A Narrative Analysis on Deep Learning in IoT based Medical Big Data Analysis with Future Perspectives

In the era of big data, intelligent sports venues have a practical significance to provide personalized service for users and build up a platform for stadium management. This article proposes a new parallel big data promotion algorithm based on the latest achievements of big data analysis. The proposed algorithm calculates the optimal value by using the observed variables Y, the hidden variable data Z, the joint distribution P (Y, Z | θ) and distribution conditions P (Z | Y | θ). The experimental results show that the proposed algorithm has higher accuracy of big data analysis, and can serve the intelligent sports venues better.

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Medical Big Data Analysis in Hospital Information System

Big Data on Real-World Applications ◽

10.5772/63754 ◽

2016 ◽

Cited By ~ 4

Author(s):

Jing-Song Li ◽

Yi-Fan Zhang ◽

Yu Tian

Keyword(s):

Information System ◽

Big Data ◽

Data Analysis ◽

Hospital Information System ◽

Big Data Analysis ◽

Medical Big Data

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A Medical Big Data Analysis Algorithm Based on Access Control System

International Journal of Reasoning-based Intelligent Systems ◽

10.1504/ijris.2018.10009929 ◽

2018 ◽

Vol 10 (2) ◽

pp. 1

Author(s):

XiaoRong Diao ◽

Xingyan Yao ◽

Yegang Chen ◽

Jun Luo

Keyword(s):

Big Data ◽

Control System ◽

Data Analysis ◽

Access Control ◽

Big Data Analysis ◽

Analysis Algorithm ◽

Access Control System ◽

Medical Big Data

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How to Determine the Early Warning Threshold Value of Meteorological Factors on Influenza through Big Data Analysis and Machine Learning

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/8845459 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Hui Ge ◽

Debao Fan ◽

Ming Wan ◽

Lizhu Jin ◽

Xiaofeng Wang ◽

...

Keyword(s):

Big Data ◽

Data Analysis ◽

Infectious Diseases ◽

Early Warning ◽

Meteorological Factors ◽

Threshold Value ◽

Big Data Analysis ◽

Meteorological Conditions ◽

Medical Big Data ◽

Influenza Outbreaks

Infectious diseases are a major health challenge for the worldwide population. Since their rapid spread can cause great distress to the real world, in addition to taking appropriate measures to curb the spread of infectious diseases in the event of an outbreak, proper prediction and early warning before the outbreak of the threat of infectious diseases can provide an important basis for early and reasonable response by the government health sector, reduce morbidity and mortality, and greatly reduce national losses. However, if only traditional medical data is involved, it may be too late or too difficult to implement prediction and early warning of an infectious outbreak. Recently, medical big data has become a research hotspot and has played an increasingly important role in public health, precision medicine, and disease prediction. In this paper, we focus on exploring a prediction and early warning method for influenza with the help of medical big data. It is well known that meteorological conditions have an influence on influenza outbreaks. So, we try to find a way to determine the early warning threshold value of influenza outbreaks through big data analysis concerning meteorological factors. Results show that, based on analysis of meteorological conditions combined with influenza outbreak history data, the early warning threshold of influenza outbreaks could be established with reasonable high accuracy.

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