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Modern treatments for Type 1 diabetes (T1D) use devices known as artificial pancreata (APs), which combine an insulin pump with a continuous glucose monitor (CGM) operating in a closed-loop manner to control blood glucose levels. In…
In this paper we investigate the use of model-based reinforcement learning to assist people with Type 1 Diabetes with insulin dose decisions. The proposed architecture consists of multiple Echo State Networks to predict blood glucose levels…
The presence of protein aggregates in cells is a known feature of many human age-related diseases, such as Huntington's disease. Simulations using fixed parameter values in a model of the dynamic evolution of expanded polyglutamine (PolyQ)…
In order for clinicians to manage disease progression and make effective decisions about drug dosage, treatment regimens or scheduling follow up appointments, it is necessary to be able to identify both short and long-term trends in…
Accurately detecting hypoglycemia without invasive glucose sensors remains a critical challenge in diabetes management, particularly in regions where continuous glucose monitoring (CGM) is prohibitively expensive or clinically inaccessible.…
The mathematical modelling of biological systems has historically followed one of two approaches: comprehensive and minimal. In comprehensive models, the involved biological pathways are modelled independently, then brought together as an…
In the Oral Glucose Tolerance Test (OGTT), a patient, after an overnight fast ingests a load of glucose. Then measurements of glucose concentration are taken every 30 minutes during two hours. The test is used to aid diagnosis of diabetes,…
We propose and create an incentive based recommendation algorithm aimed at improving the lifestyle of diabetic patients. This algorithm is integrated into a real world mobile application to provide personalized health recommendations.…
Motivation: There is a growing need to integrate mechanistic models of biological processes with computational methods in healthcare in order to improve prediction. We apply data assimilation in the context of Type 2 diabetes to understand…
Every year, humanity loses about 1.5 million persons due to diabetic disease. Therefore continuous monitoring of diabetes is highly needed, but the conventional approach, i.e., fingertip pricking, causes mental and physical pain to the…
This work proposes a smartphone video-based approach for the estimation of blood glucose in a non-invasive way. Videos using smartphone camera are collected from the tip of the subjects finger and the frames are subsequently converted into…
Chronic diseases such as diabetes pose significant management challenges, particularly due to the risk of complications like hypoglycemia, which require timely detection and intervention. Continuous health monitoring through wearable…
This article compares ten recently proposed neural networks and proposes two ensemble neural network-based models for blood glucose prediction. All of them are tested under the same dataset, preprocessing workflow, and tools using the…
Modeling the dynamics of probability distributions from time-dependent data samples is a fundamental problem in many fields, including digital health. The goal is to analyze how the distribution of a biomarker, such as glucose, changes over…
A task of vital clinical importance, within Diabetes management, is the prevention of hypo/hyperglycemic events. Increasingly adopted Continuous Glucose Monitoring (CGM) devices offer detailed, non-intrusive and real time insights into a…
Additive Gaussian process (GP) models offer flexible tools for modelling complex non-linear relationships and interaction effects among covariates. While most studies have focused on predictive performance, relatively little attention has…
Accurately predicting blood glucose (BG) levels of ICU patients is critical, as both hypoglycemia (BG < 70 mg/dL) and hyperglycemia (BG > 180 mg/dL) are associated with increased morbidity and mortality. This study presents a…
Precise glucose level monitoring is critical for people with diabetes to avoid serious complications. While there are several methods for continuous glucose level monitoring, research on maintenance devices is limited. To mitigate the gap,…
Burn-induced insulin resistance is associated with increased morbidity and mortality; however, the impact of burn injury on tissue-specific insulin sensitivity and its molecular mechanisms with consideration of insulin state remains unknown…
As we aim to control complex systems, use of a simulator in model-based reinforcement learning is becoming more common. However, it has been challenging to overcome the Reality Gap, which comes from nonlinear model bias and susceptibility…