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

Zarina Oflaz; Samir Brahim Belhaouari

doi:10.1007/978-981-97-4876-1_41

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

Zarina Oflaz^*, Samir Brahim Belhaouari

^*Corresponding author for this work

CSE Information & Computing Technology

Qatar University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study aims to investigate the relationship between hypertension and diabetes, two chronic diseases with significant public health implications. While the association between these two diseases has been well-established, the underlying pathogenic mechanisms remain unclear. The authors propose to use a recently developed model for studying multimorbidity, which considers the interactions between risk factors for each disease. We use a coupled hidden Markov model (CHMM) with binomial and truncated geometric copulas to analyze hospital appointment data from a private hospital between January 2015 and December 2020. The results suggest that the CHMM with discrete copulas is an effective tool for examining disease multimorbidity, particularly when clinical data is scarce. The study contributes to our understanding of the complex relationship between hypertension and diabetes, and highlights the importance of considering the interdependence of risk factors in the development of chronic diseases.

Original language	English
Title of host publication	Mathematical Analysis and Numerical Methods - IACMC 2023
Editors	Aliaa Burqan, Rania Saadeh, Ahmad Qazza, Osama Yusuf Ababneh, Juan C. Cortés, Kai Diethelm, Dia Zeidan
Publisher	Springer
Pages	585-598
Number of pages	14
ISBN (Print)	9789819748754
DOIs	https://doi.org/10.1007/978-981-97-4876-1_41
Publication status	Published - 2024
Event	8th International Arab Conference on Mathematics and Computations, IACMC 2023 - Zarqa, Jordan Duration: 10 May 2023 → 12 May 2023

Publication series

Name	Springer Proceedings in Mathematics and Statistics
Volume	466
ISSN (Print)	2194-1009
ISSN (Electronic)	2194-1017

Conference

Conference	8th International Arab Conference on Mathematics and Computations, IACMC 2023
Country/Territory	Jordan
City	Zarqa
Period	10/05/23 → 12/05/23

Keywords

Coupled HMM
Diabetes
Discrete copula
Disease progression
Hypertension
Multimorbidity

Access to Document

10.1007/978-981-97-4876-1_41

Cite this

Oflaz, Z., & Belhaouari, S. B. (2024). Coupled Hidden Markov Model with Binomial and Truncated Geometric Copula to Investigate Hypertension and Diabetes Multimorbidity Progression. In A. Burqan, R. Saadeh, A. Qazza, O. Y. Ababneh, J. C. Cortés, K. Diethelm, & D. Zeidan (Eds.), Mathematical Analysis and Numerical Methods - IACMC 2023 (pp. 585-598). (Springer Proceedings in Mathematics and Statistics; Vol. 466). Springer. https://doi.org/10.1007/978-981-97-4876-1_41

Oflaz, Zarina ; Belhaouari, Samir Brahim. / Coupled Hidden Markov Model with Binomial and Truncated Geometric Copula to Investigate Hypertension and Diabetes Multimorbidity Progression. Mathematical Analysis and Numerical Methods - IACMC 2023. editor / Aliaa Burqan ; Rania Saadeh ; Ahmad Qazza ; Osama Yusuf Ababneh ; Juan C. Cortés ; Kai Diethelm ; Dia Zeidan. Springer, 2024. pp. 585-598 (Springer Proceedings in Mathematics and Statistics).

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abstract = "This study aims to investigate the relationship between hypertension and diabetes, two chronic diseases with significant public health implications. While the association between these two diseases has been well-established, the underlying pathogenic mechanisms remain unclear. The authors propose to use a recently developed model for studying multimorbidity, which considers the interactions between risk factors for each disease. We use a coupled hidden Markov model (CHMM) with binomial and truncated geometric copulas to analyze hospital appointment data from a private hospital between January 2015 and December 2020. The results suggest that the CHMM with discrete copulas is an effective tool for examining disease multimorbidity, particularly when clinical data is scarce. The study contributes to our understanding of the complex relationship between hypertension and diabetes, and highlights the importance of considering the interdependence of risk factors in the development of chronic diseases.",

keywords = "Coupled HMM, Diabetes, Discrete copula, Disease progression, Hypertension, Multimorbidity",

author = "Zarina Oflaz and Belhaouari, {Samir Brahim}",

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Oflaz, Z & Belhaouari, SB 2024, Coupled Hidden Markov Model with Binomial and Truncated Geometric Copula to Investigate Hypertension and Diabetes Multimorbidity Progression. in A Burqan, R Saadeh, A Qazza, OY Ababneh, JC Cortés, K Diethelm & D Zeidan (eds), Mathematical Analysis and Numerical Methods - IACMC 2023. Springer Proceedings in Mathematics and Statistics, vol. 466, Springer, pp. 585-598, 8th International Arab Conference on Mathematics and Computations, IACMC 2023, Zarqa, Jordan, 10/05/23. https://doi.org/10.1007/978-981-97-4876-1_41

Coupled Hidden Markov Model with Binomial and Truncated Geometric Copula to Investigate Hypertension and Diabetes Multimorbidity Progression. / Oflaz, Zarina; Belhaouari, Samir Brahim.
Mathematical Analysis and Numerical Methods - IACMC 2023. ed. / Aliaa Burqan; Rania Saadeh; Ahmad Qazza; Osama Yusuf Ababneh; Juan C. Cortés; Kai Diethelm; Dia Zeidan. Springer, 2024. p. 585-598 (Springer Proceedings in Mathematics and Statistics; Vol. 466).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2024

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AB - This study aims to investigate the relationship between hypertension and diabetes, two chronic diseases with significant public health implications. While the association between these two diseases has been well-established, the underlying pathogenic mechanisms remain unclear. The authors propose to use a recently developed model for studying multimorbidity, which considers the interactions between risk factors for each disease. We use a coupled hidden Markov model (CHMM) with binomial and truncated geometric copulas to analyze hospital appointment data from a private hospital between January 2015 and December 2020. The results suggest that the CHMM with discrete copulas is an effective tool for examining disease multimorbidity, particularly when clinical data is scarce. The study contributes to our understanding of the complex relationship between hypertension and diabetes, and highlights the importance of considering the interdependence of risk factors in the development of chronic diseases.

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Oflaz Z, Belhaouari SB. Coupled Hidden Markov Model with Binomial and Truncated Geometric Copula to Investigate Hypertension and Diabetes Multimorbidity Progression. In Burqan A, Saadeh R, Qazza A, Ababneh OY, Cortés JC, Diethelm K, Zeidan D, editors, Mathematical Analysis and Numerical Methods - IACMC 2023. Springer. 2024. p. 585-598. (Springer Proceedings in Mathematics and Statistics). doi: 10.1007/978-981-97-4876-1_41