Data-driven subgrouping of patient trajectories with chronic diseases: Evidence from low back pain
This work addresses the need for more effective disease management in chronic conditions like low back pain, though it is incremental as it builds on existing subgrouping methods with a tailored probabilistic model.
The paper tackled the problem of personalizing healthcare for chronic diseases by developing a novel mixture hidden Markov model to subgroup patient trajectories, identifying 8 subgroups in a study of 847 low back pain patients and outperforming baselines in cluster validity indices.
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.