首页> 外文会议>IEEE International Conference on Communications >DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem
【24h】

DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem

机译:DLRS:用于智能医疗生态系统的基于深度学习的推荐系统

获取原文

摘要

Nowadays, the conventional healthcare domain has witnessed a paradigm shift towards patient-driven healthcare 4.0 ecosystem. In this direction, healthcare recommender systems provide ubiquitous healthcare services to the end users even on the move. However, there are various challenges for the design of patient driven healthcare recommender systems. Some of the major challenges are: a) handling huge amount of data generated by smart devices and sensors, b) dynamic network management for real-time data transmission, and c) lack of knowledge gathering and aggregation methods. For these reasons, in this paper; DLRS: A Deep Learning based Recommender System using software defined networking (SDN) is designed for smart healthcare ecosystem. DLSR works in the following phases: a) a tensor-based dimensionality reduction algorithm is proposed for removing unwanted dimensions in the acquired data, b) a decision tree-based classification scheme is presented for categorization of the patient queries on the basis of different diseases, and c) a convolutional neural network based system is designed for providing recommendations about the patient health. On evaluation, the results obtained prove the superiority of the proposed scheme in contrast to existing competing schemes.
机译:如今,常规医疗保健领域已发生了向患者驱动的医疗保健4.0生态系统的转变。在这个方向上,医疗保健推荐系统即使在移动中也可以为最终用户提供无处不在的医疗保健服务。但是,患者驱动的医疗保健推荐系统的设计面临着各种挑战。主要挑战包括:a)处理由智能设备和传感器生成的大量数据,b)用于实时数据传输的动态网络管理,以及c)缺乏知识收集和聚集方法。由于这些原因,在本文中; DLRS:使用软件定义网络(SDN)的基于深度学习的推荐系统是为智能医疗生态系统设计的。 DLSR在以下阶段工作:a)提出了一种基于张量的降维算法,用于去除采集数据中不需要的维数; b)提出了一种基于决策树的分类方案,用于根据不同疾病对患者查询进行分类和c)设计基于卷积神经网络的系统,以提供有关患者健康的建议。在评估中,获得的结果证明了该方案相对于现有竞争方案的优越性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号