  Clinical data informs the personalization of health care with a potential for
more effective disease management. In practice, this is achieved by
subgrouping, whereby clusters with similar patient characteristics are
identified and then receive customized treatment plans with the goal of
targeting subgroup-specific disease dynamics. In this paper, we propose a novel
mixture hidden Markov model for subgrouping patient trajectories from chronic
diseases. Our model is probabilistic and carefully designed to capture
different trajectory phases of chronic diseases (i.e., "severe", "moderate",
and "mild") through tailored latent states. We demonstrate our subgrouping
framework based on a longitudinal study across 847 patients with non-specific
low back pain. Here, our subgrouping framework identifies 8 subgroups. Further,
we show that our subgrouping framework outperforms common baselines in terms of
cluster validity indices. Finally, we discuss the applicability of the model to
other chronic and long-lasting diseases.
