Step‐adjusted tree‐based reinforcement learning for evaluating nested dynamic treatment regimes using test‐and‐treat observational data

2021 ◽  
Author(s):  
Ming Tang ◽  
Lu Wang ◽  
Michael A. Gorin ◽  
Jeremy M. G. Taylor
Keyword(s):  
Reinforcement Learning ◽  
Observational Data ◽  
Test And Treat ◽  
Treatment Regimes ◽  
Dynamic Treatment Regimes ◽  
Dynamic Treatment

scholarly journals Tree-based reinforcement learning for estimating optimal dynamic treatment regimes

2018 ◽  
Vol 12 (3) ◽  
pp. 1914-1938 ◽  
Author(s):  
Yebin Tao ◽  
Lu Wang ◽  
Daniel Almirall
Keyword(s):  
Reinforcement Learning ◽  
Treatment Regimes ◽  
Dynamic Treatment Regimes ◽  
Dynamic Treatment ◽  
Optimal Dynamic

Chapter 16: Practical reinforcement learning in dynamic treatment regimes

2015 ◽  
pp. 263-296 ◽  
Author(s):  
Robert D. Vincent ◽  
Joelle Pineau ◽  
Norma Ybarra ◽  
Issam El Naqa
Keyword(s):  
Reinforcement Learning ◽  
Treatment Regimes ◽  
Dynamic Treatment Regimes ◽  
Dynamic Treatment

scholarly journals Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data

2017 ◽  
Author(s):  
Ying Liu ◽  
Brent Logan ◽  
Ning Liu ◽  
Zhiyuan Xu ◽  
Jian Tang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Registry Data ◽  
Treatment Regimes ◽  
Dynamic Treatment Regimes ◽  
Dynamic Treatment ◽  
Medical Registry

scholarly journals Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV

2019 ◽  
Vol 19 (S2) ◽  
Author(s):  
Chao Yu ◽  
Yinzhao Dong ◽  
Jiming Liu ◽  
Guoqi Ren
Keyword(s):  
Reinforcement Learning ◽  
Causal Factors ◽  
Treatment Regimes ◽  
Dynamic Treatment Regimes ◽  
Dynamic Treatment

scholarly journals Reinforcement Learning in Healthcare: A Survey

2023 ◽  
Vol 55 (1) ◽  
pp. 1-36
Author(s):  
Chao Yu ◽  
Jiming Liu ◽  
Shamim Nemati ◽  
Guosheng Yin
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Future Research ◽  
Scheduling Problems ◽  
Sequential Decision ◽  
Suitable Candidate ◽  
Treatment Regimes ◽  
Dynamic Treatment Regimes ◽  
Dynamic Treatment ◽  
Theoretical Foundations

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 delayed feedbacks. In contrast to traditional supervised learning that typically relies on one-shot, exhaustive, and supervised reward signals, RL tackles sequential decision-making problems with sampled, evaluative, and delayed feedbacks simultaneously. Such a distinctive feature makes RL techniques a suitable candidate for developing powerful solutions in various healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged period with delayed feedbacks. By first briefly examining theoretical foundations and key methods in RL research, this survey provides an extensive overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis, and many other control or scheduling problems that have infiltrated every aspect of the healthcare system. In addition, we discuss the challenges and open issues in the current research and highlight some potential solutions and directions for future research.

scholarly journals A smoothed Q ‐learning algorithm for estimating optimal dynamic treatment regimes

2018 ◽  
Vol 46 (2) ◽  
pp. 446-469
Author(s):  
Yanqin Fan ◽  
Ming He ◽  
Liangjun Su ◽  
Xiao‐Hua Zhou
Keyword(s):  
Learning Algorithm ◽  
Q Learning ◽  
Treatment Regimes ◽  
Dynamic Treatment Regimes ◽  
Dynamic Treatment ◽  
Optimal Dynamic
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