Ensemble Learning for Interpretable Concept Drift and Its Application to Drug Recommendation


  • Yunjuan Peng Beijing Jiaotong University, Beijing, China
  • Qi Qiu Capital Medical University, Beijing, China
  • Dalin Zhang Beijing Jiaotong University, Beijing, China
  • Tianyu Yang Department of Electrical Engineering, Columbia University, NYC, USA
  • Hailong Zhang Virginia Polytechnic Institute and State University, Blacksburg, VA, USA




Interpretable Concept Drift, Self-adaptive Ensemble Learning, Drug Recommendation, Pattern Classification


During the COVID-19 epidemic, the online prescription pattern of Internet healthcare provides guarantee for the patients with chronic diseases and reduces the risk of cross-infection, but it also raises the burden of decision-making for doctors. Online drug recommendation system can effectively assist doctors by analysing the electronic medical records (EMR) of patients. Unlike commercial recommendations, the accuracy of drug recommendations should be very high due to their relevance to patient health. Besides, concept drift may occur in the drug treatment data streams, handling drift and location drift causes is critical to the accuracy and reliability of the recommended results. This paper proposes a multi-model fusion online drug recommendation system based on the association of drug and pathological features with online-nearline-offline architecture. The system transforms drug recommendation into pattern classification and adopts interpretable concept drift detection and adaptive ensemble classification algorithms. We apply the system to the Percutaneous Coronary Intervention (PCI) treatment process. The experiment results show our system performs nearly as good as doctors, the accuracy is close to 100%


LIU Bo, GUO Youyan, LIN Yang, WU Xinghai, and WEI Yongxiang. A brief analysis on the development of public hospitals assisted by internet medical services under the covid-19 epidemic. Chinese Hospitals, 24(09):62-64, 2020.

Gheorghe Zaman, Anamaria-Catalina RADU, Ivona RAPAN, and Florian Berghea. New wave of disruptive technologies in the healthcare system. Economic Computation & Economic Cybernetics Studies & Research, 55(1), 2021.


Yu Wang, Peng-Fei Li, Yu Tian, Jing-Jing Ren, and Jing-Song Li. A shared decision-making system for diabetes medication choice utilizing electronic health record data. IEEE Journal of Biomedical and Health Informatics, 21(5):1280-1287, 2017.


Jinsung Yoon, Camelia Davtyan, and Mihaela van der Schaar. Discovery and clinical decision support for personalized healthcare. IEEE Journal of Biomedical and Health Informatics, 21(4):1133- 1145, 2017.


Lin Liu, Lin Tang, Wen Dong, Shaowen Yao, and Wei Zhou. An overview of topic modeling and its current applications in bioinformatics. SpringerPlus, 5(1):1-22, 2016.


Corville O Allen, Timothy A Bishop, Michael T Payne, Sue S Schmidt, and Leah R Smutzer. Identifying drug-to-drug interactions in medical content and applying interactions to treatment recommendations, November 17 2020. US Patent 10,839,961.

N. Komal Kumar and D. Vigneswari. A drug recommendation system for multi-disease in health care using machine learning. In Gurdeep Singh Hura, Ashutosh Kumar Singh, and Lau Siong Hoe, editors, Advances in Communication and Computational Technology, pages 1-12, Singapore, 2021. Springer Singapore.


Chun Chen, Lu Zhang, Xiaopeng Fan, Yang Wang, Chengzhong Xu, and Renkai Liu. A epilepsy drug recommendation system by implicit feedback and crossing recommendation. In 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), pages 1134-1139, 2018.


Ferran Torrent-Fontbona and Beatriz López. Personalized adaptive cbr bolus recommender system for type 1 diabetes. IEEE Journal of Biomedical and Health Informatics, 23(1):387-394, 2019.


Wen San Yee, Hu Ng, Timothy Tzen Vun Yap, Vik Tor Goh, Keng Hong Ng, and Dong Theng Cher. An evaluation study on the predictive models of breast cancer risk factor classification. Journal of Logistics, Informatics and Service Science, 2022.

BS Kim, Tag-Gon Kim, and SH Choi. Codevs: An extension of devs for integration of simulation and machine learning. INTERNATIONAL JOURNAL OF SIMULATION MODELLING, 20(4):661-671, 2021.


Peng Tang, Qiaokang Liang, Xintong Yan, Shao Xiang, and Dan Zhang. Gp-cnn-dtel: Global-part cnn model with data-transformed ensemble learning for skin lesion classification. IEEE Journal of Biomedical and Health Informatics, 24(10):2870-2882, 2020.


