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

35.3K

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64

authors

12

articles

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

35.3K

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.

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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

35,279 Total views

24,412 Article views

9,349 Article downloads

1,518 Topic views

Published In

Frontiers in Public Health

Digital Public Health

Frontiers in ICT

Digital Public Health

Frontiers in Computer Science

Digital Public Health

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