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2024 | OriginalPaper | Chapter

Coupled Hidden Markov Model with Binomial and Truncated Geometric Copula to Investigate Hypertension and Diabetes Multimorbidity Progression

Authors : Zarina Oflaz, Samir Brahim Belhaouari

Published in: Mathematical Analysis and Numerical Methods

Publisher: Springer Nature Singapore

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Abstract

The chapter focuses on the application of a Coupled Hidden Markov Model (CHMM) with binomial and truncated geometric copulas to study the interdependencies between hypertension and diabetes. It introduces the concept of multimorbidity and the challenges in understanding the underlying pathogenic mechanisms. The study uses real-world data from a private hospital to model the joint behavior of these diseases over time, highlighting the effectiveness of CHMM copulas in handling sparse clinical data. The variational expectation maximization algorithm is employed to optimize the model's fit, and the results show a strong correlation between the diseases. The chapter concludes with insights into the transition dynamics of hidden states, offering a deeper understanding of the complex interplay between hypertension and diabetes. This work paves the way for future research into patient clustering and separate analyses of type 1 and type 2 diabetes.

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Title: Coupled Hidden Markov Model with Binomial and Truncated Geometric Copula to Investigate Hypertension and Diabetes Multimorbidity Progression
Authors: Zarina Oflaz
Samir Brahim Belhaouari
Publisher: Springer Nature Singapore
Book: Mathematical Analysis and Numerical Methods
Print ISBN: 978-981-9748-75-4

Electronic ISBN: 978-981-9748-76-1

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-97-4876-1_41

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