Predicting Next‐Day Passive Suicidal Ideation in At‐Risk Youth

INTRODUCTION: Passive suicidal ideation (SI) is a well-established risk factor for suicidal behavior but has received less attention than active SI. Although recent work has leveraged intensive longitudinal data and machine learning (ML) to forecast short-term risk for active SI, passive SI remains understudied as a prediction target. METHODS: Seventy-eight psychiatrically hospitalized youth (ages 13-17 years) completed baseline assessments and daily ratings of risk and protective factors for 28 days post-discharge. Multiple ML models were trained to predict the presence of next-day passive SI. Models with and without baseline variables were compared to assess the relative predictive value of time-varying versus baseline features. RESULTS: ML models predicted next-day passive SI with high accuracy (AUC = 0.90). The strongest predictors were within-person 7-day moving averages of passive SI duration and frequency. Including baseline variables had negligible performance impact, even during initial days post-discharge. CONCLUSIONS: Short-term passive SI remains an underutilized but important target for suicide prevention. Forecasting next-day passive SI using ML is feasible and highly accurate. Within-person, time-varying features outperformed baseline factors, even in early days post-discharge. Additional research on SI facets, such as duration, is needed. Integrating passive SI into personalized intervention frameworks may enhance the precision of suicide prevention efforts.