PMID- 31969896
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200930
IS  - 1664-8021 (Print)
IS  - 1664-8021 (Electronic)
IS  - 1664-8021 (Linking)
VI  - 10
DP  - 2019
TI  - A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression.
PG  - 1076
LID - 10.3389/fgene.2019.01076 [doi]
LID - 1076
AB  - 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.
CI  - Copyright (c) 2020 Perveen, Shahbaz, Ansari, Keshavjee and Guergachi.
FAU - Perveen, Sajida
AU  - Perveen S
AD  - Department of Computer Science & Engineering, University of Engineering & 
      Technology, Lahore, Pakistan.
FAU - Shahbaz, Muhammad
AU  - Shahbaz M
AD  - Department of Computer Science & Engineering, University of Engineering & 
      Technology, Lahore, Pakistan.
AD  - Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, 
      Canada.
FAU - Ansari, Muhammad Sajjad
AU  - Ansari MS
AD  - Division of Science and Technology, University of Education, Lahore, Pakistan.
FAU - Keshavjee, Karim
AU  - Keshavjee K
AD  - Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, 
      Canada.
AD  - Institute for Health Policy, Management and Evaluation, University of Toronto, 
      Toronto, ON, Canada.
FAU - Guergachi, Aziz
AU  - Guergachi A
AD  - Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, 
      Canada.
AD  - Ted Rogers School of Information Technology Management, Ryerson University, 
      Toronto, ON, Canada.
AD  - Department of Mathematics & Statistics, York University, Toronto, ON, Canada.
LA  - eng
PT  - Journal Article
DEP - 20200107
PL  - Switzerland
TA  - Front Genet
JT  - Frontiers in genetics
JID - 101560621
PMC - PMC6958689
OTO - NOTNLM
OT  - hidden Markov model
OT  - machine learning
OT  - prognostic modelling
OT  - risk prediction
OT  - risk scoring
OT  - type 2 diabetes mellitus
EDAT- 2020/01/24 06:00
MHDA- 2020/01/24 06:01
PMCR- 2020/01/07
CRDT- 2020/01/24 06:00
PHST- 2019/07/08 00:00 [received]
PHST- 2019/10/09 00:00 [accepted]
PHST- 2020/01/24 06:00 [entrez]
PHST- 2020/01/24 06:00 [pubmed]
PHST- 2020/01/24 06:01 [medline]
PHST- 2020/01/07 00:00 [pmc-release]
AID - 10.3389/fgene.2019.01076 [doi]
PST - epublish
SO  - Front Genet. 2020 Jan 7;10:1076. doi: 10.3389/fgene.2019.01076. eCollection 2019.