scholarly journals Application of Artificial Intelligence to Social Governance Capabilities under Public Health Emergencies

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yafang Wu ◽  
Shaonan Shan
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Big Data ◽  
Emergency Management ◽  
Information Leakage ◽  
Personal Privacy ◽  
Standard Data ◽  
Public Health Emergencies ◽  
Social Governance ◽  
Depth Analysis

Due to the high complexity, high destructive power, and comprehensive governance characteristics of public health emergencies, the ability of social governance has been distorted and alienated under intensive pressure, and the subjects of social governance have become lazy, professional, and politicized. There are obvious problems, such as system information leakage and information asymmetry. Based on the above background, the purpose of this article is to study the application of artificial intelligence to social governance capabilities under public health emergencies. This article focuses on the relevant concepts and content of emergency management of public health emergencies and in-depth analysis of the actual application of big data technology in epidemic traceability and prediction, medical diagnosis and vaccine research and development, people’s livelihood services, and government advice and suggestions, combined with investigations. The questionnaire analysis sorted out the problems in the social emergency management of public health emergencies in China. The results showed that 87.7% of the people simply sorted out laws and regulations and higher-level documents or even repeated content and lacked summary and reflection on emergency response experience, which led to the operability of emergency plans being generally even poor. In response to the shortcomings, countermeasures and suggestions were put forward, including establishing a standard data collection mechanism, establishing a data sharing mechanism, establishing a personal privacy security protection mechanism, and promoting the breadth and depth of big data applications.

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

Big Data and Machine Learning

2018 ◽  
pp. 225-239
Author(s):  
Fernando Enrique Lopez Martinez ◽  
Edward Rolando Núñez-Valdez
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Complete Solution ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Healthcare Organizations ◽  
High Level

IoT, big data, and artificial intelligence are currently three of the most relevant and trending pieces for innovation and predictive analysis in healthcare. Many healthcare organizations are already working on developing their own home-centric data collection networks and intelligent big data analytics systems based on machine-learning principles. The benefit of using IoT, big data, and artificial intelligence for community and population health is better health outcomes for the population and communities. The new generation of machine-learning algorithms can use large standardized data sets generated in healthcare to improve the effectiveness of public health interventions. A lot of these data come from sensors, devices, electronic health records (EHR), data generated by public health nurses, mobile data, social media, and the internet. This chapter shows a high-level implementation of a complete solution of IoT, big data, and machine learning implemented in the city of Cartagena, Colombia for hypertensive patients by using an eHealth sensor and Amazon Web Services components.

Reform and innovation of artificial intelligence technology for information service in university physical education

2020 ◽  
pp. 1-11
Author(s):  
Jianye Zhang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Physical Education ◽  
Information Service ◽  
Management Decision ◽  
Negative Effects ◽  
Education Management ◽  
Depth Analysis ◽  
Artificial Intelligence Technology ◽  
The Difference

This article analyzes the reform of information services in university physical education based on artificial intelligence technology and conducts in-depth and innovative research on it. In-depth analysis of the relationship between big data and the development and application of information technology such as the Internet, Internet of Things, cloud computing, to clarify the difference and connection between big data, informatization and intelligence. Artificial intelligence will bring opportunities for changes in data collection, management decision-making, governance models, education and teaching, scientific research services, evaluation and evaluation of physical education in our university. At the same time, big data education management in colleges and universities faces many challenges such as the balance of privacy and freedom, data hegemony, data junk, data standards, and data security, and they have many negative effects. In accordance with the requirements of educational modernization, centering on the goal of intelligent and humanized education management, it aims existing issues in college physical education management.

scholarly journals Knowledge System Analysis on Emergency Management of Public Health Emergencies

2020 ◽  
Vol 12 (11) ◽  
pp. 4410 ◽  
Author(s):  
Rui Yang ◽  
Guoming Du ◽  
Ziwei Duan ◽  
Mengjin Du ◽  
Xin Miao ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Management ◽  
System Analysis ◽  
Knowledge System ◽  
Future Research ◽  
Network Clustering ◽  
Research Directions ◽  
Research And Practice ◽  
Public Health Emergencies ◽  
Future Research Directions

The Coronavirus Disease 2019 (COVID-19) infectious pneumonia pandemic highlights the importance of emergency management of public health emergencies (EMPHE). This paper addresses the challenge of building a knowledge system for EMPHE research that may contribute to understand the spatial and temporal characteristics of knowledge distribution, research status, cutting-edge research and development trends, and helps to identify promising research topics and guide research and practice of EMPHE. Based on the Web of Science, this paper retrieves 1467 articles about EMPHE published from 2010 to date. Then, based on high-frequency keywords, we use CiteSpace to analyze their knowledge co-occurrence network, clustering network and knowledge evolution. Furthermore, we summarize the features and gaps in EMPHE research, providing references for future research directions. Based on the above analysis, this work constructs a knowledge system about EMPHE research, providing a comprehensive visual summary of the existing research in the field of EMPHE, with the aim to guide future research and practice.

scholarly journals Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China

2021 ◽  
Vol 45 (9) ◽  
Author(s):  
Jiancheng Dong ◽  
Huiqun Wu ◽  
Dong Zhou ◽  
Kaixiang Li ◽  
Yuanpeng Zhang ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Big Data ◽  
Prevention And Control ◽  
Vaccine Development ◽  
Management Decision ◽  
Management Decisions ◽  
Health Events ◽  
Effective Diagnosis ◽  
And Control

AbstractCOVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

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