Deep Survival Model

Deep survival models leverage deep learning to predict time-to-event outcomes, addressing limitations of traditional survival analysis methods in handling complex, high-dimensional data. Current research emphasizes developing interpretable models, such as those based on ReLU networks, and exploring various architectures including recurrent neural networks, transformers, and variational autoencoders to improve prediction accuracy and handle diverse data types (e.g., medical images, time series). These advancements are significant for diverse fields, improving prognostication in healthcare (e.g., cancer survival, patient risk stratification), enhancing customer churn prediction in business, and generally advancing the capabilities of time-to-event modeling.