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Current Medical Research and Opinion Volume 36, 2020 - Issue 3

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Diabetes

Myocardial infarction in type 2 diabetes using sodium–glucose co-transporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors or glucagon-like peptide-1 receptor agonists: proportional hazards analysis by deep neural network based machine learning

Tomohide YamadaDepartment of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan; Correspondence[email protected]

Kosuke IwasakiJapan Healthcare Practice and Data Analytics, Milliman Inc., Tokyo, Japan;

Shotaro MaederaGraduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan;

Katsuya ItoDepartment of Economics, University of Tokyo, Tokyo, Japan;

Tomomi TakeshimaJapan Healthcare Practice and Data Analytics, Milliman Inc., Tokyo, Japan;

Hisashi NomaDepartment of Data Science, The Institute of Statistical Mathematics, Tokyo, Japan

Nobuhiro ShojimaDepartment of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan;

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Pages 403-409 | Received 25 Oct 2019, Accepted 13 Dec 2019, Published online: 06 Jan 2020

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https://doi.org/10.1080/03007995.2019.1706043
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Myocardial infarction in type 2 diabetes using sodium–glucose co-transporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors or glucagon-like peptide-1 receptor agonists: proportional hazards analysis by deep neural network based machine learning

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Myocardial infarction in type 2 diabetes using sodium–glucose co-transporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors or glucagon-like peptide-1 receptor agonists: proportional hazards analysis by deep neural network based machine learning

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