Z. Cui, X. Xu, F. Xue, X. Cai, Y. Cao, W. Zhang, and J. Chen. Personalized recommendation system based on collaborative filtering for iot scenarios. IEEE Transactions on Services Computing, 13(4):685-695, 2020.


Jiyoung Yoon and Soonhee Joung. A big data based cosmetic recommendation algorithm. Journal of System and Management Sciences, 10(2):40-52, 2020.

Rung-Ching Chen, Yun-Hou Huang, Cho-Tsan Bau, and Shyi-Ming Chen. A recommendation system based on domain ontology and swrl for anti-diabetic drugs selection. Expert Systems with Applications, 39(4):3995-4006, 2012.


Qian Zhang, Guangquan Zhang, Jie Lu, and Dianshuang Wu. A framework of hybrid recommender system for personalized clinical prescription. In 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pages 189-195, 2015.


Yinghui Wang. A novel chinese traditional medicine prescription recommendation system based on knowledge graph. Journal of Physics: Conference Series, 1487:012019, mar 2020.


Yao Qin and Zherui Ma. A traditional chinese medicine prescription recommendation method based on mutual information clustering. Journal of Physics: Conference Series, 1544:012065, may 2020.


Fan Gong, Meng Wang, Haofen Wang, Sen Wang, and Mengyue Liu. Smr: medical knowledge graph embedding for safe medicine recommendation. Big Data Research, 23:100174, 2021.


Tan Ke. Research on the Concept Drift Oriented Recommender System. PhD thesis, University of Electronic Science and Technology of China, 2018.

Yi Ding and Xue Li. Time weight collaborative filtering. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM '05, page 485-492, New York, NY, USA, 2005. Association for Computing Machinery.


Lei Wang, Yunqiu Zhang, and Xiaohu Zhu. Concept drift-aware temporal cloud service apis recommendation for building composite cloud systems. Journal of Systems and Software, 174:110902, 2021.


Joo Gama, Indr Liobait, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. A survey on concept drift adaptation. ACM Computing Surveys, 46(4), 2014.


Antônio David Viniski, Jean Paul Barddal, Alceu de Souza Britto Jr, Fabrício Enembreck, and Humberto Vinicius Aparecido de Campos. A case study of batch and incremental recommender systems in supermarket data under concept drifts and cold start. Expert Systems with Applications, 176:114890, 2021.


Charinya Wangwatcharakul and Sartra Wongthanavasu. A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution. Expert Systems with Applications, 185:115626, 2021.


Simona Micevska, Ahmed Awad, and Sherif Sakr. Sddm: an interpretable statistical concept drift detection method for data streams. Journal of Intelligent Information Systems, 56(3):459-484, 2021.


Manuel Baena-Garcıa, José del Campo-Ávila, Raúl Fidalgo, Albert Bifet, R Gavalda, and Rafael Morales-Bueno. Early drift detection method. In Fourth international workshop on knowledge discovery from data streams, volume 6, pages 77-86, 2006.

Gordon J Ross, Niall M Adams, Dimitris K Tasoulis, and David J Hand. Exponentially weighted moving average charts for detecting concept drift. Pattern recognition letters, 33(2):191-198, 2012.


Roberto SM Barros, Danilo RL Cabral, Paulo M Gonçalves Jr, and Silas GTC Santos. Rddm: Reactive drift detection method. Expert Systems with Applications, 90:344-355, 2017.


Geoffrey IWebb, Loong Kuan Lee, François Petitjean, and Bart Goethals. Understanding concept drift. arXiv preprint arXiv:1704.00362, 2017.

Eibe Frank, Yong Wang, Stuart Inglis, Geoffrey Holmes, and Ian H Witten. Using model trees for classification. Machine learning, 32(1):63-76, 1998.


Mehak Naib and Amit Chhabra. Predicting primary tumors using multiclass classifier approach of data mining. International Journal of Computer Applications, 96(8), 2014.


Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. The weka data mining software: An update. SIGKDD Explor. Newsl., 11(1):10-18, November 2009.


Nir Friedman, Dan Geiger, and Moises Goldszmidt. Bayesian network classifiers. Machine learning, 29(2):131-163, 1997.


George H. John and Pat Langley. Estimating continuous distributions in bayesian classifiers, 2013.

Leroy A Gondy, C. Rindflesch B Thomas, and Naïve Bayes. Programs for machine learning. Advances in Neural Information Processing Systems, 79(2):937-944, 1993.

Niels Landwehr, Mark Hall, and Eibe Frank. Logistic model trees. Machine learning, 59(1-2):161- 205, 2005.


Leo Breiman. Random forests. Machine learning, 45(1):5-32, 2001.


Additional Files



Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.