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A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression
Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder characterized by hyperglycemia resulting from abnormalities in insulin secretion, insulin action, or both. It is associated with an increased risk of developing vascular complication of micro as well as macro nature. Becaus...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6958689/ https://www.ncbi.nlm.nih.gov/pubmed/31969896 http://dx.doi.org/10.3389/fgene.2019.01076 |
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author | Perveen, Sajida Shahbaz, Muhammad Ansari, Muhammad Sajjad Keshavjee, Karim Guergachi, Aziz |
author_facet | Perveen, Sajida Shahbaz, Muhammad Ansari, Muhammad Sajjad Keshavjee, Karim Guergachi, Aziz |
author_sort | Perveen, Sajida |
collection | PubMed |
description | Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder characterized by hyperglycemia resulting from abnormalities in insulin secretion, insulin action, or both. It is associated with an increased risk of developing vascular complication of micro as well as macro nature. Because of its inconspicuous and heterogeneous character, the management of T2DM is very complex. Modeling physiological processes over time demonstrating the patient’s evolving health condition is imperative to comprehending the patient’s current status of health, projecting its likely dynamics and assessing the requisite care and treatment measures in future. Hidden Markov Model (HMM) is an effective approach for such prognostic modeling. However, the nature of the clinical setting, together with the format of the Electronic Medical Records (EMRs) data, in particular the sparse and irregularly sampled clinical data which is well understood to present significant challenges, has confounded standard HMM. In the present study, we proposed an approximation technique based on Newton’s Divided Difference Method (NDDM) as a component with HMM to determine the risk of developing diabetes in an individual over different time horizons using irregular and sparsely sampled EMRs data. The proposed method is capable of exploiting available sequences of clinical measurements obtained from a longitudinal sample of patients for effective imputation and improved prediction performance. Furthermore, results demonstrated that the discrimination capability of our proposed method, in prognosticating diabetes risk, is superior to the standard HMM. |
format | Online Article Text |
id | pubmed-6958689 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-69586892020-01-22 A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression Perveen, Sajida Shahbaz, Muhammad Ansari, Muhammad Sajjad Keshavjee, Karim Guergachi, Aziz Front Genet Genetics Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder characterized by hyperglycemia resulting from abnormalities in insulin secretion, insulin action, or both. It is associated with an increased risk of developing vascular complication of micro as well as macro nature. Because of its inconspicuous and heterogeneous character, the management of T2DM is very complex. Modeling physiological processes over time demonstrating the patient’s evolving health condition is imperative to comprehending the patient’s current status of health, projecting its likely dynamics and assessing the requisite care and treatment measures in future. Hidden Markov Model (HMM) is an effective approach for such prognostic modeling. However, the nature of the clinical setting, together with the format of the Electronic Medical Records (EMRs) data, in particular the sparse and irregularly sampled clinical data which is well understood to present significant challenges, has confounded standard HMM. In the present study, we proposed an approximation technique based on Newton’s Divided Difference Method (NDDM) as a component with HMM to determine the risk of developing diabetes in an individual over different time horizons using irregular and sparsely sampled EMRs data. The proposed method is capable of exploiting available sequences of clinical measurements obtained from a longitudinal sample of patients for effective imputation and improved prediction performance. Furthermore, results demonstrated that the discrimination capability of our proposed method, in prognosticating diabetes risk, is superior to the standard HMM. Frontiers Media S.A. 2020-01-07 /pmc/articles/PMC6958689/ /pubmed/31969896 http://dx.doi.org/10.3389/fgene.2019.01076 Text en Copyright © 2020 Perveen, Shahbaz, Ansari, Keshavjee and Guergachi http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Perveen, Sajida Shahbaz, Muhammad Ansari, Muhammad Sajjad Keshavjee, Karim Guergachi, Aziz A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression |
title | A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression |
title_full | A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression |
title_fullStr | A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression |
title_full_unstemmed | A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression |
title_short | A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression |
title_sort | hybrid approach for modeling type 2 diabetes mellitus progression |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6958689/ https://www.ncbi.nlm.nih.gov/pubmed/31969896 http://dx.doi.org/10.3389/fgene.2019.01076 |
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