Data-driven dynamic treatment planning for chronic diseases
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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 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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