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Stochastic latent residual approach for consistency model assessment


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


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.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Science
Publisher: Horizon Research Publishing
Keywords: Latent residual; Model assessment; Model verification; Posterior predictive checking
Depositing User: Nurul Ainie Mokhtar
Date Deposited: 26 Oct 2023 03:10
Last Modified: 26 Oct 2023 03:10
URI: http://psasir.upm.edu.my/id/eprint/85920
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