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

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dc.contributor.authorBarros Beck, Jorge Alejandro
dc.contributor.authorMorales Silva, Susana
dc.contributor.authorEchavárri Vesperinas, María Orietta
dc.contributor.authorSzmulewicz Espinosa, Marta Adelina
dc.contributor.authorNúñez, Catalina
dc.contributor.authorGarcía, Arnol
dc.contributor.authorFischman, Ronit
dc.contributor.authorMoya Dabed, Claudia Andrea
dc.contributor.authorTomicic S., Alemka
dc.date.accessioned2020-04-07T16:47:31Z
dc.date.available2020-04-07T16:47:31Z
dc.date.issued2020
dc.date.updated2020-04-05T00:03:14Z
dc.description.abstractAbstract 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. Results Mainly indicated that variables within the Bayesian network are part of each patient’s state of psychological vulnerability and have the potential to impact such states and that these variables coexist and are relatively stable over time. These results have enabled us to offer a tool to detect states of psychological vulnerability associated with suicide risk. Conclusion If we accept that suicidal behaviors (vulnerability, ideation, and suicidal attempts) exist in constant change and are unstable, we can investigate what individuals experience at specific moments to become better able to intervene in a timely manner to prevent such behaviors. Future testing of the tool developed in this study is needed, not only in specialized mental health environments but also in other environments with high rates of mental illness, such as primary healthcare facilities and educational institutions.
dc.identifier.citationBMC Psychiatry. 2020 Mar 30;20(1):138
dc.identifier.doi10.1186/s12888-020-02535-x
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/28653
dc.issue.numeroNo. 138
dc.language.isoen
dc.pagina.final20
dc.pagina.inicio1
dc.revistaBMC Psychiatryes_ES
dc.rightsacceso abierto
dc.rights.holderThe Author(s)
dc.subjectSuicidees_ES
dc.subjectMood disorderses_ES
dc.subjectArtificial intelligencees_ES
dc.subjectBayesian modelses_ES
dc.subject.ddc616.858445
dc.subject.deweyMedicina y saludes_ES
dc.titleRecognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical samplees_ES
dc.typeartículo
dc.volumenVol. 20
sipa.codpersvinculados99134
sipa.codpersvinculados124553
sipa.codpersvinculados371
sipa.codpersvinculados59045
sipa.codpersvinculados193513
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