Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 5 , 31 May 2023


Open Access | Article

Analysis of reinforce learning in medical treatment

Ningyan Zhang * 1
1 University of California, Irvine, CA, 92697

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 5, 48-53
Published 31 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Ningyan Zhang. Analysis of reinforce learning in medical treatment. ACE (2023) Vol. 5: 48-53. DOI: 10.54254/2755-2721/5/20230527.

Abstract

As human approaches the big data period, artificial intelligence becomes dominating in almost every domain. As part of machine learning, reinforcement learning (RL) is intended to utilize mutual communication experiences around the world and assess feedback to strengthen human ability in decision-making. Unlike traditional supervised learning, RL is able to sample, assess and order the delayed feedback decision-making at the same time. This characteristic of RL makes it powerful when it comes to exploring a solution in the medical field. This paper investigates the wide application of RL in the medical field. Including two major parts of the medical field: artificial diagnosis and precision medicine, this paper first introduces several algorithms of RL in each part, then states the inefficiency and unsolved difficulty in this area, together with the future investigation direction of RL. This paper provides researchers with multiple feasible algorithms, supported methods and theoretical analysis, which pave the way for future development of reinforcement learning in medical field.

Keywords

Artificial diagnosis, Precise Medicine, Reinforcement learning, Upper confidence bound, Thompson sampling.

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-57-7
ISBN (Online)
978-1-915371-58-4
Published Date
31 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/5/20230527
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated