Predicting Suicidal Behavior Without Asking About Suicidal Ideation: Machine Learning and the Role of Borderline Personality Disorder Criteria.
Objective: Identifying predictors contributing to suicide risk could help prevent suicides via targeted interventions. However, using only known risk factors may not yield accurate enough results. Furthermore, risk models typically rely on suicidal ideation, even though people often withhold this in...
| Publicado en: | Suicide & Life-Threatening Behavior Vol. 51; no. 3; pp. 455 - 467 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Wiley-Blackwell
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=151251459&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 151251459 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03630234 SUI jtl: Suicide & Life-Threatening Behavior issn: 03630234 maglogo: Y pubinfo: dt: Jun2021 vid: 51 iid: 3 pid: 480 pub: Wiley-Blackwell artinfo: ui: 151251459 10.1111/sltb.12719 ppf: 455 ppct: 12 formats: tig: atl: Predicting Suicidal Behavior Without Asking About Suicidal Ideation: Machine Learning and the Role of Borderline Personality Disorder Criteria. aug: au: Horvath, Adam Dras, Mark Lai, Catie C.W. Boag, Simon affil: Department of Psychology, Macquarie University, Sydney NSW,, Australia Department of Computing, Macquarie University, Sydney NSW,, Australia su: Suicidal ideation Suicidal behavior Borderline personality disorder Suicide risk factors Machine learning sug: subj: Suicidal ideation Suicidal behavior Borderline personality disorder Suicide risk factors Machine learning keyword: borderline personality disorder bpd classification machine learning prediction suicide prevention tree boosting borderline personality disorder bpd classification machine learning prediction suicide prevention tree boosting ab: Objective: Identifying predictors contributing to suicide risk could help prevent suicides via targeted interventions. However, using only known risk factors may not yield accurate enough results. Furthermore, risk models typically rely on suicidal ideation, even though people often withhold this information. Method: This study examined the contribution of various predictors to the accuracy of six machine learning models for identifying suicidal behavior in a prison population (n = 353), including borderline personality disorder (BPD) and antisocial personality disorder (APD) criteria, and compared how excluding data about suicidal ideation affects accuracy. Results: Results revealed that gradient tree boosting accurately identified individuals with suicidal behavior, even without relying on questions about suicidal ideation (AUC = 0.875, F1 = 0.846). Furthermore, the model maintained this accuracy with only 29 predictors. Meeting five or more diagnostic criteria of BPD was an important risk factor for suicidal behavior. APD criteria, in the presence of other predictors, did not substantially improve accuracy. Additionally, it may be possible to implement a decision tree model to assess individuals at risk of suicide, without focusing upon suicidal ideation. Conclusions: These findings highlight that modern classification algorithms do not necessarily require information about suicidal ideation for modeling suicide and self‐harm behavior. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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