Modeling recurrent suicide attempts using probabilistic Hawkes processes

Assessing the risk of suicide attempt recurrence requires integrating multiple clinical factors, including suicidal ideation and intent. Although clinical evaluation remains the most reliable method for estimating risk, few longitudinal mathematical models exist that leverage routine clinical data to predict recurrence dynamically. This gap limits the use of predictive analytics in suicide prevention.

We analyzed data from 1112 individuals from the MCOSUL Cohort (Lleida, Spain), who were treated for a suicide attempt, with a minimum 5-year follow-up or until death. Baseline sociodemographic and clinical variables were collected during structured assessment, and follow-up data were extracted from electronic health records. For each participant, Hawkes process parameters (μ, α, δ) were estimated using maximum likelihood and conditioned via a neural network. A Gaussian Mixture Model was applied to identify temporal risk profiles.

Recurrence showed a temporal clustering pattern: 61.1% of repeat attempts occurred within 1 month of a previous event, and nearly all within 12 months. The model captured self-exciting dynamics and generated individualized survival and intensity curves. Five clusters emerged: a large low-risk heterogeneous group; a moderate-risk group; a predominantly male group with infrequent and less severe attempts; a high-risk group with multiple previous attempts; and a small but extreme group with severe and chronic recurrences.

Suicide attempt repetition in this cohort demonstrates self-exciting temporal behavior. Hawkes-based modeling enables dynamic, time-varying risk estimation and may offer advantages over traditional static prediction tools. Prospective validation should assess clinical integration, scalability, and utility for personalized suicide prevention.