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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Blue Eyes Intelligence Engineering and Sciences Publication
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/88011/ http://psasir.upm.edu.my/id/eprint/88011/1/ABSTRACT.pdf |
| _version_ | 1848860538221625344 |
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| author | Zulkafli, Hani Syahida Streftaris, George Gibson, Gavin |
| author_facet | Zulkafli, Hani Syahida Streftaris, George Gibson, Gavin |
| author_sort | Zulkafli, Hani Syahida |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T12:46:49Z |
| format | Article |
| id | upm-88011 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:46:49Z |
| publishDate | 2020 |
| publisher | Blue Eyes Intelligence Engineering and Sciences Publication |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| spellingShingle | Zulkafli, Hani Syahida Streftaris, George Gibson, Gavin Bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency |
| title | 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_short | Bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency |
| title_sort | bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency |
| url | http://psasir.upm.edu.my/id/eprint/88011/ http://psasir.upm.edu.my/id/eprint/88011/ http://psasir.upm.edu.my/id/eprint/88011/ http://psasir.upm.edu.my/id/eprint/88011/1/ABSTRACT.pdf |