@article{ref1,
title="Machine learning approach for the development of a crucial tool in suicide prevention: the Suicide Crisis Inventory-2 (SCI-2) Short Form",
journal="PLoS one",
year="2024",
author="De Luca, Gabriele P. and Parghi, Neelang and El Hayek, Rawad and Bloch-Elkouby, Sarah and Peterkin, Devon and Wolfe, Amber and Rogers, Megan L. and Galynker, Igor",
volume="19",
number="5",
pages="e0299048-e0299048",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="1932-6203",
doi="10.1371/journal.pone.0299048",
url="http://dx.doi.org/10.1371/journal.pone.0299048"
}