Stochastic latent residual approach for consistency model assessment
Hypoglycaemia is a condition when blood sugar levels in body are too low. This condition is usually a side effect of insulin treatment in diabetic patients. Symptoms of hypoglycaemia vary not only between individuals but also within individuals making it difficult for the patients to recognize their...
| Main Authors: | , , |
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| Format: | Article |
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Horizon Research Publishing
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/85920/ |
| _version_ | 1848860212394459136 |
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| author | Salim, Hani Syahida Streftaris, George Gibson, Gavin J. |
| author_facet | Salim, Hani Syahida Streftaris, George Gibson, Gavin J. |
| author_sort | Salim, Hani Syahida |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Hypoglycaemia is a condition when blood sugar levels in body are too low. This condition is usually a side effect of insulin treatment in diabetic patients. Symptoms of hypoglycaemia vary not only between individuals but also within individuals making it difficult for the patients to recognize their hypoglycaemia episodes. Given this condition, and because the symptoms are not exclusive to only hypoglycaemia, it is very important for patients to be able to identify that they are having a hypoglycaemia episode. Consistency models are statistical models that quantify the consistency of individual symptoms reported during hypoglycaemia. Because there are variations of consistency model, it is important to identify which model best fits the data. The aim of this paper is to asses and verify the models. We developed an assessment method based on stochastic latent residuals and performed posterior predictive checking as the model verification. It was found that a grouped symptom consistency model with multiplicative form of symptom propensity and episode intensity threshold fits the data better and has more reliable predictive ability as compared to other models. This model can be used in assisting patients and medical practitioners to quantify patients’ reporting symptoms capability, hence promote awareness of their hypoglycaemia episodes so that corrective actions can be quickly taken. |
| first_indexed | 2025-11-15T12:41:39Z |
| format | Article |
| id | upm-85920 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T12:41:39Z |
| publishDate | 2020 |
| publisher | Horizon Research Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-859202023-10-26T03:10:11Z http://psasir.upm.edu.my/id/eprint/85920/ Stochastic latent residual approach for consistency model assessment Salim, Hani Syahida Streftaris, George Gibson, Gavin J. Hypoglycaemia is a condition when blood sugar levels in body are too low. This condition is usually a side effect of insulin treatment in diabetic patients. Symptoms of hypoglycaemia vary not only between individuals but also within individuals making it difficult for the patients to recognize their hypoglycaemia episodes. Given this condition, and because the symptoms are not exclusive to only hypoglycaemia, it is very important for patients to be able to identify that they are having a hypoglycaemia episode. Consistency models are statistical models that quantify the consistency of individual symptoms reported during hypoglycaemia. Because there are variations of consistency model, it is important to identify which model best fits the data. The aim of this paper is to asses and verify the models. We developed an assessment method based on stochastic latent residuals and performed posterior predictive checking as the model verification. It was found that a grouped symptom consistency model with multiplicative form of symptom propensity and episode intensity threshold fits the data better and has more reliable predictive ability as compared to other models. This model can be used in assisting patients and medical practitioners to quantify patients’ reporting symptoms capability, hence promote awareness of their hypoglycaemia episodes so that corrective actions can be quickly taken. Horizon Research Publishing 2020 Article PeerReviewed Salim, Hani Syahida and Streftaris, George and Gibson, Gavin J. (2020) Stochastic latent residual approach for consistency model assessment. Mathematics and Statistics, 8 (5). 583 - 589. ISSN 2332-2071; ESSN: 2332-2144 https://researchportal.hw.ac.uk/en/publications/stochastic-latent-residual-approach-for-consistency-model-assessm |
| spellingShingle | Salim, Hani Syahida Streftaris, George Gibson, Gavin J. Stochastic latent residual approach for consistency model assessment |
| title | Stochastic latent residual approach for consistency model assessment |
| title_full | Stochastic latent residual approach for consistency model assessment |
| title_fullStr | Stochastic latent residual approach for consistency model assessment |
| title_full_unstemmed | Stochastic latent residual approach for consistency model assessment |
| title_short | Stochastic latent residual approach for consistency model assessment |
| title_sort | stochastic latent residual approach for consistency model assessment |
| url | http://psasir.upm.edu.my/id/eprint/85920/ http://psasir.upm.edu.my/id/eprint/85920/ |