Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Perveen, Sajida, Shahbaz, Muhammad, Ansari, Muhammad Sajjad, Keshavjee, Karim, Guergachi, Aziz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
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
_version_ 1783487468511690752
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
work_keys_str_mv AT perveensajida ahybridapproachformodelingtype2diabetesmellitusprogression
AT shahbazmuhammad ahybridapproachformodelingtype2diabetesmellitusprogression
AT ansarimuhammadsajjad ahybridapproachformodelingtype2diabetesmellitusprogression
AT keshavjeekarim ahybridapproachformodelingtype2diabetesmellitusprogression
AT guergachiaziz ahybridapproachformodelingtype2diabetesmellitusprogression
AT perveensajida hybridapproachformodelingtype2diabetesmellitusprogression
AT shahbazmuhammad hybridapproachformodelingtype2diabetesmellitusprogression
AT ansarimuhammadsajjad hybridapproachformodelingtype2diabetesmellitusprogression
AT keshavjeekarim hybridapproachformodelingtype2diabetesmellitusprogression
AT guergachiaziz hybridapproachformodelingtype2diabetesmellitusprogression