Vol. 5 No. 1 (2025): Issue 5
Articles

Personalized Medicine Recommendation using Matrix Factorization-based Collaborative Filtering

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  • Rina Sari,
  • Erik Nilsson,
  • Agus Pramono,
  • Dewi Lestari
Rina Sari
Faculty of Health Science and Technology, Universitas Muhammadiyah Surakarta, Surakarta, 57102, Indonesia
Agus Pramono
Institute for Advanced Health Studies, Universitas Islam Indonesia, Yogyakarta, 55584, Indonesia
Dewi Lestari
Institute for Advanced Health Studies, Universitas Islam Indonesia, Yogyakarta, 55584, Indonesia

Published 2025-05-19

Keywords

  • Personalized Medicine,
  • Healthcare Outcomes,
  • Collaborative Filtering,
  • Matrix Factorization,
  • Patient-Drug Interactions

How to Cite

Sari, R., Nilsson, E., Pramono, A., & Lestari, D. (2025). Personalized Medicine Recommendation using Matrix Factorization-based Collaborative Filtering. Generative Artificial Intelligence Research, 5(1). Retrieved from https://ojs.mri-pub.com/index.php/GAIR/article/view/132

Abstract

Personalized medicine, tailored to individual characteristics, has emerged as a promising approach to improve healthcare outcomes. However, the vast amount of available medical data poses challenges for effective treatment recommendations. Current research in personalized medicine recommendation predominantly relies on collaborative filtering techniques, which face limitations in accurately capturing the complex relationships within medical datasets. This paper addresses this issue by proposing a novel approach based on matrix factorization. Our innovative method enhances the accuracy and efficiency of personalized medicine recommendation by effectively modeling intricate patient-drug interactions. By integrating patient-specific data with drug characteristics, our approach demonstrates superior performance in recommending personalized treatments. This paper contributes to the advancement of personalized medicine by providing a robust and effective recommendation framework based on matrix factorization.

