activity
20182022
most citedBasal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation

115 citations · 165 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CY2022★ 14 cited

Developing moral AI to support antimicrobial decision making

William J Bolton, Cosmin Badea, Pantelis Georgiou +2

Artificial intelligence (AI) assisting with antimicrobial prescribing raises significant moral questions. Utilising ethical frameworks alongside AI-driven systems, while considerin…

cs.CY2020★ 1 cited

A novel hand-held interface supporting the self-management of Type 1 diabetes

Robert Spence, Chukwuma Uduku, Kezhi Li +2

The paper describes the interaction design of a hand-held interface supporting the self-management of Type 1 diabetes. It addresses well-established clinical and human-computer int…

eess.SP2020★ 115 cited

Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation

Taiyu Zhu, Kezhi Li, Pau Herrero +1

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 t…

q-bio.QM2019★ 9 cited

A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning

Taiyu Zhu, Kezhi Li, Pantelis Georgiou

We propose a dual-hormone delivery strategy by exploiting deep reinforcement learning (RL) for people with Type 1 Diabetes (T1D). Specifically, double dilated recurrent neural netw…

q-bio.TO2018★ 5 cited

Enhancing Blood Glucose Prediction with Meal Absorption and Physical Exercise Information

Chengyuan Liu, Josep Vehi, Nick Oliver +2

Objective: Numerous glucose prediction algorithm have been proposed to empower type 1 diabetes (T1D) management. Most of these algorithms only account for input such as glucose, in…

cs.CV2018★ 15 cited

Convolutional Recurrent Neural Networks for Glucose Prediction

Kezhi Li, John Daniels, Chengyuan Liu +2

Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with Type 1 diabetes mellitus (T1DM) such as the artificial pancr…

physics.app-ph2018★ 6 cited

Body Dust: Miniaturized Highly-integrated Low Power Sensing for Remotely Powered Drinkable CMOS Bioelectronics

Sandro Carrara, Pantelis Georgiou

The aim of this paper is to introduce current advances in technology that could enable the development of fully drinkable and autonomous bio-electronic CMOS sensors in the form of…