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 …
Explainable artificial intelligence for predictive modeling in healthcare
CC Yang - Journal of healthcare informatics research, 2022 - Springer
The principle behind artificial intelligence is mimicking human intelligence in the way that it
can perform tasks, recognize patterns, or predict outcomes through learning from the …
can perform tasks, recognize patterns, or predict outcomes through learning from the …
Electronic health records based reinforcement learning for treatment optimizing
Abstract Electronic Health Records (EHRs) have become one of the main sources of
evidence to evaluate clinical actions, improve medical quality, predict disease-risk, and …
evidence to evaluate clinical actions, improve medical quality, predict disease-risk, and …
[HTML][HTML] Machine learning approaches for the discovery of clinical pathways from patient data: A systematic review
Background: Clinical pathways are sequences of events followed during the clinical care of
a group of patients who meet pre-defined criteria. They have many applications ranging from …
a group of patients who meet pre-defined criteria. They have many applications ranging from …
Reinforcement learning models and algorithms for diabetes management
With the advancements in reinforcement learning (RL), new variants of this artificial
intelligence approach have been introduced in the literature. This has led to increased …
intelligence approach have been introduced in the literature. This has led to increased …
Impartial feature selection using multi-agent reinforcement learning for adverse glycemic event prediction
We developed an attention model to predict future adverse glycemic events 30 min in
advance based on the observation of past glycemic values over a 35 min period. The …
advance based on the observation of past glycemic values over a 35 min period. The …
[HTML][HTML] Effective treatment recommendations for type 2 diabetes management using reinforcement learning: treatment recommendation model development and …
Background Type 2 diabetes mellitus (T2DM) and its related complications represent a
growing economic burden for many countries and health systems. Diabetes complications …
growing economic burden for many countries and health systems. Diabetes complications …
Optimal treatment strategies for critical patients with deep reinforcement learning
Personalized clinical decision support systems are increasingly being adopted due to the
emergence of data-driven technologies, with this approach now gaining recognition in …
emergence of data-driven technologies, with this approach now gaining recognition in …
Smart Imitator: Learning from Imperfect Clinical Decisions
Abstract Objectives This study introduces Smart Imitator (SI), a 2-phase reinforcement
learning (RL) solution enhancing personalized treatment policies in healthcare, addressing …
learning (RL) solution enhancing personalized treatment policies in healthcare, addressing …
A tutorial on reinforcement learning in selected aspects of communications and networking
Telecommunication systems are increasingly complex, dynamic, and heterogeneous. Tools
are needed to efficiently support and automate complex control and management …
are needed to efficiently support and automate complex control and management …