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

Type: Preprint

Publication Date: 2019-01-01

Citations: 8

DOI: https://doi.org/10.48550/arxiv.1910.04059

View

Locations

  • arXiv (Cornell University) - View
  • DataCite API - View

Similar Works

Action Title Year Authors
+ PDF Chat Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An <i>In Silico</i> Validation 2020 Taiyu Zhu
Kezhi Li
Pau Herrero
Pantelis Georgiou
+ Deep Reinforcement Learning for Closed-Loop Blood Glucose Control 2020 Ian Fox
Joyce M. Lee
Rodica Pop‐Busui
Jenna Wiens
+ Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments 2023 Anas El Fathi
Marc D. Breton
+ Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments 2023 Anas El Fathi
Marc D. Breton
+ Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning 2023 Wenzhou Lv
Tianyu Wu
Luolin Xiong
Liang Wu
Zhou Jian
Yang Tang
Qi Feng
+ Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology 2023 Mehrad Jalolia
Marzia Cescon
+ PDF Chat Hybrid Control Policy for Artificial Pancreas Via Ensemble Deep Reinforcement Learning 2024 Wenzhou Lv
Tianyu Wu
Luolin Xiong
Liang Wu
Jian Zhou
Yang Tang
Feng Qian
+ PDF Chat An Improved Strategy for Blood Glucose Control Using Multi-Step Deep Reinforcement Learning 2024 Weiwei Gu
Senquan Wang
+ PDF Chat Attention Networks for Personalized Mealtime Insulin Dosing in People with Type 1 Diabetes 2024 Anas El Fathi
Elliott Pryor
Marc D. Breton
+ PDF Chat A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning 2018 Qingnan Sun
Marko V. Jankovic
Joao Budzinski
Brett L. Moore
Peter Diem
Christoph Stettler
Stavroula Mougiakakou
+ Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control 2020 Taku Yamagata
Aisling Ann O’Kane
Amid Ayobi
Dmitri Katz
Katarzyna Stawarz
Paul Marshall
Peter Flach
RaĂșl Santos‐RodrĂ­guez
+ Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control 2020 Taku Yamagata
Aisling Ann O’Kane
Amid Ayobi
Dmitri Katz
Katarzyna Stawarz
Paul Marshall
Peter Flach
RaĂșl Santos‐RodrĂ­guez
+ PDF Chat Basal-bolus advisor for type 1 diabetes (T1D) patients using multi-agent reinforcement learning (RL) methodology 2023 Mehrad Jaloli
Marzia Cescon
+ Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes 2023 Harry Emerson
Matthew Guy
Ryan McConville
+ Reinforcement Learning-Based Adaptive Insulin Advisor for Individuals with Type 1 Diabetes Patients under Multiple Daily Injections Therapy 2019 Qingnan Sun
Marko V. Jankovic
Stavroula Mougiakakou
+ PDF Chat Reinforcement Learning-Based Adaptive Insulin Advisor for Individuals with Type 1 Diabetes Patients under Multiple Daily Injections Therapy 2019 Qingnan Sun
Marko V. Jankovic
Stavroula Mougiakakou
+ Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes 2022 Harry Emerson
Matt Guy
Ryan McConville
+ PDF Chat DIETS: Diabetic Insulin Management System in Everyday Life 2024 Hui Zeng
Hui Ji
Pengfei Zhou
+ Challenging common bolus advisor for self-monitoring type-I diabetes patients using Reinforcement Learning 2020 Frédéric Logé
Erwan Le Pennec
Habiboulaye Amadou-Boubacar
+ Challenging common bolus advisor for self-monitoring type-I diabetes patients using Reinforcement Learning 2020 Frédéric Logé
Erwan Le Pennec
Habiboulaye Amadou-Boubacar