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A mutation in the DNA of a single cell that compromises its function initiates leukemia,leading to the overproduction of immature white blood cells that encroach upon the space required for the generation of healthy blood cells.Leukemia is…
While the Artificial Pancreas is effective in regulating the blood glucose in the safe range of 70-180 mg/dl in type 1 diabetic patients, the high intra-patient variability, as well as exogenous meal disturbances, poses a serious challenge.…
The management of hyperglycemia in hospitalized patients has a significant impact on both morbidity and mortality. Therefore, it is important to predict the need for diabetic patients to be hospitalized. However, using standard machine…
Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments…
Prediabetes is a common health condition that often goes undetected until it progresses to type 2 diabetes. Early identification of prediabetes is essential for timely intervention and prevention of complications. This research explores the…
Diabetes Mellitus, one of the leading causes of death worldwide, has no cure to date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated.…
As witnessed in the past year, where the world was brought to the ground by a pandemic, fighting Life-threatening diseases have found greater focus than ever. The first step in fighting a disease is to diagnose it at the right time.…
Diabetes is a chronic metabolic disease characterized by elevated blood glucose levels, leading to complications like heart disease, kidney failure, and nerve damage. Accurate state-level predictions are vital for effective healthcare…
Background: Accurate week-ahead forecasts of continuous glucose monitoring (CGM) derived metrics could enable proactive diabetes management, but relative performance of modern tabular learning approaches is incompletely defined. Methods: We…
Diabetes cases worldwide have risen steadily over the past decades, lending urgency to the search for more efficient, effective, and personalized ways to treat the disease. Current treatment strategies, however, may fail to maintain…
Biomedical research has revealed the crucial role of miRNAs in the progression of many diseases, and computational prediction methods are increasingly proposed for assisting biological experiments to verify miRNA-disease associations…
Early diagnosis is important for type 2 diabetes (T2D) to improve patient prognosis, prevent complications and reduce long-term treatment costs. We present a novel risk profiling approach based exclusively on health expenditure data that is…
Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from…
The significant portion of diabetic patients was affected due to major blindness caused by Diabetic retinopathy (DR). For diabetic retinopathy, lesion segmentation, and detection the comprehensive examination is delved into the deep…
Managing patients with chronic diseases is a major and growing healthcare challenge in several countries. A chronic condition, such as diabetes, is an illness that lasts a long time and does not go away, and often leads to the patient's…
Interactions among multiple genes across the genome may contribute to the risks of many complex human diseases. Whole-genome single nucleotide polymorphisms (SNPs) data collected for many thousands of SNP markers from thousands of…
Rapid changes in blood glucose levels can have severe and immediate health consequences, leading to the need to develop indices for assessing these rapid changes based on continuous glucose monitoring (CGM) data. We proposed a CGM index,…
This dissertation explores the application of machine learning in molecular biology, focusing on gene expression regulation and cellular behavior at the single-cell level. Using modern neural networks, the research addresses key challenges…
This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy…
Currently emerging "big data" techniques are reshaping medical science into a data science. Medical claims data allow assessing an entire nation's health state in a quantitative way, in particular with regard to the occurrences and…