AI-Predicted Disease Trajectories in Diabetes: A Retrospective Study
The study explores the utilization of artificial intelligence (AI) to predict disease progression trajectories in patients with diabetes. By analyzing historical data from a retrospective cohort, we aim to identify patterns and predictors of disease evolution. The approach seeks to enhance personalized treatment strategies and improve outcomes by foreseeing potential complications and disease milestones. The findings could pave the way for more targeted and effective management of diabetes through AI-driven insights.
- EligibilityCriteria: Inclusion Criteria:  Diagnosis: Individuals with a confirmed diagnosis of T1DM or T2DM, as indicated by their EHR labels or a history of glycated hemoglobin levels and medication usage consistent with diabetes management. Age: Patients of all ages are considered, with subgroups possibly defined for more detailed analysis (e.g., pediatric, adult, senior). Treatment history: Both patients who are newly diagnosed and those with an established history of diabetes treatment, including insulin and oral hypoglycemic agents, are included to capture a broad spectrum of disease trajectories.  Exclusion Criteria:  Incomplete records: Patients with incomplete medical records that do not provide sufficient information on their diabetes diagnosis, treatment history, or follow-up data are excluded. Other significant diseases: Individuals with comorbid conditions that could significantly alter the natural history of diabetes or its management (e.g., end-stage renal disease not related to diabetes, active cancer treatment) might be excluded to ensure the study focuses on the diabetes trajectory. - HealthyVolunteers: No - Gender: All - StdAgeList: Child, Adult, Older Adult - StudyPopulation: The study population comprises individuals diagnosed with Diabetes Mellitus, encompassing both Type 1 Diabetes Mellitus (T1DM) and Type 2 Diabetes Mellitus (T2DM). This population is identified through medical records housed within the Electronic Health Record (EHR) system, specifically utilizing data generated by the Smart Digital Clinic Software (Meteda) since its inception in our hospital environment. - SamplingMethod: Probability Sample