Data-driven subgrouping of patient trajectories with chronic diseases: Evidence from low back pain

• URL: http://arxiv.org/abs/2404.10580v1
• Date: Tue, 16 Apr 2024 14:05:29 GMT
• Title: Data-driven subgrouping of patient trajectories with chronic diseases: Evidence from low back pain
• Authors: Christof Naumzik, Alice Kongsted, Werner Vach, Stefan Feuerriegel,
• Abstract summary: 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. We show that our subgrouping framework outperforms common baselines in terms of cluster validity indices.
• Score: 18.837597864085865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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