Diabetes is a chronic condition that affects a substantial portion of the global population and is linked to elevated mortality rates and a range of severe health complications. Despite its clinical importance, progress in diabetes research is often ...
BACKGROUND: Diabetes mellitus (DM) is a major noncommunicable disease with a significant increase in prevalence, especially in low- and middle-income countries. The latest International Diabetes Federation Diabetes Atlas (2025) reports that 11.1% of ...
Non-ST-elevation myocardial infarction (NSTEMI) in elderly diabetic patients presents unique challenges in risk assessment and prognosis prediction. This study aimed to develop and validate a machine learning-based mortality risk prediction model for...
Machine learning (ML) has the potential to drastically improve clinical decision-making by predicting diseases early, accurately, and based on data. This study evaluated and compared the performance of several machine learning models, including a fee...
Univariate and multivariate Cox analyses revealed a correlation between diabetes and the prognosis of gastric cancer patients (p < 0.05). Using bioinformatics, Serine/threonine-protein kinase pim-1 (PIM1) was identified as the core target gene of tri...
Sepsis-induced glucose fluctuations present major challenges in critical care, underscoring the importance of accurate glucose monitoring and forecasting to improve patient outcomes. This study introduces a suite of forecasting models trained using c...
Diabetes mellitus (DM), a prevalent metabolic disorder, poses significant diagnostic and therapeutic challenges, especially, in the early stage diagnosis of diabetes related complications. Accurate early stage diagnosis of diabetes and its complicati...
Diabetes mellitus is a major global health burden, and early identification of insulin dependency is important for timely intervention. This study developed an artificial intelligence-based diagnostic system using a real-world clinical dataset of 100...
We aimed to identify and validate key predictive factors influencing 28-day survival rates in patients with diabetes and sepsis and to develop a predictive model based on these factors to assist clinical decision-making. In this retrospective cohort ...
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