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Application of a Machine Learning Technology in the Definition of Metabolically Healthy and Unhealthy Status: A Retrospective Study of 2567 Subjects Suffering from Obesity with or without Metabolic Syndrome.
Masi D, Risi R, Biagi F, Vasquez Barahona D, Watanabe M, Zilich R, Gabrielli G, Santin P, Mariani S, Lubrano C, Gnessi L. Masi D, et al. Among authors: santin p. Nutrients. 2022 Jan 15;14(2):373. doi: 10.3390/nu14020373. Nutrients. 2022. PMID: 35057554 Free PMC article.
Achieving Good Metabolic Control Without Weight Gain with the Systematic Use of GLP-1-RAs and SGLT-2 Inhibitors in Type 2 Diabetes: A Machine-learning Projection Using Data from Clinical Practice.
Giorda CB, Rossi A, Baccetti F, Zilich R, Romeo F, Besmir N, Di Cianni G, Guaita G, Morviducci L, Muselli M, Ozzello A, Pisani F, Ponzani P, Santin P, Verda D, Musacchio N. Giorda CB, et al. Among authors: santin p. Clin Ther. 2023 Aug;45(8):754-761. doi: 10.1016/j.clinthera.2023.06.006. Epub 2023 Jul 12. Clin Ther. 2023. PMID: 37451913 Free article.
Uncovering Predictors of Lipid Goal Attainment in Type 2 Diabetes Outpatients Using Logic Learning Machine: Insights from the AMD Annals and AMD Artificial Intelligence Study Group.
Masi D, Zilich R, Candido R, Giancaterini A, Guaita G, Muselli M, Ponzani P, Santin P, Verda D, Musacchio N; Associazione Medici Diabetologi (AMD) Annals Study Group and AMD Artificial Intelligence Study Group. Masi D, et al. Among authors: santin p. J Clin Med. 2023 Jun 16;12(12):4095. doi: 10.3390/jcm12124095. J Clin Med. 2023. PMID: 37373787 Free PMC article.