Data-driven Subgrouping of Patient Trajectories with Chronic Diseases: Evidence from Low Back Pain

Christof Friedrich Naumzik, Alice Kongsted, Werner Vach, Stefan Feuerriegel
Proceedings of the fifth Conference on Health, Inference, and Learning, PMLR 248:251-279, 2024.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v248-naumzik24a, title = {Data-driven Subgrouping of Patient Trajectories with Chronic Diseases: Evidence from Low Back Pain}, author = {Naumzik, Christof Friedrich and Kongsted, Alice and Vach, Werner and Feuerriegel, Stefan}, booktitle = {Proceedings of the fifth Conference on Health, Inference, and Learning}, pages = {251--279}, year = {2024}, editor = {Pollard, Tom and Choi, Edward and Singhal, Pankhuri and Hughes, Michael and Sizikova, Elena and Mortazavi, Bobak and Chen, Irene and Wang, Fei and Sarker, Tasmie and McDermott, Matthew and Ghassemi, Marzyeh}, volume = {248}, series = {Proceedings of Machine Learning Research}, month = {27--28 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v248/main/assets/naumzik24a/naumzik24a.pdf}, url = {https://proceedings.mlr.press/v248/naumzik24a.html}, 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. } }
Endnote
%0 Conference Paper %T Data-driven Subgrouping of Patient Trajectories with Chronic Diseases: Evidence from Low Back Pain %A Christof Friedrich Naumzik %A Alice Kongsted %A Werner Vach %A Stefan Feuerriegel %B Proceedings of the fifth Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2024 %E Tom Pollard %E Edward Choi %E Pankhuri Singhal %E Michael Hughes %E Elena Sizikova %E Bobak Mortazavi %E Irene Chen %E Fei Wang %E Tasmie Sarker %E Matthew McDermott %E Marzyeh Ghassemi %F pmlr-v248-naumzik24a %I PMLR %P 251--279 %U https://proceedings.mlr.press/v248/naumzik24a.html %V 248 %X 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.
APA
Naumzik, C.F., Kongsted, A., Vach, W. & Feuerriegel, S.. (2024). Data-driven Subgrouping of Patient Trajectories with Chronic Diseases: Evidence from Low Back Pain. Proceedings of the fifth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 248:251-279 Available from https://proceedings.mlr.press/v248/naumzik24a.html.

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