Survival Analysis

Survival analysis models the time until an event occurs, such as death or equipment failure, often in the presence of censored data where the event time is unknown. Current research emphasizes improving prediction accuracy and interpretability through advanced model architectures, including neural networks (e.g., incorporating piecewise hazard functions, longitudinal data via recurrent networks, and multimodal data fusion), and novel loss functions (e.g., contrastive learning, quantile regression). These advancements are significantly impacting diverse fields, from healthcare (e.g., cancer prognosis, risk stratification) to engineering (e.g., predictive maintenance), by enabling more accurate and insightful predictions of time-to-event.