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Open Access

A Survey of Personalized Medicine Recommendation

Fanglin Zhu1Lizhen Cui1,2( )Yonghui Xu2( )Zhe Qu1Zhiqi Shen3
School of Software, Shandong University, Jinan 250101, China
Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
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Abstract

Mining potential and valuable medical knowledge from massive medical data to support clinical decision-making has become an important research field. Personalized medicine recommendation is an important research direction in this field, aiming to recommend the most suitable medicines for each patient according to the health status of the patient. Personalized medicine recommendation can assist clinicians to make clinical decisions and avoid the occurrence of medical abnormalities, so it has been widely concerned by many researchers. Based on this, this paper makes a comprehensive review of personalized medicine recommendation. Specifically, we first make clear the definition of personalized medicine recommendation problem; then, starting from the key theories and technologies, the personalized medicine recommendation algorithms proposed in recent years are systematically classified (medicine recommendation based on multi-disease, medicine recommendation with combination pattern, medicine recommendation with additional knowledge, and medicine recommendation based on feedback) and in-depth analyzed; and this paper also introduces how to evaluate personalized medicine recommendation algorithms and some common evaluation indicators; finally, the challenges of personalized medicine recommendation problem are put forward, and the future research direction and development trends are prospected.

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International Journal of Crowd Science
Pages 77-82
Cite this article:
Zhu F, Cui L, Xu Y, et al. A Survey of Personalized Medicine Recommendation. International Journal of Crowd Science, 2024, 8(2): 77-82. https://doi.org/10.26599/IJCS.2023.9100013

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Received: 03 January 2023
Revised: 26 July 2023
Accepted: 03 August 2023
Published: 14 May 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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