Coupled Hidden Markov Model with Bivariate Discrete Copula to Study Comorbidity of Chronic Diseases

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2022-2-09
Oflaz, Zarına
A range of chronic diseases have a significant influence on each other and share common risk factors. Comorbidity, which shows the existence of two or more diseases interacting or triggering each other, is an important measure for actuarial valuations. The main proposal of the thesis is to model parallel interacting processes describing two or more chronic diseases by a combination of hidden Markov theory and copula function. This study introduces a novel coupled hidden Markov model with the bivariate discrete copula function in the hidden process. We use a novel discrete copula, namely the Binomial copula. We compute a complete data log-likelihood and develop an inference necessary to implement the model. To estimate the parameters of the model and deal with the numerical intractability of the log-likelihood, we propose a variational expectation maximization (VEM) algorithm. To perform the VEM algorithm, a lower bound of the model's log-likelihood is defined, and estimators of the parameters are computed in M-part. A possible numerical underflow occurring in the computation of forward-backward probabilities is solved. The simulation study was conducted for two different odds ratios to assess the performance of the proposed model, resulting in satisfactory findings. Additionally, the proposed model was applied to hospital appointment data from a private hospital. The model defines the dependency structure of unobserved disease data and its dynamics. The application results demonstrate that the proposed model is useful for investigating disease comorbidity when only population dynamics over time are available and no clinical data are available.

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Citation Formats
Z. Oflaz, “Coupled Hidden Markov Model with Bivariate Discrete Copula to Study Comorbidity of Chronic Diseases,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.