DeepSurv Heart Failure (HF) Prognostic Model

DeepSurv HF Prognostic Model — Explanation and Clinical Context DeepSurv is a Cox-proportional-hazards deep neural network that learns a nonlinear risk function from patient covariates and outputs individualized survival/hazard estimates. Applied to heart failure (HF), DeepSurv-based models can integrate many continuous and interacting predictors (clinical variables, biomarkers, and high-resolution signals) and may improve discrimination and personalization versus traditional Cox models. However, DeepSurv requires a trained model (weights) and an inference runtime; it cannot be computed from a formula in PHP alone.

The MAGGIC risk score is a validated, simple clinical score for 1- and 3-year mortality in HF and is provided here as an approximate fallback when a deployed DeepSurv service is not available.