An Integrated Medical Recommendation Mechanism Combining Promote Product Singular Value Decomposition and Knowledge Graph

被引:0
作者
Sun, Yibo [1 ]
Liu, Chenlei [2 ]
Tong, Xue [2 ]
Hu, Bing [3 ]
机构
[1] Univ Queensland, St Lucia, Qld 4072, Australia
[2] Nanjing Univ Posts & Telecommun, Post Big Data Technol & Applicat Engn Res Ctr Jia, Post Ind Technol Res & Dev Ctr, State Posts Bur,Internet Things Technol, Nanjing, Peoples R China
[3] Nanjing Univ Posts & Telecommun, Minist Educ, Broadband Wireless Commun Technol Engn Res Ctr, Nanjing, Peoples R China
来源
ADVANCED DATA MINING AND APPLICATIONS (ADMA 2022), PT I | 2022年 / 13725卷
基金
中国国家自然科学基金;
关键词
Medical recommendation system; Integrated algorithm; PPSVD; Knowledge graph;
D O I
10.1007/978-3-031-22064-7_6
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Due to the increasingly serious problem of information overload, it is difficult for medical staff to accurately screen out data conducive to patient consultation when dealing with massive amounts of diagnostic information. In this paper, we propose a hybrid recommendation mechanism integrating the improved Singular Value Decomposition and Knowledge Graph for medical information. It can effectively analyze the characteristic relationship between patients and medical item information and give appropriate medical recommendation scheme information based on patients' interests and preferences. We design the Promote Product Singular Value Decomposition (PPSVD) algorithm and integrate knowledge graph technology to construct the medical system's recommendation model. Finally, the experimental results show that the integrated medical recommendation model proposed has higher accuracy and usability than any other algorithms.
引用
收藏
页码:67 / 78
页数:12
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