摘要
本研究开发了一种基于深度学习技术的药店个性化药品推荐系统,旨在提高药品推荐的准确度和个性化程度。系统通过收集和预处理药店会员数据,利用深度学习模型分析用户的健康状况和药物相互作用。结果显示,该系统在准确率和召回率方面均优于传统方法。通过顾客反馈和调查,验证了系统的有效性和顾客满意度,展示了深度学习在药品推荐领域的应用潜力,为相关行业从业者提供了新的技术途径。
In this study,a personalized drug recommendation system for pharmacies based on deep learning technology is developed to improve the accuracy and personalization of drug recommendation.The system collects and preprocesses pharmacy member data,and uses deep learning models to analyze users'health status and drug interactions.The results show that,this system outperforms traditional methods in terms of accuracy and recall rate.Through customer feedback and surveys,the effectiveness of the system and customer satisfaction are verified,the application potential of deep learning in the field of drug recommendation is demonstrated,and a new technical approach is provided for related practitioners.
作者
梁兢
Liang Jing(Hubei Business College,Wuhan 430079,Hubei Province,China)
出处
《科学与信息化》
2024年第11期111-114,118,共5页
Technology and Information
关键词
深度学习
个性化推荐
药店会员系统
效能评估
deep learning
personalized recommendation
pharmacy membership system
evaluation of effectiveness
