scholarly journals Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework

2020 ◽  
Vol 17 (17) ◽  
pp. 6161 ◽  
Author(s):  
Qiong Jia ◽  
Yue Guo ◽  
Guanlin Wang ◽  
Stuart J. Barnes
Keyword(s):  
Public Health ◽  
Big Data ◽  
Data Analytics ◽  
Close Contact ◽  
Big Data Analytics ◽  
Contact Screening ◽  
Negative Impacts ◽  
Effective Use ◽  
And Control ◽  
Clear Information

Major public health incidents such as COVID-19 typically have characteristics of being sudden, uncertain, and hazardous. If a government can effectively accumulate big data from various sources and use appropriate analytical methods, it may quickly respond to achieve optimal public health decisions, thereby ameliorating negative impacts from a public health incident and more quickly restoring normality. Although there are many reports and studies examining how to use big data for epidemic prevention, there is still a lack of an effective review and framework of the application of big data in the fight against major public health incidents such as COVID-19, which would be a helpful reference for governments. This paper provides clear information on the characteristics of COVID-19, as well as key big data resources, big data for the visualization of pandemic prevention and control, close contact screening, online public opinion monitoring, virus host analysis, and pandemic forecast evaluation. A framework is provided as a multidimensional reference for the effective use of big data analytics technology to prevent and control epidemics (or pandemics). The challenges and suggestions with respect to applying big data for fighting COVID-19 are also discussed.

scholarly journals Text Mining and Big Data Analytics for Retrospective Analysis of Clinical Texts from Outpatient Care

2015 ◽  
Vol 15 (4) ◽  
pp. 58-77 ◽  
Author(s):  
Svetla Boytcheva ◽  
Galia Angelova ◽  
Zhivko Angelov ◽  
Dimitar Tcharaktchiev
Keyword(s):  
Blood Pressure ◽  
Big Data ◽  
Text Mining ◽  
Retrospective Analysis ◽  
Outpatient Care ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Free Text ◽  
Arterial Blood ◽  
And Control

Abstract This paper presents the results of an on-going research project for knowledge extraction from large corpora of clinical narratives in Bulgarian language, approximately 100 million of outpatient care notes. Entities with numerical values are mined in the free text and the extracted information is stored in a structured format. The Algorithms for retrospective analyses and big data analytics are applied for studying the treatment and evaluating the diabetes compensation and control of arterial blood pressure.

scholarly journals Big Data, Decision Models, and Public Health

2020 ◽  
Vol 17 (18) ◽  
pp. 6723
Author(s):  
Chien-Lung Chan ◽  
Chi-Chang Chang
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Medical Decision ◽  
Mining Machine ◽  
Special Issue ◽  
Trade Offs

Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.

scholarly journals Big Data and Personalisation for Non-Intrusive Smart Home Automation

2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Suriya Priya R. Asaithambi ◽  
Sitalakshmi Venkatraman ◽  
Ramanathan Venkatraman
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Smart Home ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Smart Devices ◽  
Home Automation ◽  
Open Standard ◽  
Iot Devices ◽  
And Control

With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors have been designed to enhance the quality of personal life by having the capability to generate continuous data streams that can be used to monitor and make inferences by the user. While smart home devices connect to the home Wi-Fi network, there are still compatibility issues between devices from different manufacturers. Smart devices get even smarter when they can communicate with and control each other. The information collected by one device can be shared with others for achieving an enhanced automation of their operations. This paper proposes a non-intrusive approach of integrating and collecting data from open standard IoT devices for personalised smart home automation using big data analytics and machine learning. We demonstrate the implementation of our proposed novel technology instantiation approach for achieving non-intrusive IoT based big data analytics with a use case of a smart home environment. We employ open-source frameworks such as Apache Spark, Apache NiFi and FB-Prophet along with popular vendor tech-stacks such as Azure and DataBricks.

scholarly journals Privacy-aware Big Data Analytics as a service for public health policies in smart cities

2018 ◽  
Vol 39 ◽  
pp. 68-77 ◽  
Author(s):  
Marco Anisetti ◽  
Claudio Ardagna ◽  
Valerio Bellandi ◽  
Marco Cremonini ◽  
Fulvio Frati ◽  
...  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Health Policies ◽  
Public Health Policies

The Strengths, Weaknesses, Opportunities, and Threats Analysis of Big Data Analytics in Healthcare

2022 ◽  
pp. 1703-1718
Author(s):  
Chaojie Wang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Success Factors ◽  
Swot Analysis ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Healthcare Organizations ◽  
Effective Use ◽  
The Cost

Improving the performance and reducing the cost of healthcare have been a great concern and a huge challenge for healthcare organizations and governments at every level in the US. Measures taken have included laws, regulations, policies, and initiatives that aim to improve quality of care, reduce costs of care, and increase access to care. Central to these measures is the meaningful and effective use of Big Data analytics. To reap the benefits of big data analytics and align expectations with results, researchers, practitioners, and policymakers must have a clear understanding of the unique circumstances of healthcare including the strengths, weaknesses, opportunities, and threats (SWOT) associated with the use of this emerging technology. Through descriptive SWOT analysis, this article helps healthcare stakeholders gain awareness of both success factors and issues, pitfalls, and barriers in the adoption of big data analytics in healthcare.

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