Can deep learning catch chronic illness before symptoms show? This article explores how time-aware neural networks are reshaping early detection and care planning for conditions like diabetes and COPD ...
University of Virginia School of Data Science researcher Heman Shakeri has been awarded a major new research grant to lead work at the intersection of machine learning and diabetes care.
Researchers develop an AI tool to predict cardiometabolic multimorbidity risk in type 2 diabetes, aiding early intervention and personalised care. Find out more.
The development of humans and other animals unfolds gradually over time, with cells taking on specific roles and functions ...
Diabetes affects over 537 million adults globally, with early detection critical for effective treatment and management. This project develops a machine learning classification model to predict ...
Background: Stress-induced hyperglycemia (SHG) represents a significant metabolic complication in non-diabetic cardiac surgery older adult patients, with substantial implications for postoperative ...
MASLD is prevalent in T2DM patients, with a 65% occurrence rate, and poses a higher risk for severe liver diseases. The study analyzed 3,836 T2DM patients, identifying key predictors like BMI, ...
Background: Patients with type 2 diabetes mellitus (T2DM) have an increased susceptibility to urinary tract infections (UTIs), caused by uropathogenic Escherichia coli (UPEC). Asymptomatic bacteriuria ...
ABSTRACT: Biogas is gaining prominence as a renewable energy source with significant potential to reduce greenhouse gas emissions and mitigate environmental impacts associated with fossil fuels. This ...
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