A Machine Learning approach to optimize the assessment of depressive symptomatology-Reference-Cited by-同舟云学术

A Machine Learning approach to optimize the assessment of depressive symptomatology

Published:2022 Issue: Volume:206 Page:111-120
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Eduardo Maekawa,Darragh Glavin,Eoin Martino Grua,Carina Akemi Nakamura,Marcia Scazufca,Ricardo Araya,J Peters Tim,Pepijn van de Ven

Funder

Wellcome Trust

Medical Research Council

Department for International Development, UK Government

Science Foundation Ireland

Fundação de Amparo à Pesquisa do Estado de São Paulo

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Elsevier BV

Subject

General Engineering

Reference20 articles.

1. Pilot study of a two-arm non-randomized controlled cluster trial of a psychosocial intervention to improve late life depression in socioeconomically deprived areas of São Paulo, Brazil (PROACTIVE): feasibility study of a psychosocial intervention for late life depression in São Paulo

2. Instituto Brasileiro de Geografia e Estatística. Projeções e estimativas da população do Brasil e das Unidades da Federação. Available from: https://www.ibge.gov.br/apps/populacao/projecao/. Accessed 29 May 2022.

3. Prevalence of self-reported depression in Brazil: 2013 National Health Survey results;Stopa;Rev Bras Epidemiol,2015

4. A collaborative care psychosocial intervention to improve late life depression in socioeconomically deprived areas of Guarulhos, Brazil: the PROACTIVE cluster randomised controlled trial protocol;Scazufca;Trials,2020

5. Identification and treatment of depression of older adults in primary care: findings from the São Paulo Ageing and Health Study;Scazufca;Family practice,2016

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1. Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation;JMIR Mental Health;2024-07-04

2. Bayesian networks for feature selection and patient screening for depressive symptomatology: Practical applications of machine learning models (Preprint);2023-08-21