Extracting Insights from Digital Public Health Data using Artificial Intelligence, Volume II

41.1K

views

64

authors

12

articles

Extracting Insights from Digital Public Health Data using Artificial Intelligence, Volume II

41.1K

views

64

authors

12

articles

Editors

5

Steven Fernandes

Creighton University

João Manuel R. S. Tavares

Faculty of Engineering, University of Porto

Hong Lin

University of Houston–Downtown

Yu-Dong Zhang

University of Leicester

Shyamala Guruvare

Kasturba Medical College, Manipal Academy of Higher Education

Impact

About

This Research Topic is a follow on from the Topic Editors' successful volume I.

Artificial Intelligence (AI) has the ability to perform automated/case-based reasoning, constraint processing, deep learning, and deep reinforcement learning. Recent advancements in AI techniques and GPU (graphics processing unit) computing capabilities have made it possible to process large volumes of data and extract valuable insights within a short period. Digital public health data are enormous, and harnessing AI's power can lead to exciting and ground-breaking research. Due to the current COVID-19 pandemic, AI can assist in disease surveillance methods, infectious disease modeling, non-contact temperature screening, intelligent contact tracking, detecting social/economic factors on transmission, effective health communication and misinformation detection, identifying factors that affect the mental and emotional health of the public.

The objective of this Research Topic would be to provide a platform for researchers, health practitioners, policymakers, and governments to apply the current state-of-the-art AI techniques on digital public health data and obtain critical insights. The Topic Editors welcome original, high-quality contributions that are not yet published or that are not currently under review by other journals or peer-reviewed conferences in the area of application of AI on digital public health data.

Manuscripts of interest include, but not restricted to:
• Predictive analytics on digital public health data using deep learning;
• Early warning and rapid response AI tools for disease outbreaks and epidemics;
• Preserving privacy and confidentially data using adversarial machine learning;
• Infectious disease modeling using generative adversarial networks;
• Surveillance of infectious disease and epidemic intelligence;
•Non-contact temperature screening using thermal imaging and computer vision;
• Health communication and misinformation detection using recurrent neural networks;
• Analyzing mental and emotional health using meta-learning;
• AI techniques using novel data sources to improve public health outcomes.

Download ebook

PDF

EPUB

Share

Editors

Steven Fernandes

Creighton University

João Manuel R. S. Tavares

Faculty of Engineering, University of Porto

Hong Lin

University of Houston–Downtown

Yu-Dong Zhang

University of Leicester

Shyamala Guruvare

Kasturba Medical College, Manipal Academy of Higher Education

Coordinators

Prianna Menezes

Impact

41,149 Total views

28,122 Article views

11,260 Article downloads

1,767 Topic views

Published In

Frontiers in Public Health

Digital Public Health

Frontiers in ICT

Digital Public Health

Frontiers in Computer Science

Digital Public Health

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Suggest a topic