Data-Driven Subgrouping of Patient Trajectories With Chronic Diseases: Evidence From Low Back Pain

MCML Authors

Stefan Feuerriegel

Prof. Dr.

Principal Investigator

Artificial Intelligence in Management

Abstract

Clinical data informs the personalization of health care with a potential for more effective disease management. In practice, this is achieved by emph{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 emph{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.

inproceedings

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CHIL 2024

5th AHLI Conference on Health, Inference, and Learning . New York City, NY, USA, Jun 27-28, 2024.

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Authors

C. F. Naumzik • A. Kongsted • W. Vach • S. Feuerriegel

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Links

URL

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Research Area

 A1 | Statistical Foundations & Explainability