Survival Model

Survival analysis models the time until a specific event occurs, aiming to predict survival probabilities and identify risk factors. Current research emphasizes developing more accurate and interpretable models, focusing on techniques like gradient boosting, transformer networks, and Kolmogorov-Arnold networks, often addressing challenges like censoring and competing risks. These advancements are improving risk prediction across diverse fields, from healthcare (e.g., predicting patient survival or hospital readmission) to engineering (e.g., predicting equipment failure), leading to better decision-making and resource allocation. A significant focus is on enhancing model calibration and interpretability to increase trust and clinical utility, particularly in high-stakes applications.