Machine learning approach for the development of a crucial tool in suicide prevention: The Suicide Crisis Inventory-2 (SCI-2) Short Form

The Suicide Crisis Syndrome (SCS) describes a suicidal mental state marked by entrapment, affective disturbance, loss of cognitive control, hyperarousal, and social withdrawal that has predictive capacity for near-term suicidal behavior. The Suicide Crisis Inventory-2 (SCI-2), a reliable clinical to...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2024-05, Vol.19 (5), p.e0299048-e0299048
Main Authors: De Luca, Gabriele P, Parghi, Neelang, El Hayek, Rawad, Bloch-Elkouby, Sarah, Peterkin, Devon, Wolfe, Amber, Rogers, Megan L, Galynker, Igor
Format: Article
Language:English
Subjects:
Adolescent
Adult
Aged
Algorithms
Coronaviruses
COVID-19
Crises
Emotional behavior
Entrapment
Female
Humans
Logistic Models
Machine Learning
Male
Middle Aged
Pandemics
Performance prediction
Quantitative psychology
Statistical analysis
Suicidal behavior
Suicidal Ideation
Suicide
Suicide - psychology
Suicide Prevention
Suicides & suicide attempts
Surveys
Surveys and Questionnaires
Validity
Young Adult
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The Suicide Crisis Syndrome (SCS) describes a suicidal mental state marked by entrapment, affective disturbance, loss of cognitive control, hyperarousal, and social withdrawal that has predictive capacity for near-term suicidal behavior. The Suicide Crisis Inventory-2 (SCI-2), a reliable clinical tool that assesses SCS, lacks a short form for use in clinical settings which we sought to address with statistical analysis. To address this need, a community sample of 10,357 participants responded to an anonymous survey after which predictive performance for suicidal ideation (SI) and SI with preparatory behavior (SI-P) was measured using logistic regression, random forest, and gradient boosting algorithms. Four-fold cross-validation was used to split the dataset in 1,000 iterations. We compared rankings to the SCI-Short Form to inform the short form of the SCI-2. Logistic regression performed best in every analysis. The SI results were used to build the SCI-2-Short Form (SCI-2-SF) utilizing the two top ranking items from each SCS criterion. SHAP analysis of the SCI-2 resulted in meaningful rankings of its items. The SCI-2-SF, derived from these rankings, will be tested for predictive validity and utility in future studies.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0299048