PROSODIC: Predictive Intelligence for Secondary Prevention of Youth Suicide

Suicide is currently the leading cause of preventable death among young people, yet early identification of reattempt risk remains limited by its multifactorial nature, stigma, fragmented data, reliance on subjective assessments, and heavy workloads. The PROSODIC project developed an interpretable machine learning model to predict first suicide reattempts among youths, using routinely collected clinical and sociodemographic data from 1,031 patients in Seville, Spain, aged 5-25 years. Logistic Regression achieved the best performance (AUC-ROC = 0.65), balancing accuracy and interpretability. A three-level graded risk scale was derived to guide clinical action. Results highlight social vulnerability, behavioral dysregulation, and care discontinuity as key predictors, supporting the model's clinical relevance and feasibility for integration into real-world suicide secondary prevention pathways. Limitations include the use of single-site data and moderate discriminative performance; however, future work will extend validation across additional regions to enhance generalizability and robustness.