Authors :
P. Adi Lakshmi; Anitha Kolipakula; Sathvik Saran Atchukolu; Rudra Manikanta Abburi; Bhargavi Chadalavada
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/nvfarnx7
Scribd :
https://tinyurl.com/mwtte7se
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR505
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
This project introduces a pioneering approach
for optimizing drug dosage control strategies through the
utilization of reinforcement learning (RL), a sophisticated
subset of machine learning techniques. The core objective
is to dynamically adjust drug dosages in real-time based
on patient responses, thereby maximizing therapeutic
efficacy while minimizing potential adverse effects. By
integrating reinforcement learning algorithms, including
Q-learning, Deep Q-Networks (DQN), and actor-critic
methods, the system learns from patient data to make
precise dosage adjustments considering individual patient
characteristics, disease progression, and response to
treatment. The framework promises to revolutionize
personalized medicine by providing tailored drug
dosages, enhancing treatment outcomes, and ensuring
patient safety. The project's scope covers not only the
development and implementation of this innovative RL-
based system but also addresses significant challenges
such as model interpretability, scalability, and regulatory
compliance, ensuring its practical applicability in
healthcare settings. Through this work, we aim to bridge
the gap between conventional drug prescription
methodologies and the potential for personalized,
optimized care, making a substantial contribution to the
advancement of healthcare systems.
Keywords :
Precision Medicine, Reinforcement Learning, Drug Dosage Control, Personalized Healthcare, Machine Learning.
This project introduces a pioneering approach
for optimizing drug dosage control strategies through the
utilization of reinforcement learning (RL), a sophisticated
subset of machine learning techniques. The core objective
is to dynamically adjust drug dosages in real-time based
on patient responses, thereby maximizing therapeutic
efficacy while minimizing potential adverse effects. By
integrating reinforcement learning algorithms, including
Q-learning, Deep Q-Networks (DQN), and actor-critic
methods, the system learns from patient data to make
precise dosage adjustments considering individual patient
characteristics, disease progression, and response to
treatment. The framework promises to revolutionize
personalized medicine by providing tailored drug
dosages, enhancing treatment outcomes, and ensuring
patient safety. The project's scope covers not only the
development and implementation of this innovative RL-
based system but also addresses significant challenges
such as model interpretability, scalability, and regulatory
compliance, ensuring its practical applicability in
healthcare settings. Through this work, we aim to bridge
the gap between conventional drug prescription
methodologies and the potential for personalized,
optimized care, making a substantial contribution to the
advancement of healthcare systems.
Keywords :
Precision Medicine, Reinforcement Learning, Drug Dosage Control, Personalized Healthcare, Machine Learning.
