@article{ref1,
title="Predicting 3-year persistent or recurrent major depressive episode using machine learning techniques",
journal="Psychiatry research communications",
year="2022",
author="Fialho, Amanda Rodrigues and Montezano, Bruno Braga and Ballester, Pedro Lemos and de Azevedo Cardoso, Taiane and Mondin, Thaíse Campos and Moreira, Fernanda Pedrotti and Souza, Luciano Dias de Mattos and da Silva, Ricardo Azevedo and Jansen, Karen",
volume="2",
number="3",
pages="e100055-e100055",
abstract="Background The identification of predictors of recurrence and persistence of depressive episodes in major depressive disorder (MDD) can be important to inform clinicians and collaborate to clinical decisions.  Objective The aim of the present study is to predict recurrent or persistent depressive episodes, in addition to predicting severe recurrent or persistent depressive episodes using a machine learning method.  Methods This is a prospective cohort study with three years of follow-up. Individuals diagnosed with MDD in the first phase of the study (2012-2015) were evaluated in the second phase (2012-2015). The sociodemographic, clinical, comorbid disorders and substance use variables were used as predictors in all predictive models. Initially, the first model predicted recurrence/persistence, including subjects of any severity of depression level. The second model predicted recurrence/persistence depression as the first model, although it was trained with severely depressed subjects and those without indicative for depression. The third model predicted severe depression among depressed patients.  Results Area under the curve (AUC) values ranged from 0.65 to 0.81, and accuracies ranged from 62% to 71%. Psychiatric comorbidities, substance abuse/dependence, and family medical history were important features in all three models.  Limitation The time between baseline and the second phase of the study was approximately three years, making it difficult to detect depressive symptoms during this time frame. Also, age at depression onset and number of episodes were not included in the model due to the large number of missing data.  Conclusions In conclusion, this study adds new information that can help health professionals both in their clinical practice and in public services.<p /> <p>Language: en</p>",
language="en",
issn="2772-5987",
doi="10.1016/j.psycom.2022.100055",
url="http://dx.doi.org/10.1016/j.psycom.2022.100055"
}