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December 11, 2025

A machine learning approach for predicting suicidal ideation among family members of persons with disabilities: a cross-sectional study in the Republic of Korea.

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Key Points

To predict suicidal ideation among family members of individuals with disabilities using machine learning.
Utilized a machine learning approach
Conducted a cross-sectional study
Focused on family members of persons with disabilities
Analyzed data based on disability onset type
Identified differing predictive factors based on disability onset type
Established a need for tailored intervention approaches
Provided evidence for targeted suicide prevention strategies

Implication

Cross-sectional study shows predictive factors for suicidal ideation in disabilities, indicating tailored suicide prevention strategies.

Abstract

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Cite This Study

Jin Hyuk Lee (2025) studied this question.

synapsesocial.com/papers/69401b262d562116f28f783a https://doi.org/10.24171/j.phrp.2025.0261

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Constructing prediction models and analyzing factors in suicidal ideation using machine learning, focusing on the older population2024 · 6 citations
2Comparison of Machine Learning Models for Predicting Suicide Attempts among Korean Adolescents with Suicidal Ideation: Secondary Data Analysis Based on 20th Korea Youth Risk Behavior Survey2025 · 1 citations
3Machine learning prediction of depression in culturally diverse families: Findings from the Korea Community Health Survey2025 · 1 citations
4Machine Learning–Based Prediction of Suicidal Thinking in Adolescents by Derivation and Validation in 3 Independent Worldwide Cohorts: Algorithm Development and Validation Study2024 · 32 citations
5Classifying Suicide Attempts from Suicidal Ideation among Adolescents using Machine Learning2025

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