AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (364 KB)
Submit Manuscript AI Chat Paper
Show Outline
Show full outline
Hide outline
Show full outline
Hide outline
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
Show Author Information


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.


E. S. Berner, Clinical Decision Support Systems: Theory and Practice. New York, NY, USA: Springer, 2007.

D. Tawadrous, S. Z. Shariff, R. B. Haynes, A. V. Iansavichus, A. K. Jain, and A. X. Garg, Use of clinical decision support systems for kidney-related drug prescribing: A systematic review, Am. J. Kidney Dis., vol. 58, no. 6, pp. 903–914, 2011.


C. Palleria, A. D. Paolo, C. Giofrè, C. Caglioti, G. Leuzzi, A. Siniscalchi, G. D. Sarro, and L. Gallelli, Pharmacokinetic drug-drug interaction and their implication in clinical management, J. Res. Med. Sci., vol. 18, no. 7, p. 601, 2013.

R. C. Chen, J. Y. Chiu, and C. T. Batj, The recommendation of medicines based on multiple criteria decision making and domain ontology—an example of anti-diabetic medicines, in Proc. 2011 Int. Conf. Machine Learning and Cybernetics, Guilin, China, 2011, pp. 27–32.

S. M. Chen, Y. H. Huang, and R. C. Chen, A recommendation system for anti-diabetic drugs selection based on fuzzy reasoning and ontology techniques, Int. J. Patt. Recogn. Artif. Intell., vol. 27, no. 4, p. 1359001, 2013.

N. Mahmoud and H. Elbeh, IRS-T2D: Individualize recommendation system for type2 diabetes medication based on ontology and SWRL, in Proc. 10th Int. Conf. Informatics and Systems, Giza, Egypt, 2016, pp. 203–209.

H. Liu, G. Xie, J. Mei, W. Shen, W. Sun, and X. Li, An efficacy driven approach for medication recommendation in type 2 diabetes treatment using data mining techniques, Stud. Heath. Technol. Inform., vol. 192, p. 1071, 2013.

M. A. Wedagu, D. Chen, M. A. I. Hussain, T. Gebremeskel, M. T. Orlando, and A. Manzoor, Medicine recommendation system for diabetes using prior medical knowledge, in Proc. 2020 4th Int. Conf. Vision, Image and Signal Processing, Bangkok, Thailand, 2020, pp. 1–5.
M. Balvert, G. Patoulidis, A. Patti, T. M. Deist, C. Eyler, B. E. Dutilh, A. Schönhuth, and D. Craft, A drug recommendation system (Dr. S) for cancer cell lines, arXiv preprint arXiv: 1912.11548, 2019.

R. Su, Y. Huang, D. G. Zhang, G. Xiao, and L. Wei, SRDFM: Siamese response deep factorization machine to improve anti-cancer drug recommendation, Brief. Bioinform., vol. 23, no. 2, p. bbab534, 2022.

C. Chen, L. Zhang, X. Fan, Y. Wang, C. Xu, and R. Liu, A epilepsy drug recommendation system by implicit feedback and crossing recommendation, in Proc. 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Guangzhou, China, 2018, pp. 1134–1139.

D. Chen, D. Jin, T. T. Goh, N. Li, and L. Wei, Context-awareness based personalized recommendation of anti-hypertension drugs, J. Med. Syst., vol. 40, no. 9, pp. 1–10, 2016.


M. Sajde, H. Malek, and M. Mohsenzadeh, RecoMed: A knowledge-aware recommender system for hypertension medications, Inform. Med. Unlocked, vol. 30, p. 100950, 2022.

Y. Liu, B. Logan, N. Liu, Z. Xu, J. Tang, and Y. Wang, Deep reinforcement learning for dynamic treatment regimes on medical registry data, in Proc. 2017 IEEE Int. Conf. Healthcare Informatics (ICHI), Park City, UT, USA, 2017, pp. 380–385.

L. Verboven, T. Calders, S. Callens, J. Black, G. Maartens, K. E. Dooley, S. Potgieter, R. M. Warren, K. Laukens, and A. V. Rie, A treatment recommender clinical decision support system for personalized medicine: Method development and proof-of-concept for drug resistant tuberculosis, BMC Med. Inform. Decis. Mak., vol. 22, no. 1, p. 56, 2022.

Y. Zhang, R. Chen, J. Tang, W. F. Stewart, and J. Sun, LEAP: Learning to prescribe effective and safe treatment combinations for multimorbidity, in Proc. 23rd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Halifax, Canada, 2017, pp. 1315–1324.
L. Wang, W. Zhang, X. He, and H. Zha, Personalized prescription for comorbidity, in Proc. 23rd Int. Conf. Database Systems for Advanced Applications, Gold Coast, Australia, 2018, pp. 3–19.
S. Wang, P. Ren, Z. Chen, Z. Ren, J. Ma, and M. D. Rijke, Order-free medicine combination prediction with graph convolutional reinforcement learning, in Proc. 28th ACM Int. Conf. Information and Knowledge Management, Beijing, China, 2019, pp. 1623–1632.

J. Shang, C. Xiao, T. Ma, H. Li, and J. Sun, GAMENet: Graph augmented memory networks for recommending medication combination, Proc. AAAI Conf. Artif. Intell., vol. 33, no. 1, pp. 1126–1133, 2019.

S. Wang, SeqMed: Recommending medication combination with sequence generative adversarial nets, in Proc. 2020 IEEE Int. Conf. Bioinformatics and Biomedicine (BIBM), Seoul, Republic of Korea, 2021, pp. 2664–2671.
L. Montalvo and E. Villanueva, Drug recommendation system for geriatric patients based on Bayesian networks and evolutionary computation, in Proc. 3rd Int. Conf. Intelligent Human Systems Integration, Modena, Italy, 2020, pp. 492–497.
C. Yang, C. Xiao, F. Ma, L. Glass, and J. Sun, SafeDrug: Dual molecular graph encoders for recommending effective and safe drug combinations, arXiv preprint arXiv: 2105.02711, 2021.
C. Yang, C. Xiao, L. Glass, and J. Sun, Change matters: Medication change prediction with recurrent residual networks, arXiv preprint arXiv: 2105.01876, 2021.
R. Wu, Z. Qiu, J. Jiang, G. Qi, and X. Wu, Conditional generation net for medication recommendation, in Proc. ACM Web Conference 2022, Virtual Event, 2022, pp. 935–945.

Y. Zhang, D. Zhang, M. M. Hassan, A. Alamri, and L. Peng, CADRE: Cloud-assisted drug recommendation service for online pharmacies, Mob. Netw. Appl., vol. 20, no. 3, pp. 348–355, 2015.


F. Gong, M. Wang, H. Wang, S. Wang, and M. Liu, SMR: Medical knowledge graph embedding for safe medicine recommendation, Big Data Res., vol. 23, p. 100174, 2021.

L. Wang, W. Zhang, X. He, and H. Zha, Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation, in Proc. 24th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, London, UK, 2018, pp. 2447–2456.
J. Shang, T. Ma, C. Xiao, and J. Sun, Pre-training of graph augmented transformers for medication recommendation, arXiv preprint arXiv: 1906.00346, 2019.
S. Li, F. Hao, M. Li, and H. C. Kim, Medicine rating prediction and recommendation in mobile social networks, in Proc. 8th Int. Conf. Grid and Pervasive Computing, Seoul, Republic of Korea, 2013, pp. 216–223.
S. Garg, Drug recommendation system based on sentiment analysis of drug reviews using machine learning, in Proc. 2021 11th Int. Conf. Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2021, pp. 175–181.
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.










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 (