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Analysis of reinforce learning in medical treatment
- 1 University of California, Irvine, CA, 92697
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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.
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Cite this article
Zhang,N. (2023). Analysis of reinforce learning in medical treatment. Applied and Computational Engineering,5,48-53.
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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
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