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Highlighting psychological pain avoidance and decision‐making bias as key predictors of suicide attempt in major depressive disorder—A novel investigative approach using machine learning.

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Title: Highlighting psychological pain avoidance and decision‐making bias as key predictors of suicide attempt in major depressive disorder—A novel investigative approach using machine learning.
Authors: Ji, Xinlei (AUTHOR), Zhao, Jiahui (AUTHOR), Fan, Lejia (AUTHOR), Li, Huanhuan (AUTHOR), Lin, Pan (AUTHOR), Zhang, Panwen (AUTHOR), Fang, Shulin (AUTHOR), Law, Samuel (AUTHOR), Yao, Shuqiao (AUTHOR), Wang, Xiang (AUTHOR)
Source: Journal of Clinical Psychology. Apr2022, Vol. 78 Issue 4, p671-691. 21p. 1 Diagram, 3 Charts, 2 Graphs.
Subjects: Attempted suicide, Mental depression, Suicide risk factors, Machine learning, Psychometrics, Suicidal ideation
Abstract: Objective: Predicting suicide is notoriously difficult and complex, but a serious public health issue. An innovative approach utilizing machine learning (ML) that incorporates features of psychological mechanisms and decision‐making characteristics related to suicidality could create an improved model for identifying suicide risk in patients with major depressive disorder (MDD). Method: Forty‐four patients with MDD and past suicide attempts (MDD_SA, N = 44); 48 patients with MDD but without past suicide attempts (MDD_NS, N = 48–42 of whom with suicide ideation [MDD_SI, N = 42]), and healthy controls (HCs, N = 51) completed seven psychometric assessments including the Three‐dimensional Psychological Pain Scale (TDPPS), and one behavioral assessment, the Balloon Analogue Risk Task (BART). Descriptive statistics, group comparisons, logistic regressions, and ML were used to explore and compare the groups and generate predictors of suicidal acts. Results: MDD_SA and MDD_NS differed in TDPPS total score, pain arousal and avoidance subscale scores, suicidal ideation scores, and relevant decision‐making indicators in BART. Logistic regression tests linked suicide attempts to psychological pain avoidance and a risk decision‐making indicator. The resultant key ML model distinguished MDD_SA/MDD_NS with 88.2% accuracy. The model could also distinguish MDD_SA/MDD_SI with 81.25% accuracy. The ML model using hopelessness could classify MDD_SI/HC with 94.4% accuracy. Conclusion: ML analyses showed that motivation to avoid intolerable psychological pain, coupled with impaired decision‐making bias toward under‐valuing life's worth are highly predictive of suicide attempts. Analyses also demonstrated that suicidal ideation and attempts differed in potential mechanisms, as suicidal ideation was more related to hopelessness. ML algorithms show useful promises as a predictive instrument. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Clinical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Highlighting psychological pain avoidance and decision‐making bias as key predictors of suicide attempt in major depressive disorder—A novel investigative approach using machine learning.
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  Data: <searchLink fieldCode="AR" term="%22Ji%2C+Xinlei%22">Ji, Xinlei</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Jiahui%22">Zhao, Jiahui</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fan%2C+Lejia%22">Fan, Lejia</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Huanhuan%22">Li, Huanhuan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Pan%22">Lin, Pan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Panwen%22">Zhang, Panwen</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fang%2C+Shulin%22">Fang, Shulin</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Law%2C+Samuel%22">Law, Samuel</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yao%2C+Shuqiao%22">Yao, Shuqiao</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Xiang%22">Wang, Xiang</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Clinical+Psychology%22">Journal of Clinical Psychology</searchLink>. Apr2022, Vol. 78 Issue 4, p671-691. 21p. 1 Diagram, 3 Charts, 2 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Attempted+suicide%22">Attempted suicide</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+depression%22">Mental depression</searchLink><br /><searchLink fieldCode="DE" term="%22Suicide+risk+factors%22">Suicide risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Psychometrics%22">Psychometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Suicidal+ideation%22">Suicidal ideation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: Predicting suicide is notoriously difficult and complex, but a serious public health issue. An innovative approach utilizing machine learning (ML) that incorporates features of psychological mechanisms and decision‐making characteristics related to suicidality could create an improved model for identifying suicide risk in patients with major depressive disorder (MDD). Method: Forty‐four patients with MDD and past suicide attempts (MDD_SA, N = 44); 48 patients with MDD but without past suicide attempts (MDD_NS, N = 48–42 of whom with suicide ideation [MDD_SI, N = 42]), and healthy controls (HCs, N = 51) completed seven psychometric assessments including the Three‐dimensional Psychological Pain Scale (TDPPS), and one behavioral assessment, the Balloon Analogue Risk Task (BART). Descriptive statistics, group comparisons, logistic regressions, and ML were used to explore and compare the groups and generate predictors of suicidal acts. Results: MDD_SA and MDD_NS differed in TDPPS total score, pain arousal and avoidance subscale scores, suicidal ideation scores, and relevant decision‐making indicators in BART. Logistic regression tests linked suicide attempts to psychological pain avoidance and a risk decision‐making indicator. The resultant key ML model distinguished MDD_SA/MDD_NS with 88.2% accuracy. The model could also distinguish MDD_SA/MDD_SI with 81.25% accuracy. The ML model using hopelessness could classify MDD_SI/HC with 94.4% accuracy. Conclusion: ML analyses showed that motivation to avoid intolerable psychological pain, coupled with impaired decision‐making bias toward under‐valuing life's worth are highly predictive of suicide attempts. Analyses also demonstrated that suicidal ideation and attempts differed in potential mechanisms, as suicidal ideation was more related to hopelessness. ML algorithms show useful promises as a predictive instrument. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Clinical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/jclp.23246
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        Text: English
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      Pagination:
        PageCount: 21
        StartPage: 671
    Subjects:
      – SubjectFull: Attempted suicide
        Type: general
      – SubjectFull: Mental depression
        Type: general
      – SubjectFull: Suicide risk factors
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Psychometrics
        Type: general
      – SubjectFull: Suicidal ideation
        Type: general
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      – TitleFull: Highlighting psychological pain avoidance and decision‐making bias as key predictors of suicide attempt in major depressive disorder—A novel investigative approach using machine learning.
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              M: 04
              Text: Apr2022
              Type: published
              Y: 2022
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