Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample
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 eac...
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| Format: | Article |
| Language: | English |
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BMC
2020-03-01
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| Series: | BMC Psychiatry |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12888-020-02535-x |
| _version_ | 1818159463420395520 |
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| author | Jorge Barros Susana Morales Arnol García Orietta Echávarri Ronit Fischman Marta Szmulewicz Claudia Moya Catalina Núñez Alemka Tomicic |
| author_facet | Jorge Barros Susana Morales Arnol García Orietta Echávarri Ronit Fischman Marta Szmulewicz Claudia Moya Catalina Núñez Alemka Tomicic |
| author_sort | Jorge Barros |
| collection | DOAJ |
| description | 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. 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. |
| first_indexed | 2024-12-11T15:46:23Z |
| format | Article |
| id | doaj.art-19708bb55e3446229abc22f0f5e10b57 |
| institution | Directory Open Access Journal |
| issn | 1471-244X |
| language | English |
| last_indexed | 2024-12-11T15:46:23Z |
| publishDate | 2020-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychiatry |
| spelling | doaj.art-19708bb55e3446229abc22f0f5e10b572022-12-22T00:59:40ZengBMCBMC Psychiatry1471-244X2020-03-0120112010.1186/s12888-020-02535-xRecognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sampleJorge Barros0Susana Morales1Arnol García2Orietta Echávarri3Ronit Fischman4Marta Szmulewicz5Claudia Moya6Catalina Núñez7Alemka Tomicic8Psychiatry Department, School of Medicine, Pontificia Universidad Católica de ChilePsychiatry Department, School of Medicine, Pontificia Universidad Católica de ChileIndependent mathematical engineerPsychiatry Department, School of Medicine, Pontificia Universidad Católica de ChileMillennium Institute for Research in Depression and Personality MIDAPPsychiatry Department, School of Medicine, Pontificia Universidad Católica de ChileMillennium Institute for Research in Depression and Personality MIDAPPsychiatry Department, School of Medicine, Pontificia Universidad Católica de ChileMillennium Institute for Research in Depression and Personality MIDAPAbstract 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.http://link.springer.com/article/10.1186/s12888-020-02535-xSuicideMood disordersArtificial intelligence, Bayesian models |
| spellingShingle | Jorge Barros Susana Morales Arnol García Orietta Echávarri Ronit Fischman Marta Szmulewicz Claudia Moya Catalina Núñez Alemka Tomicic Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample BMC Psychiatry Suicide Mood disorders Artificial intelligence, Bayesian models |
| title | Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample |
| title_full | Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample |
| title_fullStr | Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample |
| title_full_unstemmed | Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample |
| title_short | Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample |
| title_sort | recognizing states of psychological vulnerability to suicidal behavior a bayesian network of artificial intelligence applied to a clinical sample |
| topic | Suicide Mood disorders Artificial intelligence, Bayesian models |
| url | http://link.springer.com/article/10.1186/s12888-020-02535-x |
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