Intelligent Semantic Analysis for Healthcare explores the latest trends, developments, and future directions of intelligent semantic analysis techniques on retrieving and managing meaningful medical information for healthcare information systems. The book explores different computational methods, ideas, strategies, and techniques to analyze relevant healthcare information in an innovative and efficient way, thus bridging the gap between gathering and comprehending data with healthcare and biological applications. It offers a comprehensive view of intelligent semantic analysis in healthcare, bridging the gap between data collection and healthcare applications, and providing innovative computational methods for data analysis.
Sections focus on intelligent semantic analysis rather than broader topics of big data and healthcare analytics. Additionally, the book is geared towards practical approaches and innovative techniques for state-of-the-art and current challenges in healthcare data management.
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. Contributors - p. xiii 7. Preface - p. xvii 8. Part I. Foundational frameworks of intelligent healthcare - p. P1 9. Chapter 1. Fundamentals of data analysis for healthcare informatics - p. 3 10. Chapter 2. From efficiency to sustainability: The evolution of smart healthcare systems - p. 27 11. Chapter 3. Architectural analysis of cyber-physical systems for healthcare: A review - p. 47 12. Part II. The physical layer: IoT, wearables, and cyber-physical systems - p. P2 13. Chapter 4. Transforming healthcare with intelligent data analysis: Harnessing wearable technology, generative AI, and cloud computing - p. 77 14. Chapter 5. Current trends in IoT-enabled healthcare for wellness management - p. 89 15. Chapter 6. Intelligent health semantic interoperability for internet of martial art things: From wearables to standards - p. 99 16. Chapter 7. Medical cyber physical systems (MCPS) for patient monitoring and treatment - p. 125 17. Part III. The semantic heart: LLMs, knowledge graphs, and interoperability - p. P3 18. Chapter 8. Enhanced semantic model in healthcare - p. 145 19. Chapter 9. Large language models in healthcare semantics - p. 157 20. Chapter 10. Hybrid semantic statistical architectures for explainable and trustworthy clinical decision support systems - p. 177 21. Chapter 11. Semantic clinical decision support systems: challenges and future scope - p. 205 22. Part IV. Clinical intelligence: Domain-specific applications - p. P4 23. Chapter 12. Multimodal semantic integration in radiology: From pixels to clinical knowledge graphs - p. 227 24. Chapter 13. A framework for brain tumor (Glioma) analysis using SVM and graph convolutional networks - p. 245 25. Chapter 14. Sentinel Eye: Hybrid AI for noninvasive anemia detection using eye scans, symptoms, and history - p. 265 26. Part V. Trust and resilience: Security, privacy, and explainability - p. P5 27. Chapter 15. Semantic computing in healthcare: Bridging the gap between innovation and privacy security measures - p. 281 28. Chapter 16. Securing cyber physical systems in healthcare using blockchain - p. 305 29. Chapter 17. Exploring security and privacy challenges in intelligent semantic healthcare - p. 333 30. Part VI. Sustainability, policy, and the future landscape - p. P6 31. Chapter 18. Smart choices, sustainable futures: Advancing healthcare responsibly - p. 351 32. Chapter 19. Embedding sustainability into work-life balance practices with special reference to healthcare sector - p. 359 33. Chapter 20. The role of the National Health Digitization Agency (NHDA) in digitizing the Algerian health sector: Between reality and prospects contributors - p. 375 34. Chapter 21. Smart healthcare systems: In the era of metaverse, artificial intelligence, and data science - p. 407 35. Chapter 22. Challenges and future scope of intelligent semantic healthcare - p. 425 36. Index - p. 441
Intelligent Semantic Analysis for Healthcare explores the latest trends, developments, and future directions of intelligent semantic analysis techniques on retrieving and managing meaningful medical information for healthcare information systems. The book explores different computational methods, ideas, strategies, and techniques to analyze relevant healthcare information in an innovative and efficient way, thus bridging the gap between gathering and comprehending data with healthcare and biological applications. It offers a comprehensive view of intelligent semantic analysis in healthcare, bridging the gap between data collection and healthcare applications, and providing innovative computational methods for data analysis.
Sections focus on intelligent semantic analysis rather than broader topics of big data and healthcare analytics. Additionally, the book is geared towards practical approaches and innovative techniques for state-of-the-art and current challenges in healthcare data management.
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. Contributors - p. xiii 7. Preface - p. xvii 8. Part I. Foundational frameworks of intelligent healthcare - p. P1 9. Chapter 1. Fundamentals of data analysis for healthcare informatics - p. 3 10. Chapter 2. From efficiency to sustainability: The evolution of smart healthcare systems - p. 27 11. Chapter 3. Architectural analysis of cyber-physical systems for healthcare: A review - p. 47 12. Part II. The physical layer: IoT, wearables, and cyber-physical systems - p. P2 13. Chapter 4. Transforming healthcare with intelligent data analysis: Harnessing wearable technology, generative AI, and cloud computing - p. 77 14. Chapter 5. Current trends in IoT-enabled healthcare for wellness management - p. 89 15. Chapter 6. Intelligent health semantic interoperability for internet of martial art things: From wearables to standards - p. 99 16. Chapter 7. Medical cyber physical systems (MCPS) for patient monitoring and treatment - p. 125 17. Part III. The semantic heart: LLMs, knowledge graphs, and interoperability - p. P3 18. Chapter 8. Enhanced semantic model in healthcare - p. 145 19. Chapter 9. Large language models in healthcare semantics - p. 157 20. Chapter 10. Hybrid semantic statistical architectures for explainable and trustworthy clinical decision support systems - p. 177 21. Chapter 11. Semantic clinical decision support systems: challenges and future scope - p. 205 22. Part IV. Clinical intelligence: Domain-specific applications - p. P4 23. Chapter 12. Multimodal semantic integration in radiology: From pixels to clinical knowledge graphs - p. 227 24. Chapter 13. A framework for brain tumor (Glioma) analysis using SVM and graph convolutional networks - p. 245 25. Chapter 14. Sentinel Eye: Hybrid AI for noninvasive anemia detection using eye scans, symptoms, and history - p. 265 26. Part V. Trust and resilience: Security, privacy, and explainability - p. P5 27. Chapter 15. Semantic computing in healthcare: Bridging the gap between innovation and privacy security measures - p. 281 28. Chapter 16. Securing cyber physical systems in healthcare using blockchain - p. 305 29. Chapter 17. Exploring security and privacy challenges in intelligent semantic healthcare - p. 333 30. Part VI. Sustainability, policy, and the future landscape - p. P6 31. Chapter 18. Smart choices, sustainable futures: Advancing healthcare responsibly - p. 351 32. Chapter 19. Embedding sustainability into work-life balance practices with special reference to healthcare sector - p. 359 33. Chapter 20. The role of the National Health Digitization Agency (NHDA) in digitizing the Algerian health sector: Between reality and prospects contributors - p. 375 34. Chapter 21. Smart healthcare systems: In the era of metaverse, artificial intelligence, and data science - p. 407 35. Chapter 22. Challenges and future scope of intelligent semantic healthcare - p. 425 36. Index - p. 441
