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Diabetes: Management and Pharmacotherapy

Diabetes: Management and Pharmacotherapy

The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
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Diabetes Mellitus: Overview and Type I Subtype

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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

Pathophysiology of Diabetes

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Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
Type 1 diabetes is characterized by autoimmune-mediated destruction of pancreatic β cells, with environmental factors potentially triggering this process in genetically susceptible individuals. Despite many not having a family history, certain genes increase susceptibility,...

Diabetes Mellitus: Type 2 and Gestational

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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...

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Artificial Intelligence Methodologies and Their Application to Diabetes.

Mercedes Rigla¹, Gema García-Sáez^2,3, Belén Pons¹

¹1 Endocrinology and Nutrition Department, Parc Tauli University Hospital, Sabadell, Spain.

Journal of Diabetes Science and Technology

|May 26, 2017

Summary

This summary is machine-generated.

Artificial intelligence (AI) is transforming diabetes management by integrating data from continuous glucose monitoring, insulin pumps, and wearable devices. This technology aids healthcare providers in making informed decisions for better patient care.

Keywords:

artificial intelligence decision support diabetes machine learning

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Area of Science:

Medical Informatics
Artificial Intelligence
Data Science

Background:

Diabetes management has evolved with continuous glucose monitoring and insulin pump data.
Wearable devices now provide physiological data like heart rate, sleep, and activity levels.
Future data integration includes hydration, geolocation, and barometric pressure.

Purpose of the Study:

To explain artificial intelligence (AI) methodologies used in diabetes management.
To promote the involvement of healthcare providers (doctors and nurses) in AI applications.
To facilitate knowledge acquisition and decision support in diabetes care.

Main Methods:

Analysis of integrated data from various monitoring devices.
Application of artificial intelligence (AI) algorithms for pattern recognition and prediction.
Development of multidisciplinary research teams combining medical and computer science expertise.

Main Results:

AI-driven analysis of diverse data streams enhances patient and doctor decision support.
Increased publication trends show AI tools becoming integral to diabetes healthcare.
AI facilitates knowledge acquisition in the evolving field of diabetes management.

Conclusions:

Artificial intelligence (AI) is revolutionizing diabetes care by enabling sophisticated data analysis.
Healthcare professionals need to integrate AI knowledge into their practice.
AI tools are essential for advancing decision support and knowledge acquisition in diabetes management.