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
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.
APA:
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.
MLA:
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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