Time Survival

Time survival analysis focuses on predicting the time until a specific event occurs, a crucial task across diverse fields like medicine and finance. Current research emphasizes improving the accuracy and robustness of survival models, particularly using deep learning architectures like recurrent neural networks and novel loss functions that address challenges like censoring (incomplete data) and data heterogeneity. These advancements aim to enhance predictive performance, calibration (accurate probability estimates), and fairness across different subpopulations, leading to more reliable and impactful applications in areas such as personalized medicine and risk assessment.