Machine Learning Approach to Drug Treatment Strategy for Diabetes Care
Globally, the number of people with diabetes mellitus has quadrupled in the past three decades, and approximately one in 11 adults worldwide have diabetes mellitus. Since both microvascular and macrovascular diseases in patients with diabetes predispose them to a lower quality of life as well as hig...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Korean Diabetes Association
2023-05-01
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| Series: | Diabetes & Metabolism Journal |
| Subjects: | |
| Online Access: | http://www.e-dmj.org/upload/pdf/dmj-2022-0349.pdf |
| _version_ | 1797814522202816512 |
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| author | Kazuya Fujihara Hirohito Sone |
| author_facet | Kazuya Fujihara Hirohito Sone |
| author_sort | Kazuya Fujihara |
| collection | DOAJ |
| description | Globally, the number of people with diabetes mellitus has quadrupled in the past three decades, and approximately one in 11 adults worldwide have diabetes mellitus. Since both microvascular and macrovascular diseases in patients with diabetes predispose them to a lower quality of life as well as higher rates of mortality, managing blood glucose levels is of clinical relevance in diabetes care. Many classes of antihyperglycemic drugs are currently approved to treat hyperglycemia in patients with type 2 diabetes mellitus, with several new drugs having been developed during the last decade. Diabetes-related complications have been reduced substantially worldwide. Prioritization of therapeutic agents varies according to national guidelines. However, since the characteristics of participants in clinical trials differ from patients in actual clinical practice, it is difficult to apply the results of such trials to clinical practice. Machine learning approaches became highly topical issues in medicine along with rapid technological innovations in the fields of information and communication in the 1990s. However, adopting these technologies to support decision-making regarding drug treatment strategies for diabetes care has been slow. This review summarizes data from recent studies on the choice of drugs for type 2 diabetes mellitus focusing on machine learning approaches. |
| first_indexed | 2024-03-13T08:08:52Z |
| format | Article |
| id | doaj.art-e47b5c2273e941fa96403a5876319bf2 |
| institution | Directory Open Access Journal |
| issn | 2233-6079 2233-6087 |
| language | English |
| last_indexed | 2024-03-13T08:08:52Z |
| publishDate | 2023-05-01 |
| publisher | Korean Diabetes Association |
| record_format | Article |
| series | Diabetes & Metabolism Journal |
| spelling | doaj.art-e47b5c2273e941fa96403a5876319bf22023-06-01T00:30:47ZengKorean Diabetes AssociationDiabetes & Metabolism Journal2233-60792233-60872023-05-0147332533210.4093/dmj.2022.03492706Machine Learning Approach to Drug Treatment Strategy for Diabetes CareKazuya FujiharaHirohito Sone0 Department of Internal Medicine, Niigata University Faculty of Medicine, Niigata, JapanGlobally, the number of people with diabetes mellitus has quadrupled in the past three decades, and approximately one in 11 adults worldwide have diabetes mellitus. Since both microvascular and macrovascular diseases in patients with diabetes predispose them to a lower quality of life as well as higher rates of mortality, managing blood glucose levels is of clinical relevance in diabetes care. Many classes of antihyperglycemic drugs are currently approved to treat hyperglycemia in patients with type 2 diabetes mellitus, with several new drugs having been developed during the last decade. Diabetes-related complications have been reduced substantially worldwide. Prioritization of therapeutic agents varies according to national guidelines. However, since the characteristics of participants in clinical trials differ from patients in actual clinical practice, it is difficult to apply the results of such trials to clinical practice. Machine learning approaches became highly topical issues in medicine along with rapid technological innovations in the fields of information and communication in the 1990s. However, adopting these technologies to support decision-making regarding drug treatment strategies for diabetes care has been slow. This review summarizes data from recent studies on the choice of drugs for type 2 diabetes mellitus focusing on machine learning approaches.http://www.e-dmj.org/upload/pdf/dmj-2022-0349.pdfartificial intelligencediabetes mellitus, type 2decision makinghypoglycemic agentsmachine learning |
| spellingShingle | Kazuya Fujihara Hirohito Sone Machine Learning Approach to Drug Treatment Strategy for Diabetes Care Diabetes & Metabolism Journal artificial intelligence diabetes mellitus, type 2 decision making hypoglycemic agents machine learning |
| title | Machine Learning Approach to Drug Treatment Strategy for Diabetes Care |
| title_full | Machine Learning Approach to Drug Treatment Strategy for Diabetes Care |
| title_fullStr | Machine Learning Approach to Drug Treatment Strategy for Diabetes Care |
| title_full_unstemmed | Machine Learning Approach to Drug Treatment Strategy for Diabetes Care |
| title_short | Machine Learning Approach to Drug Treatment Strategy for Diabetes Care |
| title_sort | machine learning approach to drug treatment strategy for diabetes care |
| topic | artificial intelligence diabetes mellitus, type 2 decision making hypoglycemic agents machine learning |
| url | http://www.e-dmj.org/upload/pdf/dmj-2022-0349.pdf |
| work_keys_str_mv | AT kazuyafujihara machinelearningapproachtodrugtreatmentstrategyfordiabetescare AT hirohitosone machinelearningapproachtodrugtreatmentstrategyfordiabetescare |