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Comparative Study of Multi-Armed Bandit Algorithms in Clinical Trials
- 1 Universtiy of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
- 2 Qingdao Hengxing University of Science and Technology, 588 Jiushui East Street, Qingdao, Shandong, China
- 3 University of Washington, 1410 NE Campus Pkwy, Seattle, WA 98195
- 4 Universtiy of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093
* Author to whom correspondence should be addressed.
Abstract
In recent years, with the rapid development of the information age, the influence of Multi Armed Bandit Algorithms (MAB) models in clinical trials for disease prevention has been increasing. In this study, based on Python programming language, Multi-Armed Bandit Algorithms (MAB) algorithm, Upper Confidence Bound (UCB) algorithm, Adaptive Epsilon-Greedy Algorithm, and Thompson Sampling (TS) algorithms to validate the idea of preventing, controlling and predicting the occurrence of diseases. The results show that the MAB model can effectively solve various decision-making problems in clinical trials, improve the efficiency of access to medical care, save doctors ‘diagnosis time, and at the same time achieve the prevention and treatment of diseases while minimising patients’ pain. This study is dedicated to proposing a more effective decision-making method and verifies that the method has a wide range of applications and great potential for development today.
Keywords
Multi-Armed Bandit Algorithms, Python, Clinical Trial
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Cite this article
Huang,W.;Wang,W.;Wu,Y.;Xi,C. (2024). Comparative Study of Multi-Armed Bandit Algorithms in Clinical Trials. Applied and Computational Engineering,83,45-51.
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 CONF-MLA 2024 Workshop: Semantic Communication Based Complexity Scalable Image Transmission System for Resource Constrained Devices
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