Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments

A El Fathi, MD Breton - IFAC-PapersOnLine, 2023 - Elsevier
People with type 1 diabetes (T1D) struggle to calculate the optimal insulin dose at mealtime,
especially when under multiple daily injections (MDI) therapy. Effectively, they will not …

Tinjauan Supervised Reinforcement Learning pada Tindakan Medis Penyakit Diabetes Melitus: Review of Supervised Reinforcement Learning on Medical Actions for …

IP Putri, D Marcelina, E Yulianti - MALCOM: Indonesian Journal of …, 2024 - journal.irpi.or.id
Diabetes Melitus (DM) merupakan penyakit kronis yang memerlukan pengelolaan medis
yang berkelanjutan. Pengelolaan pengendalian penyakit diabetes bergantung pada kadar …

[PDF][PDF] Artificial intelligence and machine learning for improving glycemic control in diabetes: best practices, pitfalls and opportunities

AC Breton, K Nikita, F Doyle Jr, TB Jorge Bondia III… - 2023 - researchgate.net
Objective: Artificial intelligence and machine learning are transforming many fields including
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …

[引用][C] Reinforcement Learning-based Artificial Pancreas Systems to Automate Treatment in Type 1 Diabetes

C Hettiarachchi - 2023 - The Australian National University

Artificial Intelligence Models for the Management of Type 1 Diabetes

F D'Antoni - 2023 - iris.unicampus.it
Abstract Type 1 Diabetes mellitus (T1D) is a chronic metabolic disease due to which the
pancreas is not able to produce an adequate amount of insulin, resulting in an increased …

Machine Learning Based Techniques for the Design of Personalized Insulin Bolus Calculators in Type 1 Diabetes Therapy

G Noaro - 2023 - research.unipd.it
Negli ultimi anni, l'incidenza e la prevalenza del diabete di tipo 1 (T1D) sono in aumento in
tutto il mondo. Oltre all'onere economico legato al T1D, la gestione e il trattamento di questa …

Insights into the Application of Deep Reinforcement Learning in Healthcare and Materials Science

BR Smith - 2023 - trace.tennessee.edu
Reinforcement learning (RL) is a type of machine learning designed to optimize sequential
decision-making. While controlled environments have served as a foundation for RL …

[PDF][PDF] Uncovering Bias in Reinforcement Learning for Heparin Treatment Planning

B Smith, A Khojandi, R Vasudevan, NI Shafi, R Davis - 2023 - researchgate.net
Dynamic treatment policies have the potential to greatly improve medical outcomes for
patients. Algorithmic bias has not been fully explored for its impact on data-driven temporal …

Artificial intelligence-based analysis of small data sets in medicine

D Mikołajewski, E Mikołajewska - Studia i Materiały Informatyki …, 2023 - yadda.icm.edu.pl
AI-based computing of small data sets are a step towards edge computing and further
personalization of diagnostics, therapy and predictions in clinical practice. However, this still …

[PDF][PDF] Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems. Bioengineering 2022, 9, 664

J de Farias, WM Bessa - 2022 - utupub.fi
Type 1 diabetes mellitus is a disease that affects millions of people around the world. Recent
progress in embedded devices has allowed the development of artificial pancreas that can …