DySurv: Dynamic Deep Learning Model for Survival Prediction in the ICU

arXiv:2310.18681v2 Announce Type: replace
Abstract: Survival analysis focuses on estimating time-to-event distributions which can help in dynamic risk prediction in healthcare. Extending beyond the classical Cox model, deep learning techniques have been developed which moved away from the constraining assumptions of proportional hazards. Traditional statistical models often only include static information where, in this work, we propose a novel conditional variational autoencoder-based method called DySurv, which uses a combination of static and time-series measurements from patient electronic health records to …

abstract analysis arxiv assumptions beyond cs.lg deep learning deep learning techniques dynamic event hazards healthcare prediction risk statistical survival type