EB-VAE combines tumor growth and dropout data for personalized treatment response modeling.
problem Challenges in integrating longitudinal tumor measurements, dropout information, and genetic covariates.
method Extended EB-VAE framework to jointly model longitudinal and time-to-event data, incorporating dropout hazard and genetic covariates.
result Hybrid decoder formulation yields consistent treatment-effect parameters and prior predictive performance comparable to neural decoder.