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Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review.
Artif Intell Med. 2024 May;151:102868. doi: 10.1016/j.artmed.2024.102868. Epub 2024 Apr 4.
Artif Intell Med. 2024.
PMID: 38632030
Review.
Prioritizing the glucose-lowering medicines for type 2 diabetes by an extended fuzzy decision-making approach with target-based attributes.
Eghbali-Zarch M, Tavakkoli-Moghaddam R, Esfahanian F, Masoud S.
Eghbali-Zarch M, et al.
Med Biol Eng Comput. 2022 Aug;60(8):2423-2444. doi: 10.1007/s11517-022-02602-3. Epub 2022 Jul 1.
Med Biol Eng Comput. 2022.
PMID: 35776373
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A Markov decision process for modeling adverse drug reactions in medication treatment of type 2 diabetes.
Eghbali-Zarch M, Tavakkoli-Moghaddam R, Esfahanian F, Azaron A, Sepehri MM.
Eghbali-Zarch M, et al.
Proc Inst Mech Eng H. 2019 Aug;233(8):793-811. doi: 10.1177/0954411919853394. Epub 2019 Jun 10.
Proc Inst Mech Eng H. 2019.
PMID: 31177917
The satisfying results show that the model has the capability to offer an optimal treatment policy with an acceptable expected quality of life by utilizing fewer medications and provide significant implications in endocrinology and metabolism applications....
The satisfying results show that the model has the capability to offer an optimal treatment policy with an acceptable expected quality of li …
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Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment.
Eghbali-Zarch M, Tavakkoli-Moghaddam R, Esfahanian F, Sepehri MM, Azaron A.
Eghbali-Zarch M, et al.
Artif Intell Med. 2018 May;87:20-33. doi: 10.1016/j.artmed.2018.03.003. Epub 2018 Mar 30.
Artif Intell Med. 2018.
PMID: 29606521
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