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References

  1. F. Zhu et al., "A Survey of Personalized Medicine Recommendation," International Journal of Crowd Science, vol. 8, pp. 77-82, 2024.
  2. H. Zhou et al., "TriGCN: Graph Convolution Network Based on Tripartite Graph for Personalized Medicine Recommendation System," Syst., vol. 12, p. 398, 2024.
  3. W. Lon, "Personalized Medicine Recommendation Based on Tensor Decomposition," Computer Science, 2015.
  4. K. Shankar et al., "An Effective-Based Personalized Medicine Recommendation System Using an Ensemble of Extreme Learning Machine Model," pp. 59-70, 2021.
  5. A. Martínez-Rodrigo et al., "Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing," Health Information Science and Systems, vol. 12, pp. 1-16, 2024.
  6. J. Liu et al., "Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation," arXiv.org, vol. abs/2305.17866, 2023.
  7. V. L. Nguyen, T. Q. Vo, and H. T. T. Nguyen, "Dynamic matrix factorization-based collaborative filtering in movie recommendation services," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, 2024.
  8. Y. Terui et al., "Collaborative filtering based on nonnegative/binary matrix factorization," ArXiv, vol. abs/2410.10381, 2024.
  9. Y. Sun and Q. Liu, "Collaborative filtering recommendation based on K-nearest neighbor and non-negative matrix factorization algorithm," J. Supercomput., vol. 81, p. 79, 2024.
  10. Y. Zhang et al., "Hybrid Algorithm for Item Collaborative Filtering Based on Matrix Factorization," 2023 4th Information Communication Technologies Conference (ICTC), pp. 276-284, 2023.
  11. R. Lara-Cabrera et al., "Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming," Applied Sciences, vol. 10, p. 675, 2020.
  12. Z. Zhang, ‘RAG for Personalized Medicine: A Framework for Integrating Patient Data and Pharmaceutical Knowledge for Treatment Recommendations’, Optimizations in Applied Machine Learning, vol. 4, no. 1, 2024, Accessed: Jun. 01, 2025.
  13. Q. Zhu, ‘Autonomous Cloud Resource Management through DBSCAN-based unsupervised learning’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Jun. 2025, doi: 10.71070/oaml.v5i1.112.
  14. S. Dan and Q. Zhu, ‘Enhancement of data centric security through predictive ridge regression’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, May 2025, doi: 10.71070/oaml.v5i1.113.
  15. S. Dan and Q. Zhu, ‘Highly efficient cloud computing via Adaptive Hierarchical Federated Learning’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Apr. 2025, doi: 10.71070/oaml.v5i1.114.
  16. Q. Zhu and S. Dan, ‘Data Security Identification Based on Full-Dimensional Dynamic Convolution and Multi-Modal CLIP’, Journal of Information, Technology and Policy, 2023.
  17. Q. Zhu, ‘An innovative approach for distributed cloud computing through dynamic Bayesian networks’, Journal of Computational Methods in Engineering Applications, 2024.
  18. Z. Luo, H. Yan, and X. Pan, ‘Optimizing Transformer Models for Resource-Constrained Environments: A Study on Model Compression Techniques’, Journal of Computational Methods in Engineering Applications, pp. 1–12, Nov. 2023, doi: 10.62836/jcmea.v3i1.030107.
  19. H. Yan and D. Shao, ‘Enhancing Transformer Training Efficiency with Dynamic Dropout’, Nov. 05, 2024, arXiv: arXiv:2411.03236. doi: 10.48550/arXiv.2411.03236.
  20. H. Yan, ‘Real-Time 3D Model Reconstruction through Energy-Efficient Edge Computing’, Optimizations in Applied Machine Learning, vol. 2, no. 1, 2022.
  21. Y. Shu, Z. Zhu, S. Kanchanakungwankul, and D. G. Truhlar, ‘Small Representative Databases for Testing and Validating Density Functionals and Other Electronic Structure Methods’, J. Phys. Chem. A, vol. 128, no. 31, pp. 6412–6422, Aug. 2024, doi: 10.1021/acs.jpca.4c03137.
  22. C. Kim, Z. Zhu, W. B. Barbazuk, R. L. Bacher, and C. D. Vulpe, ‘Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implications’, Toxicology Letters, vol. 401, pp. 125–138, 2024.
  23. J. Shen et al., ‘Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation’, NeuroImage, vol. 297, p. 120739, 2024.
  24. K. F. Faridi et al., ‘Factors associated with reporting left ventricular ejection fraction with 3D echocardiography in real‐world practice’, Echocardiography, vol. 41, no. 2, p. e15774, Feb. 2024, doi: 10.1111/echo.15774.
  25. Z. Zhu, ‘Tumor purity predicted by statistical methods’, in AIP Conference Proceedings, AIP Publishing, 2022.
  26. Z. Zhao, P. Ren, and Q. Yang, ‘Student self-management, academic achievement: Exploring the mediating role of self-efficacy and the moderating influence of gender insights from a survey conducted in 3 universities in America’, Apr. 17, 2024, arXiv: arXiv:2404.11029. doi: 10.48550/arXiv.2404.11029.
  27. Z. Zhao, P. Ren, and M. Tang, ‘Analyzing the Impact of Anti-Globalization on the Evolution of Higher Education Internationalization in China’, Journal of Linguistics and Education Research, vol. 5, no. 2, pp. 15–31, 2022.
  28. M. Tang, P. Ren, and Z. Zhao, ‘Bridging the gap: The role of educational technology in promoting educational equity’, The Educational Review, USA, vol. 8, no. 8, pp. 1077–1086, 2024.
