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Bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency

Hypoglycaemia symptoms vary between individual and across episodes making it difficult for the patients to realize if they are having a hypoglycaemia. Therefore, the ability to detect the onset of hypoglycaemia is important for quick corrective action. In this paper, we describe a Bayesian hierarchi...

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Main Authors: Zulkafli, Hani Syahida, Streftaris, George, Gibson, Gavin
Format: Article
Language:English
Published: Blue Eyes Intelligence Engineering and Sciences Publication 2020
Online Access:http://psasir.upm.edu.my/id/eprint/88011/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/88011/
https://www.ijrte.org/portfolio-item/C6460098319/
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spelling my.upm.eprints.880112022-05-24T04:40:46Z http://psasir.upm.edu.my/id/eprint/88011/ Bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency Zulkafli, Hani Syahida Streftaris, George Gibson, Gavin Hypoglycaemia symptoms vary between individual and across episodes making it difficult for the patients to realize if they are having a hypoglycaemia. Therefore, the ability to detect the onset of hypoglycaemia is important for quick corrective action. In this paper, we describe a Bayesian hierarchical model which is able to quantify the consistency of reporting symptoms by individual patient and simultaneously investigate patient-specific covariates affecting the consistency. The model is developed within a Bayesian framework using Markov chain Monte Carlo methodology where the consistency parameter is estimated via Gibbs sampling. The association between patient-specific covariates and consistency is investigated using generalized linear model before implementing the stepwise regression to identify the best predictive model. The results obtained show that symptoms classified as autonomic and neuroglycopenic are prominent in detecting the onset of hypoglycaemia. No patient-specific covariate appears to be significantly affecting patients reporting' consistency. However, the best predictive model obtained contains covariates gender, type of diabetes, retinopathy, serum angiotensin converting enzyme and C-peptide.The hierarchical model developed allows researchers to estimate patient’s consistency in reporting symptoms and identify factors affecting it under one setting. Blue Eyes Intelligence Engineering and Sciences Publication 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/88011/1/ABSTRACT.pdf Zulkafli, Hani Syahida and Streftaris, George and Gibson, Gavin (2020) Bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency. International Journal of Recent Technology and Engineering, 8 (5). 5319 - 5324. ISSN 2277-3878 https://www.ijrte.org/portfolio-item/C6460098319/ 10.35940/ijrte.C6460.018520
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Hypoglycaemia symptoms vary between individual and across episodes making it difficult for the patients to realize if they are having a hypoglycaemia. Therefore, the ability to detect the onset of hypoglycaemia is important for quick corrective action. In this paper, we describe a Bayesian hierarchical model which is able to quantify the consistency of reporting symptoms by individual patient and simultaneously investigate patient-specific covariates affecting the consistency. The model is developed within a Bayesian framework using Markov chain Monte Carlo methodology where the consistency parameter is estimated via Gibbs sampling. The association between patient-specific covariates and consistency is investigated using generalized linear model before implementing the stepwise regression to identify the best predictive model. The results obtained show that symptoms classified as autonomic and neuroglycopenic are prominent in detecting the onset of hypoglycaemia. No patient-specific covariate appears to be significantly affecting patients reporting' consistency. However, the best predictive model obtained contains covariates gender, type of diabetes, retinopathy, serum angiotensin converting enzyme and C-peptide.The hierarchical model developed allows researchers to estimate patient’s consistency in reporting symptoms and identify factors affecting it under one setting.
format Article
author Zulkafli, Hani Syahida
Streftaris, George
Gibson, Gavin
spellingShingle Zulkafli, Hani Syahida
Streftaris, George
Gibson, Gavin
Bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency
author_facet Zulkafli, Hani Syahida
Streftaris, George
Gibson, Gavin
author_sort Zulkafli, Hani Syahida
title Bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency
title_short Bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency
title_full Bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency
title_fullStr Bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency
title_full_unstemmed Bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency
title_sort bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency
publisher Blue Eyes Intelligence Engineering and Sciences Publication
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/88011/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/88011/
https://www.ijrte.org/portfolio-item/C6460098319/
_version_ 1734301606696976384
score 13.211869

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