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 predis-pose them to a lower quality of life as well as higher rates of mortality, managing blood glucose levels is of clinical relevance in dia-betes 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 character-istics 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 innova-tions 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.

Automated summary

Globally, the number of people with diabetes mellitus has increased by four times in the past 30 years, and approximately one in 11 adults worldwide have diabetes mellitus. Managing blood glucose levels is crucial for diabetes care due to its impact on the quality of life and mortality rates of the patients. Various classes of antihyperglycemic drugs are available, but the application of clinical trial results to actual clinical practice is challenging. Machine learning approaches in drug treatment strategies for diabetes care have been slow to be adopted.