  29. P. Ren, Z. Zhao, and Q. Yang, ‘Exploring the Path of Transformation and Development for Study Abroad Consultancy Firms in China’, Apr. 17, 2024, arXiv: arXiv:2404.11034. doi: 10.48550/arXiv.2404.11034.
  30. P. Ren and Z. Zhao, ‘Parental Recognition of Double Reduction Policy, Family Economic Status And Educational Anxiety: Exploring the Mediating Influence of Educational Technology Substitutive Resource’, Economics & Management Information, pp. 1–12, 2024.
  31. Z. Zhao, P. Ren, and M. Tang, ‘How Social Media as a Digital Marketing Strategy Influences Chinese Students’ Decision to Study Abroad in the United States: A Model Analysis Approach’, Journal of Linguistics and Education Research, vol. 6, no. 1, pp. 12–23, 2024.
  32. Z. Zhao and P. Ren, ‘Identifications of Active Explorers and Passive Learners Among Students: Gaussian Mixture Model-Based Approach’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, May 2025.
  33. Z. Zhao and P. Ren, ‘Prediction of Student Answer Accuracy based on Logistic Regression’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Feb. 2025.
  34. Z. Zhao and P. Ren, ‘Prediction of Student Disciplinary Behavior through Efficient Ridge Regression’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Mar. 2025.
  35. Z. Zhao and P. Ren, ‘Random Forest-Based Early Warning System for Student Dropout Using Behavioral Data’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Apr. 2025.
  36. P. Ren and Z. Zhao, ‘Recognition and Detection of Student Emotional States through Bayesian Inference’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, May 2025.
  37. P. Ren and Z. Zhao, ‘Support Vector Regression-based Estimate of Student Absenteeism Rate’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Jun. 2025.
  38. G. Zhang and T. Zhou, ‘Finite Element Model Calibration with Surrogate Model-Based Bayesian Updating: A Case Study of Motor FEM Model’, IAET, pp. 1–13, Sep. 2024, doi: 10.62836/iaet.v3i1.232.
  39. G. Zhang, W. Huang, and T. Zhou, ‘Performance Optimization Algorithm for Motor Design with Adaptive Weights Based on GNN Representation’, Electrical Science & Engineering, vol. 6, no. 1, Art. no. 1, Oct. 2024, doi: 10.30564/ese.v6i1.7532.
  40. T. Zhou, G. Zhang, and Y. Cai, ‘Unsupervised Autoencoders Combined with Multi-Model Machine Learning Fusion for Improving the Applicability of Aircraft Sensor and Engine Performance Prediction’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Feb. 2025, doi: 10.71070/oaml.v5i1.83.
  41. Y. Tang and C. Li, ‘Exploring the Factors of Supply Chain Concentration in Chinese A-Share Listed Enterprises’, Journal of Computational Methods in Engineering Applications, pp. 1–17, 2023.
  42. C. Li and Y. Tang, ‘Emotional Value in Experiential Marketing: Driving Factors for Sales Growth–A Quantitative Study from the Eastern Coastal Region’, Economics & Management Information, pp. 1–13, 2024.
  43. C. Li and Y. Tang, ‘The Factors of Brand Reputation in Chinese Luxury Fashion Brands’, Journal of Integrated Social Sciences and Humanities, pp. 1–14, 2023.
  44. C. Y. Tang and C. Li, ‘Examining the Factors of Corporate Frauds in Chinese A-share Listed Enterprises’, OAJRC Social Science, vol. 4, no. 3, pp. 63–77, 2023.
  45. W. Huang, T. Zhou, J. Ma, and X. Chen, ‘An ensemble model based on fusion of multiple machine learning algorithms for remaining useful life prediction of lithium battery in electric vehicles’, Innovations in Applied Engineering and Technology, pp. 1–12, 2025.
  46. W. Huang and J. Ma, ‘Predictive Energy Management Strategy for Hybrid Electric Vehicles Based on Soft Actor-Critic’, Energy & System, vol. 5, no. 1, 2025.
  47. J. Ma, K. Xu, Y. Qiao, and Z. Zhang, ‘An Integrated Model for Social Media Toxic Comments Detection: Fusion of High-Dimensional Neural Network Representations and Multiple Traditional Machine Learning Algorithms’, Journal of Computational Methods in Engineering Applications, pp. 1–12, 2022.
  48. W. Huang, Y. Cai, and G. Zhang, ‘Battery Degradation Analysis through Sparse Ridge Regression’, Energy & System, vol. 4, no. 1, Art. no. 1, Dec. 2024, doi: 10.71070/es.v4i1.65.
  49. Z. Zhang, K. Xu, Y. Qiao, and A. Wilson, ‘Sparse Attention Combined with RAG Technology for Financial Data Analysis’, Journal of Computer Science Research, vol. 7, no. 2, Art. no. 2, Mar. 2025, doi: 10.30564/jcsr.v7i2.8933.
  50. P.-M. Lu and Z. Zhang, ‘The Model of Food Nutrition Feature Modeling and Personalized Diet Recommendation Based on the Integration of Neural Networks and K-Means Clustering’, Journal of Computational Biology and Medicine, vol. 5, no. 1, 2025.
  51. Y. Qiao, K. Xu, Z. Zhang, and A. Wilson, ‘TrAdaBoostR2-based Domain Adaptation for Generalizable Revenue Prediction in Online Advertising Across Various Data Distributions’, Advances in Computer and Communication, vol. 6, no. 2, 2025.
  52. K. Xu, Y. Gan, and A. Wilson, ‘Stacked Generalization for Robust Prediction of Trust and Private Equity on Financial Performances’, Innovations in Applied Engineering and Technology, pp. 1–12, 2024.
  53. A. Wilson and J. Ma, ‘MDD-based Domain Adaptation Algorithm for Improving the Applicability of the Artificial Neural Network in Vehicle Insurance Claim Fraud Detection’, Optimizations in Applied Machine Learning, vol. 5, no. 1, 2025.