Intelligent Biomedical Technologies and Applications for Healthcare 5.0, Volume Sixteen covers artificial health intelligence, biomedical image analysis, 5G, the Internet of Medical Things, intelligent healthcare systems, and extended health intelligence (EHI). This volume contains four sections. The focus of the first section is health data analytics and applications. The second section covers research on information exchange and knowledge sharing. The third section is on the Internet of Things (IoT) and the Internet of Everything (IoE)-based solutions. The final section focuses on the implementation, assessment, adoption, and management of healthcare informatics solutions. This new volume in the Advances in Ubiquitous Sensing Applications for Healthcare series focuses on innovative methods in the healthcare industry and will be useful for biomedical engineers, researchers, and students working in interdisciplinary fields of research. This volume bridges these newly developing technologies and the medical community in the rapidly developing healthcare world, introducing them to modern healthcare advances such as EHI and Smart Healthcare Systems.
1. Intelligent Biomedical Technologies and Applications for Healthcare 5.0 - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. ix 7. About the editors - p. xi 8. Preface - p. xiii 9. Chapter 1 Profiling and validating Fast Healthcare Interoperability Resource for maternal and neonatal child health referrals at primary healthcare level through BlockMom - p. 1 10. Chapter 2 A cost-effective way of implementing big data in developing countries—Case of Malawi - p. 11 11. Chapter 3 Characterizing hospital admission patterns and length of stay in the emergency department at Mater Dei Hospital Malta - p. 29 12. Chapter 4 Machine learning approach for enhancement in healthcare - p. 65 13. Chapter 5 Disease detection and treatment methods - p. 73 14. Chapter 6 Hybrid fuzzy-based improved particle swarm optimization technique for cancer cell detection - p. 83 15. Chapter 7 A review on COVID-19 (SARS-CoV-2) pandemic: Using artificial intelligence and machine learning applications - p. 93 16. Chapter 8 Validity, reliability, and usability assessment of smartphone-based Health 5.0 application for measuring chronic low back pain - p. 111 17. Chapter 9 Healthcare 5.0 opportunities and challenges: A literature review - p. 133 18. Chapter 10 An impact of reliability in healthcare Internet of Things (HIoT) - p. 147 19. Chapter 11 Investigating the effect of a software intervention based on a theoretical behavior framework to encourage ergonomic compliance during computing device usage - p. 153 20. Chapter 12 Machine learning approach for post-covid disease prediction - p. 195 21. Chapter 13 Improving interoperability between health information technology systems used by mental health and acute hospitals - p. 205 22. Chapter 14 Data management in the healthcare sector—A review - p. 219 23. Chapter 15 Navigating the frontier: Integrating emerging biomedical technologies into modern healthcare - p. 229 24. Chapter 16 Healthcare cyber risk and its impact on healthcare - p. 245 25. Chapter 17 Machine and deep learning techniques for smart healthcare industry: Big picture and open research challenges - p. 255 26. Chapter 18 Precise brain stroke prediction using different machine learning algorithms - p. 277 27. Index - p. 285
Intelligent Biomedical Technologies and Applications for Healthcare 5.0, Volume Sixteen covers artificial health intelligence, biomedical image analysis, 5G, the Internet of Medical Things, intelligent healthcare systems, and extended health intelligence (EHI). This volume contains four sections. The focus of the first section is health data analytics and applications. The second section covers research on information exchange and knowledge sharing. The third section is on the Internet of Things (IoT) and the Internet of Everything (IoE)-based solutions. The final section focuses on the implementation, assessment, adoption, and management of healthcare informatics solutions. This new volume in the Advances in Ubiquitous Sensing Applications for Healthcare series focuses on innovative methods in the healthcare industry and will be useful for biomedical engineers, researchers, and students working in interdisciplinary fields of research. This volume bridges these newly developing technologies and the medical community in the rapidly developing healthcare world, introducing them to modern healthcare advances such as EHI and Smart Healthcare Systems.
1. Intelligent Biomedical Technologies and Applications for Healthcare 5.0 - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. ix 7. About the editors - p. xi 8. Preface - p. xiii 9. Chapter 1 Profiling and validating Fast Healthcare Interoperability Resource for maternal and neonatal child health referrals at primary healthcare level through BlockMom - p. 1 10. Chapter 2 A cost-effective way of implementing big data in developing countries—Case of Malawi - p. 11 11. Chapter 3 Characterizing hospital admission patterns and length of stay in the emergency department at Mater Dei Hospital Malta - p. 29 12. Chapter 4 Machine learning approach for enhancement in healthcare - p. 65 13. Chapter 5 Disease detection and treatment methods - p. 73 14. Chapter 6 Hybrid fuzzy-based improved particle swarm optimization technique for cancer cell detection - p. 83 15. Chapter 7 A review on COVID-19 (SARS-CoV-2) pandemic: Using artificial intelligence and machine learning applications - p. 93 16. Chapter 8 Validity, reliability, and usability assessment of smartphone-based Health 5.0 application for measuring chronic low back pain - p. 111 17. Chapter 9 Healthcare 5.0 opportunities and challenges: A literature review - p. 133 18. Chapter 10 An impact of reliability in healthcare Internet of Things (HIoT) - p. 147 19. Chapter 11 Investigating the effect of a software intervention based on a theoretical behavior framework to encourage ergonomic compliance during computing device usage - p. 153 20. Chapter 12 Machine learning approach for post-covid disease prediction - p. 195 21. Chapter 13 Improving interoperability between health information technology systems used by mental health and acute hospitals - p. 205 22. Chapter 14 Data management in the healthcare sector—A review - p. 219 23. Chapter 15 Navigating the frontier: Integrating emerging biomedical technologies into modern healthcare - p. 229 24. Chapter 16 Healthcare cyber risk and its impact on healthcare - p. 245 25. Chapter 17 Machine and deep learning techniques for smart healthcare industry: Big picture and open research challenges - p. 255 26. Chapter 18 Precise brain stroke prediction using different machine learning algorithms - p. 277 27. Index - p. 285
