All Times EDT
Keywords: Hierarchical model, Markov jump process, finite mixture model
Hidden Markov jump processes are an attractive approach for modeling clinical disease progression data because they are explainable and capable of handling both irregularly sampled and noisy data. Most applications in this context consider time-homogeneous models due to their relative computational simplicity. However, the time homogeneous assumption is too strong to accurately model the natural history of many diseases. Moreover, the population at risk is not homogeneous either, since disease exposure and susceptibility can vary considerably. We propose a piece-wise stationary transition matrix to explain the heterogeneity in time. We propose a hierarchical structure for the heterogeneity in population, where prior information is considered to deal with unbalanced data. An efficient, scalable EM algorithm is proposed for inference. We demonstrate the feasibility and superiority of our model on both synthetic data and a cervical cancer screening dataset from the Cancer Registry of Norway.