Reinforcement learning in healthcare: A survey
As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …
making by using interaction samples of an agent with its environment and the potentially …
Reinforcement learning for intelligent healthcare applications: A survey
Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …
Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial
G Wang, X Liu, Z Ying, G Yang, Z Chen, Z Liu… - Nature Medicine, 2023 - nature.com
The personalized titration and optimization of insulin regimens for treatment of type 2
diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based …
diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based …
The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care
Sepsis is the third leading cause of death worldwide and the main cause of mortality in
hospitals,–, but the best treatment strategy remains uncertain. In particular, evidence …
hospitals,–, but the best treatment strategy remains uncertain. In particular, evidence …
Artificial intelligence, bias and clinical safety
In medicine, artificial intelligence (AI) research is becoming increasingly focused on
applying machine learning (ML) techniques to complex problems, and so allowing …
applying machine learning (ML) techniques to complex problems, and so allowing …
Artificial intelligence in surgery: promises and perils
Objective: The aim of this review was to summarize major topics in artificial intelligence (AI),
including their applications and limitations in surgery. This paper reviews the key …
including their applications and limitations in surgery. This paper reviews the key …
A gentle introduction to reinforcement learning and its application in different fields
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …
become one of the most important and useful technology. It is a learning method where a …
[HTML][HTML] Artificial intelligence for diabetes management and decision support: literature review
I Contreras, J Vehi - Journal of medical Internet research, 2018 - jmir.org
Background Artificial intelligence methods in combination with the latest technologies,
including medical devices, mobile computing, and sensor technologies, have the potential to …
including medical devices, mobile computing, and sensor technologies, have the potential to …
Artificial intelligence and machine learning for improving glycemic control in diabetes: best practices, pitfalls, and opportunities
Objective: Artificial intelligence and machine learning are transforming many fields including
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …
Machine learning and smart devices for diabetes management: Systematic review
(1) Background: The use of smart devices to better manage diabetes has increased
significantly in recent years. These technologies have been introduced in order to make life …
significantly in recent years. These technologies have been introduced in order to make life …