Deep Survival

Deep survival analysis focuses on predicting the time until a specific event occurs, particularly in scenarios with censored data (where the event isn't observed for all subjects). Current research emphasizes the development and application of machine learning models, including neural networks (like DeepSurv and Survival MDNs), survival random forests, and support vector machines, to improve prediction accuracy and handle complex, high-dimensional data. These advancements are significant for various fields, from healthcare (predicting patient survival and treatment response) to engineering (predicting equipment failure) and marketing (predicting customer purchase timing), enabling more informed decision-making and resource allocation. The development of user-friendly tools and open-source packages further facilitates broader adoption and collaboration within the field.