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 …
Transfer learning in deep reinforcement learning: A survey
Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …
problems. Recent years have witnessed remarkable progress in reinforcement learning …
A reinforcement learning-based optimal control approach for managing an elective surgery backlog after pandemic disruption
H Xu, Y Fang, CA Chou, N Fard, L Luo - Health Care Management …, 2023 - Springer
Contagious disease pandemics, such as COVID-19, can cause hospitals around the world
to delay nonemergent elective surgeries, which results in a large surgery backlog. To …
to delay nonemergent elective surgeries, which results in a large surgery backlog. To …
[PDF][PDF] Design and implementation of a patient appointment and scheduling system
JL Akinode, SA Oloruntoba - Department of Computer Science …, 2017 - researchgate.net
The current health care landscape desired efficiency and patient satisfaction for optimal
performance. The outpatient of most clinics in developing countries are faced with plethora …
performance. The outpatient of most clinics in developing countries are faced with plethora …
NICE: Robust scheduling through reinforcement learning-guided integer programming
Integer programs provide a powerful abstraction for representing a wide range of real-world
scheduling problems. Despite their ability to model general scheduling problems, solving …
scheduling problems. Despite their ability to model general scheduling problems, solving …
Intelligent and convolutional-neural-network based smart hospital and patient scheduling system
K Rajakumari, M Madhunisha - 2020 International Conference …, 2020 - ieeexplore.ieee.org
Healthcare Management is the major concern in now-a-days to care about and the waiting
time for every hospitals or clinics are growing day by day. For avoiding this patient's waiting …
time for every hospitals or clinics are growing day by day. For avoiding this patient's waiting …
Reinforcement Learning Based Resource Management for CAR T-Cell Therapies
S Szentpéteri, KB Kis, P Egri, C Sanges, S Danhof… - Procedia CIRP, 2024 - Elsevier
This paper focuses on optimizing resource management strategies in chimeric antigen
receptor (CAR) T-cell therapies using reinforcement learning (RL). CAR T-cell therapy is an …
receptor (CAR) T-cell therapies using reinforcement learning (RL). CAR T-cell therapy is an …
An alternative to the black box: Strategy learning
S Taub, OS Pianykh - Plos one, 2022 - journals.plos.org
In virtually any practical field or application, discovering and implementing near-optimal
decision strategies is essential for achieving desired outcomes. Workflow planning is one of …
decision strategies is essential for achieving desired outcomes. Workflow planning is one of …
Learning-based Scheduling
SN Nayak - 2022 - dspace.mit.edu
Integer programs provide a powerful abstraction for representing a wide range of real-world
scheduling problems. Despite their ability to model general scheduling problems, solving …
scheduling problems. Despite their ability to model general scheduling problems, solving …