PMID- 35776373
OWN - NLM
STAT- MEDLINE
DCOM- 20220720
LR  - 20220720
IS  - 1741-0444 (Electronic)
IS  - 0140-0118 (Linking)
VI  - 60
IP  - 8
DP  - 2022 Aug
TI  - Prioritizing the glucose-lowering medicines for type 2 diabetes by an extended 
      fuzzy decision-making approach with target-based attributes.
PG  - 2423-2444
LID - 10.1007/s11517-022-02602-3 [doi]
AB  - Different therapeutic classes have been authorized for the treatment of 
      hyperglycemia in type 2 diabetic patients, and even more drug classes are under 
      development. This variety of alternative treatments and the general treatment 
      algorithms of the clinical guidelines lead to a nonuniform prescription of drugs 
      by endocrinologists and diabetic specialists. Diabetes medication choice is a 
      multi-objective problem with many difficulties in making rational decisions 
      because of the wide range of hyperglycemia-lowering agents with multiple benefits 
      and multiple risk elements. This paper proposes a group Entropy-CRiteria 
      Importance Through Inter-criteria Correlation (CRITIC)-Weighted Aggregated Sum 
      Product ASsessment (WASPAS) multi-criteria decision-making (MCDM) model with 
      target-based criteria to prioritize and rank the glucose-lowering medicines for 
      type 2 diabetes using the American Diabetes Association and International 
      Diabetes Federation Clinical Guidelines. The proposed model consists of a 
      weighting method comprising both objective and subjective approaches; the two 
      most common objective approaches (i.e., Entropy and CRITIC methods) are used to 
      find the objective weights. Then, these weights are aggregated with the 
      subjective weights that endocrinologists assign to the criteria. Afterward, a 
      WASPAS target-based method is developed to provide the final ranking of the 
      medications. Finally, the close correlation between the final ranking of the 
      proposed methodology and the average priority order of the medications obtained 
      by different MCDM methods implies the strength and validity of the model 
      performance.
CI  - (c) 2022. International Federation for Medical and Biological Engineering.
FAU - Eghbali-Zarch, Maryam
AU  - Eghbali-Zarch M
AUID- ORCID: 0000-0003-0296-4025
AD  - Department of Industrial Engineering, Faculty of Engineering, Alzahra University, 
      Tehran, Iran. m.eghbalizarch@alzahra.ac.ir.
FAU - Tavakkoli-Moghaddam, Reza
AU  - Tavakkoli-Moghaddam R
AD  - School of Industrial Engineering, College of Engineering, University of Tehran, 
      Tehran, Iran.
FAU - Esfahanian, Fatemeh
AU  - Esfahanian F
AD  - Department of Endocrinology and Metabolism, School of Medicine, Tehran University 
      of Medical Sciences, Tehran, Iran.
FAU - Masoud, Sara
AU  - Masoud S
AD  - Department of Industrial and Systems Engineering, Wayne State University, 
      Detroit, MI, 48202, USA.
LA  - eng
PT  - Journal Article
DEP - 20220701
PL  - United States
TA  - Med Biol Eng Comput
JT  - Medical & biological engineering & computing
JID - 7704869
RN  - IY9XDZ35W2 (Glucose)
SB  - IM
MH  - Decision Making
MH  - *Diabetes Mellitus, Type 2/drug therapy
MH  - Fuzzy Logic
MH  - Glucose
MH  - Humans
MH  - *Hyperglycemia
OTO - NOTNLM
OT  - CRITIC
OT  - Entropy
OT  - Medical decision-making
OT  - Pharmacological therapy selection
OT  - Target-based criteria
OT  - Type 2 diabetes
OT  - WASPAS
EDAT- 2022/07/02 06:00
MHDA- 2022/07/22 06:00
CRDT- 2022/07/01 11:24
PHST- 2022/02/10 00:00 [received]
PHST- 2022/06/07 00:00 [accepted]
PHST- 2022/07/02 06:00 [pubmed]
PHST- 2022/07/22 06:00 [medline]
PHST- 2022/07/01 11:24 [entrez]
AID - 10.1007/s11517-022-02602-3 [pii]
AID - 10.1007/s11517-022-02602-3 [doi]
PST - ppublish
SO  - Med Biol Eng Comput. 2022 Aug;60(8):2423-2444. doi: 10.1007/s11517-022-02602-3. 
      Epub 2022 Jul 1.