Survival models stop at a risk score: ODDS-DR rewrites outputs as additive log-odds and sweeps a full risk trajectory in two backward passes
Clinical survival models usually emit distribution parameters, abstract quantities that do not map onto the risk factors clinicians recognize. ODDS-DR reparameterizes the network outputs so they add up in log-odds, letting each output neuron be attributed directly to input features. On the SUPPORT2 and aortic coarctation cohorts its discrimination matches or exceeds DeepSurv, DeepHit and DSM, with a C-index of 0.83 for the log-logistic variant on SUPPORT2.