A probabilistic generative model to discover the treatments of coexisting diseases with missing data

Onintze Zaballa, Aritz Pérez, Elisa Gómez-Inhiesto, Teresa Acaiturri Ayesta, José Antonio Lozano

Abstract: Highlights•We propose a novel generative model for the joint evolution of comorbidities.•The model is efficiently learned with the Expectation-Maximization algorithm.•The complexity of the model is reduced with a dynamic programing based method.•Experiments in synthetic data show that the model underlying the data is recovered.•Applications: missing data imputation, treatment segmentation and patient subtyping.