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

International Journal of Computers Communications & Control

article

Yunjuan Peng¹  Qi Qiu²  Dalin Zhang¹  Tianyu Yang³  Hailong Zhang⁴ 

[1] Beijing Jiaotong University;Capital Medical University;Department of Electrical Engineering, Columbia University;Virginia Polytechnic Institute and State University

关键词: Interpretable Concept Drift;    Self-adaptive Ensemble Learning;    Drug Recommendation;    Pattern Classification;   

DOI  :  10.15837/ijccc.2023.1.5011

学科分类：自动化工程

来源: Universitatea Agora

【 摘 要 】

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%.

【 授权许可】

CC BY-NC