Modeling longitudinal dynamics of comorbidities

Maag B, Feuerriegel S, Kraus M, Saar-Tsechansky M, Züger T (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: Association for Computing Machinery, Inc

Pages Range: 222-235

Conference Proceedings Title: ACM CHIL 2021 - Proceedings of the 2021 ACM Conference on Health, Inference, and Learning

ISBN: 9781450383592

DOI: 10.1145/3450439.3451871


In medicine, comorbidities refer to the presence of multiple, co-occurring diseases. Due to their co-occurring nature, the course of one comorbidity is often highly dependent on the course of the other disease and, hence, treatments can have significant spill-over effects. Despite the prevalence of comorbidities among patients, a comprehensive statistical framework for modeling the longitudinal dynamics of comorbidities is missing. In this paper, we propose a probabilistic model for analyzing comorbidity dynamics over time in patients. Specifically, we develop a coupled hidden Markov model with a personalized, non-homogeneous transition mechanism, named Comorbidity-HMM. The specification of our Comorbidity-HMM is informed by clinical research: (1) It accounts for different disease states (i. e., acute, stable) in the disease progression by introducing latent states that are of clinical meaning. (2) It models a coupling among the trajectories from comorbidities to capture co-evolution dynamics. (3) It considers between-patient heterogeneity (e. g., risk factors, treatments) in the transition mechanism. Based on our model, we define a spill-over effect that measures the indirect effect of treatments on patient trajectories through coupling (i. e., through comorbidity co-evolution). We evaluated our proposed Comorbidity-HMM based on 675 health trajectories where we investigate the joint progression of diabetes mellitus and chronic liver disease. Compared to alternative models without coupling, we find that our Comorbidity-HMM achieves a superior fit. Further, we quantify the spill-over effect, that is, to what extent diabetes treatments are associated with a change in the chronic liver disease from an acute to a stable disease state. To this end, our model is of direct relevance for both treatment planning and clinical research in the context of comorbidities.

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Maag, B., Feuerriegel, S., Kraus, M., Saar-Tsechansky, M., & Züger, T. (2021). Modeling longitudinal dynamics of comorbidities. In ACM CHIL 2021 - Proceedings of the 2021 ACM Conference on Health, Inference, and Learning (pp. 222-235). Association for Computing Machinery, Inc.


Maag, Basil, et al. "Modeling longitudinal dynamics of comorbidities." Proceedings of the 2021 ACM Conference on Health, Inference, and Learning, CHIL 2021 Association for Computing Machinery, Inc, 2021. 222-235.

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