Survival Regression

Survival regression aims to predict the time until a specific event occurs, accounting for the common issue of censored data where the event isn't observed for all individuals. Current research emphasizes developing robust methods that handle complex data structures, such as incorporating temporal dynamics using transformers and addressing heterogeneity through hypergraph representations, often within deep learning frameworks like neural networks and variational autoencoders. These advancements improve prediction accuracy and allow for more nuanced analyses of survival data across diverse fields, including healthcare, engineering, and economics, leading to better risk stratification and more informed decision-making.