PMID- 29606521
OWN - NLM
STAT- MEDLINE
DCOM- 20190624
LR  - 20190624
IS  - 1873-2860 (Electronic)
IS  - 0933-3657 (Linking)
VI  - 87
DP  - 2018 May
TI  - Pharmacological therapy selection of type 2 diabetes based on the SWARA and 
      modified MULTIMOORA methods under a fuzzy environment.
PG  - 20-33
LID - S0933-3657(17)30522-5 [pii]
LID - 10.1016/j.artmed.2018.03.003 [doi]
AB  - Medication selection for Type 2 Diabetes (T2D) is a challenging medical 
      decision-making problem involving multiple medications that can be prescribed to 
      control the patient's blood glucose. The wide range of hyperglycemia lowering 
      agents with varying effects and various side effects makes the decision quite 
      difficult. This paper presents computer-aided medical decision support using a 
      fuzzy Multi-Criteria Decision-Making (MCDM) model that hybridizes a Step-wise 
      Weight Assessment Ratio Analysis (SWARA) method with a modification of Fuzzy 
      Multi-Objective Optimization on the basis of a Ratio Analysis plus the full 
      multiplicative form (FMULTIMOORA) method for pharmacological therapy selection of 
      T2D. It makes the use of SWARA for obtaining the relative significance of every 
      selected criterion by soliciting experts' opinions and FMULTIMOORA method for 
      evaluation of each alternative according to all criteria based on a published 
      clinical guideline. In this paper, an extended reference point approach is 
      considered in the proposed hybrid MCDM model that resolves the classic reference 
      point limitations and improves the FMULTIMOORA ranking procedure. Computational 
      results indicate that Metformin is confirmed as the first-line medication and 
      Sulfonylurea as the second-line add-on therapy. The Glucagon-like peptide-1 
      receptor agonist, Dipeptidyl peptidase-4 inhibitor, and Insulin are placed 3rd, 
      4th, and 5th, respectively. A sensitivity analysis is conducted to validate the 
      model performance by comparing its result with studies in the literature, other 
      fuzzy MCDM techniques and an interval MULTIMOORA method based on an observational 
      dataset. The close correspondence between the final rankings of anti-diabetic 
      agents resulted from the proposed hybrid model and other methodologies provide 
      significant implications for endocrinologists to refer.
CI  - Copyright (c) 2018 Elsevier B.V. All rights reserved.
FAU - Eghbali-Zarch, M
AU  - Eghbali-Zarch M
AD  - School of Industrial Engineering, College of Engineering, University of Tehran, 
      Tehran, Iran.
FAU - Tavakkoli-Moghaddam, R
AU  - Tavakkoli-Moghaddam R
AD  - School of Industrial Engineering, College of Engineering, University of Tehran, 
      Tehran, Iran; LCFC, Arts et Metiers Paris Tech, Metz, France; Universal 
      Scientific Education and Research Network (USERN), Tehran, Iran. Electronic 
      address: tavakoli@ut.ac.ir.
FAU - Esfahanian, F
AU  - Esfahanian F
AD  - Department of Endocrinology & Metabolism, School of Medicine, Tehran University 
      of Medical Sciences, Tehran, Iran.
FAU - Sepehri, M M
AU  - Sepehri MM
AD  - Faculty of Industrial and Systems Engineering, Tarbiat Modares University, 
      Tehran, Iran.
FAU - Azaron, A
AU  - Azaron A
AD  - Beedie School of Business, Simon Fraser University, Vancouver, Canada; School of 
      Business, Kwantlen Polytechnic University, Vancouver, Canada.
LA  - eng
PT  - Journal Article
DEP - 20180330
PL  - Netherlands
TA  - Artif Intell Med
JT  - Artificial intelligence in medicine
JID - 8915031
RN  - 0 (Hypoglycemic Agents)
SB  - IM
MH  - *Decision Support Techniques
MH  - Diabetes Mellitus, Type 2/*drug therapy
MH  - *Fuzzy Logic
MH  - Humans
MH  - Hypoglycemic Agents/*therapeutic use
OTO - NOTNLM
OT  - Fuzzy environment
OT  - MULTIMOORA
OT  - Medical decision making
OT  - Pharmacological therapy selection
OT  - SWARA
OT  - Type 2 diabetes
EDAT- 2018/04/03 06:00
MHDA- 2019/06/25 06:00
CRDT- 2018/04/03 06:00
PHST- 2017/10/09 00:00 [received]
PHST- 2018/03/07 00:00 [revised]
PHST- 2018/03/13 00:00 [accepted]
PHST- 2018/04/03 06:00 [pubmed]
PHST- 2019/06/25 06:00 [medline]
PHST- 2018/04/03 06:00 [entrez]
AID - S0933-3657(17)30522-5 [pii]
AID - 10.1016/j.artmed.2018.03.003 [doi]
PST - ppublish
SO  - Artif Intell Med. 2018 May;87:20-33. doi: 10.1016/j.artmed.2018.03.003. Epub 2018 
      Mar 30.