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Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.

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  • معلومة اضافية
    • المصدر:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • الموضوع:
      Artificial Intelligence* ; Suicide*; Humans ; Machine Learning ; Anger ; Risk Assessment
    • نبذة مختصرة :
      This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the common challenge of limited datasets in health-related Machine Learning (ML) applications, we use data augmentation in tandem with ML to enhance the identification of individuals at high risk of suicide. We use SHapley Additive exPlanations (SHAP) for XAI and traditional correlation analysis to rank feature importance, pinpointing primary factors influencing suicide risk and preventive measures. Experimental results show the Random Forest (RF) model is excelling in accuracy, F1 score, and AUC (>97% across metrics). According to SHAP, anger issues, depression, and social isolation emerge as top predictors of suicide risk, while individuals with high incomes, esteemed professions, and higher education present the lowest risk. Our findings underscore the effectiveness of ML and XAI in suicide risk assessment, offering valuable insights for psychiatrists and facilitating informed clinical decisions.
      (© 2024. Crown.)
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    • الموضوع:
      Date Created: 20240315 Date Completed: 20240318 Latest Revision: 20240318
    • الموضوع:
      20240318
    • الرقم المعرف:
      PMC10940617
    • الرقم المعرف:
      10.1038/s41598-024-53426-0
    • الرقم المعرف:
      38485985