IDNetwork: A deep Illness-Death Network based on multi-states event history process for versatile disease prognostication
Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, PMLR 146:1-21, 2021.
Multi-state models can capture the different patterns of disease evolution. In particular, the illness-death model is used to follow disease progression from a healthy state to an intermediate state and to a death-related final state. We aim to use those models in order to adapt treatment decisions according to the evolution of the disease. In state-of-the-art methods, the risks of transition are modeled via (semi-) Markov processes and transition-specific Cox proportional hazard (P.H.) models. We propose a neural network architecture called IDNetwork (Illness-Death Network) that relaxes the linear Cox P.H. assumption and integrates a large number of patients’ characteristics. Our method significantly improves the predictive performance compared to state-of-the-art methods on a simulated data set, on two clinical trials for patients with colon cancer and on a real-world data set in breast cancer.