Improving Forecasts of Suicide Attempts for Patients with Little Data
This research addresses difficulty in predicting suicide attempts due to patient differences and limited data. Instead of one general model or many overfitted individual models, Latent Similarity Gaussian Processes (LSGPs) are introduced. This model places patients in a latent space where distance represents similarity, allowing patients with little data to use trends from similar individuals for better forecasts.