIDER: S-EPMC11258528 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature