20.500.12556/RUL-181423 Indirect, machine learning-based suicide risk screening evidence from cross-national validation Background: Suicide is a major public health challenge requiring early detection of suicidal ideation (SI). Traditional direct questioning methods suffer from stigma and disclosure bias, failing to identify many at-risk individuals. While machine learning (ML) models show promise, most lack external validation. Indirect screening, using psychosocial data rather than direct SI questions, offers a scalable alternative. This study aimed to externally validate an indirect, ML-based SI screening tool. We tested if a model trained on a Slovenian general population sample retained predictive accuracy when applied to an independent Croatian sample during a period of societal stress (pandemic and earthquakes), assessing performance across age and gender subgroups. Methods: A logistic regression model was trained on a Slovenian sample (N = 2,989) and validated on a Croatian sample (N = 2,364). The model used only indirect predictors, including sociodemographics, life satisfaction, behavioral changes, and Brief COPE subscales. The target outcome was the presence of SI (SIDAS score > 0). Performance was measured by the area under the receiver operating characteristic curve (AUROC). Results: The model demonstrated strong external validity on the entire Croatian sample, achieving an AUROC of 0.80. Performance remained robust across subgroups: males (AUROC = 0.83), females (AUROC = 0.79), younger adults (AUROC = 0.77), and older adults (AUROC = 0.81). Self-blame, behavioral disengagement, and relationship dissatisfaction were key predictors. Conclusions: An indirect, ML-based screening tool can reliably identify SI risk in the general population. The model demonstrated strong cross-national transferability and resilience during a societal crisis, proving it is a feasible and valid strategy for population-level prevention. machine learning mass screening risk assessment suicidal ideation strojno učenje množično presejanje ocena tveganja samomorilne misli true false true Angleški jezik Slovenski jezik Članek v reviji 2026-04-07 10:32:24 2026-04-07 10:32:33 2026-07-15 08:51:36 0000-00-00 00:00:00 2026 0 0 8 str. iss. 1, art. e31 Vol. 69 Feb. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 616.89 1778-3585 10.1192/j.eurpsy.2026.10166 271787011 RAZ_Rus_Prelog_Polona_2026.pdf RAZ_Rus_Prelog_Polona_2026.pdf 1 059984F26E161D56568B4E0B140466F8 2db7a4f2e43059d58fbbf3efee1a878d4b714499ea5da263d0e183ee70426180 aeda8bf6-325c-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=231847 https://www.cambridge.org/core/journals/european-psychiatry/article/indirect-machine-learningbased-suicide-risk-screening-evidence-from-crossnational-validation/4F54185E0B346D48ABC3998B0CE22BC9 1 2467098d-325c-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=231846 Medicinska fakulteta 0 0 0