  In order to deliver effective care, health management must consider the
distinctive trajectories of chronic diseases. These diseases recurrently
undergo acute, unstable, and stable phases, each of which requires a different
treatment regimen. However, the correct identification of trajectory phases,
and thus treatment regimens, is challenging. In this paper, we propose a
data-driven, dynamic approach for identifying trajectory phases of chronic
diseases and thus suggesting treatment regimens. Specifically, we develop a
novel variable-duration copula hidden Markov model (VDC-HMMX). In our VDC-HMMX,
the trajectory is modeled as a series of latent states with acute, stable, and
unstable phases, which are eventually recovered. We demonstrate the
effectiveness of our VDC-HMMX model on the basis of a longitudinal study with
928 patients suffering from low back pain. A myopic classifier identifies
correct treatment regimens with a balanced accuracy of slightly above 70%. In
comparison, our VDC-HMMX model is correct with a balanced accuracy of 83.65%.
This thus highlights the value of longitudinal monitoring for chronic disease
management.

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