Reinforcement learning application in diabetes blood glucose control: A systematic review
M Tejedor, AZ Woldaregay, F Godtliebsen - Artificial intelligence in …, 2020 - Elsevier
Background Reinforcement learning (RL) is a computational approach to understanding and
automating goal-directed learning and decision-making. It is designed for problems which …
automating goal-directed learning and decision-making. It is designed for problems which …
Advanced diabetes management using artificial intelligence and continuous glucose monitoring sensors
Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of
type 1 diabetes (T1D). These sensors provide in real-time, every 1–5 min, the current blood …
type 1 diabetes (T1D). These sensors provide in real-time, every 1–5 min, the current blood …
[PDF][PDF] ISPAD clinical practice consensus guidelines 2018: diabetes technologies
JL Sherr, M Tauschmann, T Battelino, M de Bock… - Pediatr Diabetes, 2018 - bnsde.org
ISPAD Clinical Practice Consensus Guidelines 2018: Diabetes technologies Page 1 ISPAD
CLINICAL PRACTICE CONSENSUS GUIDELINES ISPAD Clinical Practice Consensus …
CLINICAL PRACTICE CONSENSUS GUIDELINES ISPAD Clinical Practice Consensus …
Preventing undesirable behavior of intelligent machines
Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple
data analysis and pattern recognition tools to complex systems that achieve superhuman …
data analysis and pattern recognition tools to complex systems that achieve superhuman …
Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment
Worldwide, the number of people affected by diabetes is rapidly increasing due to aging
populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in …
populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in …
Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation
People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain
their blood glucose concentration in a therapeutically adequate target range. Although the …
their blood glucose concentration in a therapeutically adequate target range. Although the …
Smartphone-based technology in diabetes management
J Doupis, G Festas, C Tsilivigos, V Efthymiou… - Diabetes Therapy, 2020 - Springer
Diabetes is a group of metabolic disorders characterized by elevated levels of blood glucose
which leads over time to serious complications and significant morbidity and mortality …
which leads over time to serious complications and significant morbidity and mortality …
The review of insulin pens—past, present, and look to the future
M Masierek, K Nabrdalik, O Janota… - Frontiers in …, 2022 - frontiersin.org
Currently, there are about 150–200 million diabetic patients treated with insulin globally. The
year 2021 is special because the 100th anniversary of the insulin discovery is being …
year 2021 is special because the 100th anniversary of the insulin discovery is being …
ReplayBG: a digital twin-based methodology to identify a personalized model from type 1 diabetes data and simulate glucose concentrations to assess alternative …
Objective: Design and assessment of new therapies for type 1 diabetes (T1D) management
can be greatly facilitated by in silico simulations. The ReplayBG simulation methodology …
can be greatly facilitated by in silico simulations. The ReplayBG simulation methodology …
[HTML][HTML] Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes
The widespread adoption of effective hybrid closed loop systems would represent an
important milestone of care for people living with type 1 diabetes (T1D). These devices …
important milestone of care for people living with type 1 diabetes (T1D). These devices …