scholarly journals Health data analysis based on multi-calculation of big data during COVID-19 pandemic

2020 ◽  
Vol 39 (6) ◽  
pp. 8775-8782
Author(s):  
Yang Bo ◽  
Wang Chunli
Keyword(s):  
College Students ◽  
Big Data ◽  
Physical Health ◽  
Reference Value ◽  
Health Data ◽  
Digital Information ◽  
Medical Institutions ◽  
Asymptomatic Infections ◽  
And Control ◽  
Analysis Platform

Under the influence of the COVID-19, the analysis of physical health data is helpful to grasp the physical condition in time and promote the level of prevention and control of the epidemic. Especially for novel corona virus asymptomatic infections, the initial analysis of physical health data can help to detect the possibility of virus infection to some extent. The digital information system of traditional hospitals and other medical institutions is not perfect. For a large number of health data generated by smart medical technology, there is a lack of an effective storage, management, query and analysis platform. Especially, it lacks the ability of mining valuable information from big data. Aiming at the above problems, the idea of combining Struts 2 and Hadoop in the system architecture of the platform is proposed in this paper. Data mining association algorithm is adopted and improved based on MapReduce. A service platform for college students’ physical health is designed to solve the storage, processing and mining of health big data. The experiment result shows that the system can effectively complete the processing and analysis of the big data of College students’ physical health, which has a certain reference value for college students’ physical health monitoring during the COVID-19 epidemic.

scholarly journals You + ME Registry: A Research Platform to Facilitate Clinical and Therapeutic Discoveries in ME/CFS and Related Diseases

2021 ◽  
Author(s):  
Allison Ramiller ◽  
Kathleen Mudie ◽  
Elle Seibert ◽  
Sadie Whittaker
Keyword(s):  
Multiple Sclerosis ◽  
Big Data ◽  
Chronic Fatigue Syndrome ◽  
Chronic Fatigue ◽  
Health Data ◽  
Myalgic Encephalomyelitis ◽  
Fatigue Syndrome ◽  
Research Platform ◽  
And Control

ME/CFS (Myalgic Encephalomyelitis / Chronic Fatigue Syndrome) is a chronic, complex, heterogeneous disease that affects millions and lacks both diagnostics and treatments. Big data, or the collection of vast quantities of data that can be mined for information, has transformed the understanding of many complex illnesses like cancer (1,2) and multiple sclerosis (3,4), by dissecting heterogeneity, identifying subtypes, and enabling the development of personalized treatments. It is possible that big data can reveal the same for ME/CFS. Solve M.E. developed and launched the You + ME Registry to collect longitudinal health data from people with ME/CFS, people with Long COVID (LC) and control volunteers using rigorous protocols designed to harmonize with other groups collecting data from similar groups of people. The Registry is an invaluable resource because it integrates with a symptom tracking app, as well as a biorepository, to provide a robust and rich dataset that is available to qualified researchers. Accordingly, it facilitates collaboration that may ultimately uncover causes and help accelerate the development of therapies.

scholarly journals Empirical Study on Theories and Techniques of Adolescent Physical Health Promotion under the Background of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Haolun Xu
Keyword(s):  
Health Promotion ◽  
Big Data ◽  
Physical Health ◽  
Classification Accuracy ◽  
Health Data ◽  
Training Set ◽  
Accuracy Rate ◽  
Standing Long Jump ◽  
Long Jump ◽  
Accuracy Rates

