Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach.

Journal: Journal of affective disorders
PMID: 30419527

Abstract

BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Predominant polarity (PP) appears to be an important specifier of BD. The present study employed machine learning (ML) algorithms to accurately determine a patient´s PP without the inclusion of number and polarity of past episodes, while exploring associations between PP and demographic and clinical variables.

Authors

  • Gabriel Okawa Belizario
    Bipolar Disorder Research Program (PROMAN), Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, Rua Dr. Ovidio Pires de Campos, 785 - 3 andar / Ala norte / Ceapesq / Sala 4, 05403-010 São Paulo, Brazil. Electronic address: gabrielokawabelizario@gmail.com.
  • Renato Gomes Borges Junior
    Instituto de Informática, Universidade Federal de Goiás (INF/UFG), Brazil.
  • Rogerio Salvini
    Instituto de Informática, Universidade Federal de Goiás, Goiânia, GO, Brazil.
  • Beny Lafer
    Bipolar Research Program, Department and Institute of Psychiatry, University of São Paulo Medical School, São Paulo, Brazil.
  • Rodrigo da Silva Dias
    Bipolar Disorder Research Program (PROMAN), Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, Rua Dr. Ovidio Pires de Campos, 785 - 3 andar / Ala norte / Ceapesq / Sala 4, 05403-010 São Paulo, Brazil.

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