and medical settings (Yu, Liu, Nemati & Yin, 2021).
Additionally, data privacy and security are crucial
research directions, especially when using distributed
learning methods like federated learning (Otoum et
al. 2021).
To address these research gaps, recent studies
have proposed several improvements. For instance,
Transitional Variational Autoencoders (tVAEs) are
used to generate more realistic patient trajectories,
enhancing the model's ability to simulate patient data.
Furthermore, RL frameworks combined with
federated learning are explored to strengthen data
protection and confidentiality measures in healthcare
IoT systems. This method allows model training
without sharing raw data, thereby protecting patient
privacy (Otoum et al. 2021).
This paper endeavours to offer an exhaustive
analysis of the main research findings and recent
advancements in the application of RL in healthcare.
This paper explores a wide range of RL applications,
including dynamic treatment regimes, automated
medical diagnosis, and healthcare resource
management. The review includes a detailed
examination of existing models, their limitations, and
the innovative solutions proposed to address these
challenges. Additionally, this review is structured to
explore the different facets and impacts of RL in
healthcare, With the aspiration of offering an all-
encompassing overview pertaining to the current
framework and prospective developments of RL
research in this significant sector.
2 METHODS
2.1 Introduction to Reinforcement
Learning
Reinforcement learning, representing a sophisticated
branch within the broader domain of machine
learning, emphasizes enabling an agent to learn
optimal decision-making through interactions with its
environment. The fundamental principle involves the
agent performing actions in different states with the
aim of optimizing aggregate rewards over an
extended period. Through the acquisition of feedback
in the guise of rewards or penalties corresponding to
its actions, the agent is steered towards formulating
an optimal policy.
Reinforcement Learning challenges are
commonly framed within the construct of Markov
Decision Processes (MDPs), which are defined by the
following key tuple ๐, ๐ด, ๐, ๐
, ๐พ (Amparore et al.,
2013):
- ๐ : A set of states
- ๐: A set of actions
- ๐ : A transition likelihood matrix ๐(๐ โฒ|๐ , ๐) ,
defining the likelihood of shifting to states from state
๐ after executing an action ๐.
- ๐
: A reward function ๐
(๐ , ๐) , providing the
instantaneous reward after action ๐ in state ๐ .
- ๐พ : A discount factor gamma in [0, 1], which
quantifies the significance of future rewards.
The agent's paramount goal is to devise a
strategy ๐(๐ )
that maximizes the anticipated
aggregated reward, commonly known as the return.
This return is calculated as the sum of discounted
rewards accrued over time, reflecting both immediate
and future benefits:
๐บ
๎ฏง
= ๐
๎ฏง๎ฌพ๎ฌต
+ ๐พ๐
๎ฏง๎ฌพ๎ฌถ
+ ๐พ
๎ฌถ
๐
๎ฏง๎ฌพ๎ฌท
+ โฏ =
โ
๐พ
๎ฏ
๐
๎ฏง๎ฌพ๎ฏ๎ฌพ๎ฌต
๎ฎถ
๎ฏ๎ญ๎ฌด
(1)
A commonly employed algorithm in RL is Q-
learning, which updates the value of state-action pairs
(Q-values) using the Bellman equation:
๐(๐ , ๐) โ๐(๐ , ๐)+๐ผ[๐
+ ๐พ๐๐๐ฅ
๎ฏ๏ฑ
๐(๐ โฒ, ๐โฒ) โ
๐(๐ , ๐)] (2)
where ๐ผ is the learning rate.
Through continuous interaction with its
environment, the agent progressively learns to select
actions that maximize long-term cumulative rewards.
This ability renders RL especially effective for
handling complex decision-making tasks across
various fields, such as healthcare.
2.2 Dynamic Treatment Regimes
2.2.1 Enhancing DRL with Transitional
Variational Autoencoders
In their study, Baucum and Khojandi introduce
Transitional Variational Autoencoders (tVAEs) to
improve Deep Reinforcement Learning (DRL)
applications in healthcare. Temporal Variational
Autoencoders (tVAEs) are sophisticated generative
neural network models designed to create an explicit
correlation between the configurations of clinical
parameters across sequential temporal intervals,
utilizing retrospective patient data. These models
facilitate the accurate reconstruction of temporal
patterns in clinical datasets by capturing the
underlying distributional dynamics over time. One
significant benefit of the tVAE model is its minimal
reliance on distributional assumptions while
maintaining consistent training and testing
architectures. By utilizing tVAEs, researchers can
create more realistic patient trajectories, facilitating
the development of effective treatment policies
(Baucum et al., 2020).