Artificial Intelligence in Healthcare and COVID-19
- 1st Edition - May 21, 2023
- Imprint: Academic Press
- Editors: Parag Chatterjee, Massimo Esposito
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 5 3 1 - 2
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 5 7 3 - 2
Artificial Intelligence in Healthcare and COVID-19 showcases theoretical concepts and implementational and research perspectives surrounding AI. The book addresses both medical a… Read more
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Request a sales quoteArtificial Intelligence in Healthcare and COVID-19 showcases theoretical concepts and implementational and research perspectives surrounding AI. The book addresses both medical and technological visions, making it even more applied. With the advent of COVID-19, it is obvious that leading universities and medical schools must include these topics and case studies in their usual courses of health informatics to keep up with the pace of technological and medical advancements. This book will also serve professors teaching courses and industry practitioners and professionals working in the R&D team of leading medical and informatics companies who want to embrace AI and eHealth to fight COVID-19.
Since AI in healthcare is a comparatively new field, there exists a vacuum of literature in this field, especially when applied to COVID-19. With the area of AI in COVID-19 being quite young, students and researchers usually face a struggle to rely on the few published papers (which are obviously too specific) or whitepapers by tech-giants (which are too wide).
- Discusses the fundamentals and theoretical concepts of applying AI in healthcare pertaining to COVID-19
- Provides a landscape view to the applied aspect of AI in healthcare related COVID-19 through case studies and innovative applications
- Discusses key concerns and challenges in the field of AI in eHealth during the pandemic, along with other allied fields like IoT, creating a broad platform of transdisciplinary discussion
Researchers, Professors and Postgraduate students working in the field of Artificial Intelligence in healthcare and biomedical engineering, Industry professionals who work in the field of healthcare informatics, especially in the areas of IoT and predictive health
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- Chapter 1. Improvement of mental health of frontline healthcare workers during COVID-19 pandemic using artificial intelligence
- Abstract
- Other notes
- 1.1 Introduction
- 1.2 Background
- 1.3 Main content
- 1.4 Methodologies and implementation
- 1.5 Discussion
- 1.6 Conclusion
- References
- Chapter 2. Effective algorithms for solving statistical problems posed by COVID-19 pandemic
- Abstract
- 2.1 Introduction
- 2.2 Forecasting the epidemic curves of coronavirus
- 2.3 Nonparametric tests used for forecasting models estimation
- 2.4 Comparison of forecast models
- 2.5 Conclusion and scope for the future work
- References
- Chapter 3. Reconsideration of drug repurposing through artificial intelligence program for the treatment of the novel coronavirus
- Abstract
- 3.1 Introduction
- 3.2 Viral morphology
- 3.3 Virus lifecycle
- 3.4 Currently available viral targeting drug candidates at various stages of life cycle
- 3.5 Different drug repurposing approaches
- 3.6 Artificial intelligence algorithms for drug repurposing
- 3.7 Computational intelligence-based approaches to identify therapeutic candidates for repurposing against coronavirus
- 3.8 Challenges in drug repurposing
- 3.9 Future perspectives of artificial intelligence-informed drug repurposing
- 3.10 Conclusion
- References
- Chapter 4. COVID-19: artificial intelligence solutions, prediction with country cluster analysis, and time-series forecasting
- Abstract
- 4.1 Introduction
- 4.2 Review of literature on COVID-19 pandemic
- 4.3 K-means clustering for COVID-19 country analysis
- 4.4 Time-series modeling for COVID-19 new cases
- 4.5 Conclusion
- References
- Further reading
- Chapter 5. Graph convolutional networks for pain detection via telehealth
- Abstract
- 5.1 Introduction
- 5.2 Methodology
- 5.3 Experiments
- 5.4 Results and discussion
- 5.5 Conclusion
- Acknowledgment
- References
- Chapter 6. The role of social media in the battle against COVID-19
- Abstract
- 6.1 Introduction
- 6.2 Materials and methods
- 6.3 Related reviews
- 6.4 Understanding COVID-19 data
- 6.5 Misinformation identification and spreading
- 6.6 COVID-19 forecasting
- 6.7 Discussion: challenges and future directions
- 6.8 Conclusion
- References
- Chapter 7. De-identification techniques to preserve privacy in medical records
- Abstract
- 7.1 Introduction
- 7.2 Background
- 7.3 Material and methods
- 7.4 Results and discussion
- 7.5 Conclusion
- References
- Chapter 8. Estimation of COVID-19 fatality associated with different SARS-CoV-2 variants
- Abstract
- 8.1 Introduction
- 8.2 Materials and methods
- 8.3 Results
- 8.4 Discussion and conclusion
- References
- Chapter 9. Artificial intelligence for chest imaging against COVID-19: an insight into image segmentation methods
- Abstract
- 9.1 Introduction
- 9.2 Chest CT findings of COVID-19 pneumonia
- 9.3 Medical image segmentation and artificial intelligence
- 9.4 Existing methods for COVID-19 chest CT images segmentation
- 9.5 Attention-FCNN: a novel DL model for the segmentation of COVID-19 chest CT scans
- 9.6 Discussion and conclusions
- References
- Index
- Edition: 1
- Published: May 21, 2023
- No. of pages (Paperback): 224
- No. of pages (eBook): 224
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780323905312
- eBook ISBN: 9780323905732
PC
Parag Chatterjee
Parag Chatterjee works as an Assistant Professor at the Department of Biological Engineering (Area of Informatics) in the University of the Republic (Universidad de la República), Uruguay and also as a Research Professor at National Technological University (Universidad Tecnológica Nacional) in Buenos Aires, Argentina. After graduating in Computer Science from University of Calcutta, India., currently he is working in the field of Internet of Things, especially in the aspects of eHealth, to research on the methodologies for better prevention of cardiometabolic diseases and aging. Chatterjee has authored several scientific papers, published in international journals and presented at international conferences.
Affiliations and expertise
Assistant Professor, Department of Biological Engineering, University of the Republic (Universidad de la República), Rincón, Paysandú, Uruguay; Research Professor, National Technological University (Universidad Tecnológica Nacional), Medrano, Buenos Aires, Capital Federal, ArgentinaME
Massimo Esposito
Senior Researcher, Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), Naples, Italy
Affiliations and expertise
Institute for High Performance Computing and Networking (ICAR), Naples, ItalyRead Artificial Intelligence in Healthcare and COVID-19 on ScienceDirect