Artificial 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).
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xi 7. Preface - p. xiii 8. Chapter 1. Improvement of mental health of frontline healthcare workers during COVID-19 pandemic using artificial intelligence - p. 1 9. Chapter 2. Effective algorithms for solving statistical problems posed by COVID-19 pandemic - p. 21 10. Chapter 3. Reconsideration of drug repurposing through artificial intelligence program for the treatment of the novel coronavirus - p. 45 11. Chapter 4. COVID-19: artificial intelligence solutions, prediction with country cluster analysis, and time-series forecasting - p. 69 12. Chapter 5. Graph convolutional networks for pain detection via telehealth - p. 93 13. Chapter 6. The role of social media in the battle against COVID-19 - p. 105 14. Chapter 7. De-identification techniques to preserve privacy in medical records - p. 125 15. Chapter 8. Estimation of COVID-19 fatality associated with different SARS-CoV-2 variants - p. 149 16. Chapter 9. Artificial intelligence for chest imaging against COVID-19: an insight into image segmentation methods - p. 167 17. Index - p. 201
Artificial 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).
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xi 7. Preface - p. xiii 8. Chapter 1. Improvement of mental health of frontline healthcare workers during COVID-19 pandemic using artificial intelligence - p. 1 9. Chapter 2. Effective algorithms for solving statistical problems posed by COVID-19 pandemic - p. 21 10. Chapter 3. Reconsideration of drug repurposing through artificial intelligence program for the treatment of the novel coronavirus - p. 45 11. Chapter 4. COVID-19: artificial intelligence solutions, prediction with country cluster analysis, and time-series forecasting - p. 69 12. Chapter 5. Graph convolutional networks for pain detection via telehealth - p. 93 13. Chapter 6. The role of social media in the battle against COVID-19 - p. 105 14. Chapter 7. De-identification techniques to preserve privacy in medical records - p. 125 15. Chapter 8. Estimation of COVID-19 fatality associated with different SARS-CoV-2 variants - p. 149 16. Chapter 9. Artificial intelligence for chest imaging against COVID-19: an insight into image segmentation methods - p. 167 17. Index - p. 201
