Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.

Journal: BMC psychiatry
PMID: 32228548

Abstract

BACKGROUND: This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated with suicide risk. A Bayesian network (BN) was developed and applied to establish conditional dependence relationships among variables for each individual subject studied. These conditional dependencies represented the different states that patients could experience in relation to suicidal behavior (SB). The clinical sample included 650 mental health patients with mood and anxiety symptomatology.

Authors

  • Jorge Barros
    Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, La Reconquista 498, Las Condes, Santiago, Chile.
  • Susana Morales
    Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, La Reconquista 498, Las Condes, Santiago, Chile. sus.mosi@gmail.com.
  • Arnol García
    Independent mathematical engineer, Santiago, Chile.
  • Orietta Echávarri
    Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, La Reconquista 498, Las Condes, Santiago, Chile.
  • Ronit Fischman
    Millennium Institute for Research in Depression and Personality MIDAP, Santiago, Chile.
  • Marta Szmulewicz
    Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, La Reconquista 498, Las Condes, Santiago, Chile.
  • Claudia Moya
    Millennium Institute for Research in Depression and Personality MIDAP, Santiago, Chile.
  • Catalina Núñez
    Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, La Reconquista 498, Las Condes, Santiago, Chile.
  • Alemka Tomicic
    Millennium Institute for Research in Depression and Personality MIDAP, Santiago, Chile.

Keywords

External Resources

Popular Topics
Recent Journals