Analyzing and Researching the Intermediate Layer of Alliance Medical Data Combined with Edge Computing

Abstract

Through big data analytics and deep learning, users can uncover unseen hidden information from personal data in the cloud and derive health improvements from it - including genomic, microbial, medical history, comprehensive blood analysis chemistry, proteins and metabolites, and daily data from exercise devices and scales. The analysis and integration of this data has the potential to improve health and prevent disease. In the research, a set of tools to collect, compute and analyze data through the middle layer, combined with alliance learning and deep learning technologies to conduct sampling comparison training on medical data, while combining edge computing nodes and data hidden features of alliance learning for data construction and model verification. The outcomes can provide early warning of disease agents through data analysis, early symptom detection, and long-term monitoring before the disease becomes widespread. Daily behavior and health can be improved by supporting the P4 medical model, and by integrating these capabilities, many chronic diseases can be prevented from further devastating patients' health. In the experiments, we propose a framework for designing proxies through system construction, data quantification, description and analysis of patient-specific chronic disease risk, and data learning to validate the model construction.

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