Predict Diabetes Complications

Diabetes represents a significant global challenge, with a variety of severe complications affecting multiple organs and bodily systems. These complications include cardiovascular diseases, neuropathy, nephropathy, and retinopathy, contributing to overall morbidity and mortality in the affected population. Furthermore, the incidence of these complications can vary considerably based on demographics and individual risk factors, complicating effective treatment and management of the disease [1], [2].

Approximately 30% of individuals with diabetes develop disease-related complications [3]. This underscores the urgent need for effective strategies to prevent and manage these complications, which can significantly improve patient quality of life and alleviate the burden on the healthcare system.

Size of the Problem

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• Global Prevalence: According to the World Health Organization, approximately 422 million people worldwide have diabetes [1].
• Mortality: Diabetes was the direct cause of approximately 1.6 million deaths in 2016 [2].
• Economic Costs: The global estimated cost of diabetes was about $1.3 trillion in 2015 [3].

Addressing the complications of diabetes is crucial due to its profound impact on both patient quality of life and global health systems. Diabetic complications are a leading cause of disability, resulting in significant economic burden due to treatment costs and loss of productivity. Furthermore, the ability to prevent or delay these complications can translate into substantial improvements in health outcomes and a reduction in premature deaths. Thus, developing effective strategies to prevent and manage these complications is essential, not only improving patients' lives but also reducing the strain on health resources [2].

To tackle this challenge, the solution proposed is the development of a digital assistant specifically designed to help medical specialists predict complications related to diabetes more quickly and take rapid action. Built upon the guidelines outlined in [4], this assistant leverages AI to analyze clinical and biometric data, providing personalized recommendations to specialists for improving patient habits and preventing complications. Through continuous interaction with clinical staff, the assistant empowers specialists to take proactive steps in patient care, enhancing treatment outcomes and overall patient well-being.

• Electronic Health Records (EHR): Detailed patient medical histories including lab results, medical history, and clinical notes.
• Medical Image Databases: Sets of retinography images for analysis and detection of diabetic retinopathy using computer vision algorithms.
• Pharmacy Records: Information on prescriptions that can help monitor treatment adherence and predict complications based on medication use.
• Continuous Glucose Monitoring (CGM) Data: Continuous readings of glucose levels that can be analyzed to detect patterns and predict episodes of hyperglycemia or hypoglycemia.
• Long-Term Cohort Study Databases: Longitudinal information on diabetic patients that can be used to study disease progression and the effectiveness of various therapeutic interventions.