In order to make my country’s youth health management more scientific, more standardized, and more effective, it is necessary to conduct in-depth research on the management of youth physical health promotion. Through the investigation and analysis of the physical health data of adolescents in my country, this paper proposes that the results of health intervention training as part of the empirical research on the construction of adolescent health big data management service system can effectively improve the relationship hypothesis of the physical health of adolescents and by selecting the example of our country’s “Adolescent Physical Health Data Analysis in 2020” for regression analysis. The research results show that the theory of adolescent physical health promotion can improve the physical health of adolescents by interfering with students’ physical exercise. In the processing of data, GBDT is suitable when the training set is relatively large, and as the sample size increases, the accuracy rate can reach 79.79%. In terms of the classification accuracy of male sitting forward bending promotion, the accuracy of the RF method is higher than that of GBDT. In terms of the promotion classification effect of boys’ 1000 m running, the RF method achieved the highest promotion accuracy rate of 77.62%. In the male pull-ups to promote the classification effect, when the proportion of the training set is 60%, the RF method gets the highest accuracy rate, which is 92.04%. The results of the classification effect for girls standing long jump promotion show that the classification accuracy rate for girls standing long jump promotion is between 51% and 56%. When the training set is less than 60%, the RF method is the best, the highest is 53.93%, and the rest is the GBDT method, the highest is 55.46%; in Macro-F1, the RF and GBDT indicators have their own advantages. In the promotion of the classification effect on the final fitness level of girls, the accuracy rates of RF and GBDT methods range from 90% to 96%, and the accuracy rates of the NN method range from 80% to 87%; when the practice rate reaches 80%, the GBDT method achieves the highest accuracy rate of 95.06%; on the Macro-F1 index, the GBDT method is obviously the best.

scholarly journals Research on the Secure Medical Big Data Ecosystem Based on Hadoop

2020 ◽  
Author(s):  
Xiangfeng Zhang ◽  
Yanmei Wang
Keyword(s):  
Information System ◽  
Big Data ◽  
Health Information System ◽  
Health Data ◽  
Personal Health ◽  
Medical Institutions ◽  
Data Platform ◽  
Data Ecosystem ◽  
Medical Big Data ◽  
Personalized Health

Abstract This paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. Since traditional healing activities take place in medical institutions, patient users must travel to these institutions to learn about their treatment status. The personalized health information system designed for this purpose enables patient users to understand their treatment and rehabilitation status anytime and anywhere. The above is a consideration from the perspective of the patient user, from the perspective of personal health data. Because traditional medical health data is scattered throughout different independent medical institutions, and these databases are heterogeneous. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. The characteristics of blockchain without a central server make the system without a single point In case of failure, the stability of the system is well maintained. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data is stored and analysed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

scholarly journals Design of Campus Financial Risk Prevention and Control Platform under the Background of Big Data

2021 ◽  
Vol 257 ◽  
pp. 02040
Author(s):  
Sijin Li
Keyword(s):  
College Students ◽  
Big Data ◽  
Prevention And Control ◽  
Financial Risk ◽  
Risk Prevention ◽  
Financial Fraud ◽  
The Internet ◽  
Financial Industry ◽  
Control Factors ◽  
And Control

With the continuous development of information technology and the gradual rise of the Internet financial industry, the incidence of campus fraud is higher and higher, and the financial fraud against college students has gradually attracted widespread attention. In order to study the risk prevention and control factors of College Students’ financial fraud under the background of big data, an information platform is established to release risk information in real time and analyze the risk factors of College Students’ financial fraud. The Internet, big data and campus financial risk prevention and control are combined to improve the financial environment of university campus, improve the prevention awareness of college students, and reduce unnecessary losses.

scholarly journals Construction of College Students’ Physical Health Data Sharing System Based on Django Framework

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hui-chao Li ◽  
Shun-fa Shen
Keyword(s):  
College Students ◽  
Physical Health ◽  
Data Sharing ◽  
Exercise Intervention ◽  
Health Management ◽  
Implementation Process ◽  
Health Data ◽  
Living Environment ◽  
Management Service ◽  
Athletic Ability

In view of the current situation of college students’ physical health affected by the learning environment and living environment, which leads to the low level of students’ physical health and the lack of understanding of students’ physical health, this paper puts forward the construction of college students’ physical health data sharing system based on Django framework. By analyzing the feasibility of constructing the data sharing system of college students’ physical health, this paper constructs the organizational framework of the data sharing system of college students’ physical health and constructs the data sharing system of college students’ physical health according to the implementation process of the data sharing system of college students’ physical health management service. Through the student’s physical health test on education intervention and exercise intervention, it is concluded that college students’ physical health data sharing system based on the Django framework can cultivate students’ interest in sports and improve their athletic ability and physical fitness.

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