Self-Supervised Multi-Source Heterogeneous Data Fusion Using Encode and Decode Attention for Intelligent Medical Device Communication Analysis

Abstract

With the development of artificial intelligence, the application of intelligent medical devices is gradually becoming popular. Intelligent medical devices can access many types of medical data through sensors and analyze these data to monitor, diagnose and treat patients. Intelligent medical device communication is an important research area that is of great significance to the development of the medical field. Intelligent medical device communication technology allows medical devices to communicate and share information with each other, thereby improving the efficiency and accuracy of medical devices. However, due to the heterogeneous nature of medical data and the diversity of data sources, it has become a challenge to integrate and analyze these data effectively. In order to achieve data interaction and sharing between different devices, it is necessary to fuse heterogeneous data from multiple sources to improve data integrity and usability. In this paper, we propose a self-supervised multi-source heterogeneous data fusion method using encode and decode attention for intelligent medical device communication analysis. The method improves data fusion by automatically learning the correlation between data through self-supervised learning and using encoding and decoding attention to strengthen the correlation between data. The experimental results show that the proposed method can effectively improve the accuracy and efficiency of medical data fusion and provide a strong support for communication between intelligent medical devices. This research integrates different types of medical information to provide better decision support for physicians and to promote medical technology advancement and health management.