Assessing the predictive ability of the Suicide Crisis Inventory for near-term suicidal behavior using machine learning approaches

Neelang Parghi, Lakshmi Chennapragada, Shira Barzilay, Saskia Newkirk, Brian Ahmedani, Benjamin Lok, Igor Galynker

Research output: Contribution to journalArticlepeer-review


Objective: This study explores the prediction of near-term suicidal behavior using machine learning (ML) analyses of the Suicide Crisis Inventory (SCI), which measures the Suicide Crisis Syndrome, a presuicidal mental state. Methods: SCI data were collected from high-risk psychiatric inpatients (N = 591) grouped based on their short-term suicidal behavior, that is, those who attempted suicide between intake and 1-month follow-up dates (N = 20) and those who did not (N = 571). Data were analyzed using three predictive algorithms (logistic regression, random forest, and gradient boosting) and three sampling approaches (split sample, Synthetic minority oversampling technique, and enhanced bootstrap). Results: The enhanced bootstrap approach considerably outperformed the other sampling approaches, with random forest (98.0% precision; 33.9% recall; 71.0% Area under the precision-recall curve [AUPRC]; and 87.8% Area under the receiver operating characteristic [AUROC]) and gradient boosting (94.0% precision; 48.9% recall; 70.5% AUPRC; and 89.4% AUROC) algorithms performing best in predicting positive cases of near-term suicidal behavior using this dataset. Conclusions: ML can be useful in analyzing data from psychometric scales, such as the SCI, and for predicting near-term suicidal behavior. However, in cases such as the current analysis where the data are highly imbalanced, the optimal method of measuring performance must be carefully considered and selected.

Original languageEnglish
Article numbere1863
JournalInternational Journal of Methods in Psychiatric Research
Issue number1
StatePublished - Mar 2021
Externally publishedYes

Bibliographical note

Funding Information:
This study was supported by the American Foundation for Suicide Prevention (AFSP) focus grant #RFA‐1‐015‐14, by the National Institute of Mental Health grant R34 MH119294‐01, and by Richard and Cynthia Zirinsky foundation. The content is solely the responsibility of the authors and does not necessarily represent the official AFSP views. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors would like to thank Irina Kopykina for her valuable contributions, the research assistants for their efforts in data collecting and entering, and our participants. NP dedicates this paper to the memory of Faigy Mayer.

Publisher Copyright:
© 2020 The Authors. International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.


  • Imminent Risk
  • machine learning
  • risk assessment
  • suicide
  • suicide crisis syndrome

ASJC Scopus subject areas

  • Psychiatry and Mental health


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