FMCPNN in Digital Twins Smart Healthcare

被引：10

作者：

Yu, Zengchen ^{[1
]}

Wan, Zhibo ^{[1
]}

Xie, Shuxuan ^{[1
]}

Wang, Ke ^{[2
]}

Lv, Zhihan ^{[3
]}

机构：

[1] Qingdao Univ, Qingdao, Peoples R China

[2] Qingdao Municipal Hosp, Qingdao, Peoples R China

[3] Uppsala Univ, Uppsala, Sweden

来源：

IEEE CONSUMER ELECTRONICS MAGAZINE | 2023年 / 12卷 / 04期

关键词：

Frequency modulation; Medical diagnostic imaging; Digital twins; Diseases; Prediction algorithms; Neural networks; Deep learning;

D O I：

10.1109/MCE.2022.3184441

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In recent years, digital twins havepenetrated into the medical field, bringing revolutionary changes to the medical field. In this study, we propose a disease diagnosis algorithm, factorization machine combine product-based neural network (FMCPNN), which is improved on the basis of product-based neural network (PNN). PNN is an end-to-end factorization machine (FM) algorithm, which can solve the problem of data sparseness. But PNN lacks low-order feature interaction, resulting in weak generalization ability. FMCPNN adds the second-order interaction part of FM on the basis of PNN, which improves the performance of PNN. FMCPNN can be well applied in the digital twins medical system to improve the accuracy and speed of disease diagnosis. Our tests show that the performance of FMCPNN surpasses some advanced models.

引用

页码：66 / 73

页数：8

共 14 条

[11] An Open-Source and Wearable System for Measuring 3D Human Motion in Real-Time [J].