Multimodal Data Fusion in Healthcare: AI Approaches for Precision Diagnosis explores the transformative potential of AI in modern medicine by integrating diverse data sources such as medical imaging, genomics, EHRs, and wearable sensors. It highlights how AI technologies are revolutionizing healthcare systems through personalized and proactive diagnostics. The book covers cutting-edge methodologies, real-world applications, and the challenges of multimodal data fusion. Topics include AI-driven diagnostics, precision medicine, real-time patient monitoring, and the integration of clinical, genomic, and wearable data, providing both theoretical foundations and practical insights. This book is essential for healthcare professionals, data scientists, and engineers, offering clear frameworks for integrating diverse data types. It addresses crucial issues like data interoperability, privacy, and technical constraints, providing practical solutions. It serves as an invaluable reference for understanding and applying AI advancements in diagnostic precision and personalized medicine.
1. Chapter 1. Introduction to multimodal data fusion in healthcare - p. 1 2. Chapter 2. DeepSeek and multimodal AI in healthcare: enabling precision diagnosis and smart clinical decision support - p. 21 3. Chapter 3. From data to diagnosis: enhancing medical predictions with explainable AI - p. 41 4. Chapter 4. Multimodal data fusion healthcare applications with Internet of Things - p. 63 5. Chapter 5. Integration of natural language processing with electronic health records - p. 81 6. Chapter 6. The neurological weather forecast: predicting cognitive storms before they arise - p. 95 7. Chapter 7. AI–driven models for early detection and prevention of cardiovascular diseases - p. 111 8. Chapter 8. Wearable sensor and clinical data fusion for cardiovascular risk prediction - p. 123 9. Chapter 9. ACUCARE—an analysis of multiple disease prediction system - p. 137 10. Chapter 10. MobileSwin-GI: a state-of-the-art hybrid deep learning model for gastrointestinal disease diagnosis - p. 155 11. Chapter 11. Accelerating rare disease detection using generative artificial intelligence, federated learning, and multimodal data integration - p. 181 12. Chapter 12. Enhancing brain tumor diagnosis using multimodal magnetic resonance imaging and computed tomography imaging with machine learning - p. 201 13. Chapter 13. Artificial intelligence–driven multimodal fusion for early detection of neurodegenerative diseases - p. 227 14. Chapter 14. Multimodal data fusion with machine learning and deep learning for improved attention–deficit hyperactivity disorder diagnosis - p. 249 15. Chapter 15. A coherent review of deep learning techniques for in-depth analysis of electroencephalogram signals - p. 267 16. Chapter 16. Multimodal sentiment analysis: combining bidirectional encoder representations from transformers for text and visual features - p. 291 17. Chapter 17. Social media bigotry detection - p. 309 18. Chapter 18. Challenges and ethics in multimodal healthcare artificial intelligence - p. 327 19. Chapter 19. Improving tumor diagnosis and prognosis through deep learning and medical imaging - p. 335 20. Chapter 20. Navigating risks: a deep dive into healthcare industry analysis - p. 347 21. Chapter 21. Conclusion and future directions in multimodal data fusion in healthcare - p. 361 22. Index - p. 371
Multimodal Data Fusion in Healthcare: AI Approaches for Precision Diagnosis explores the transformative potential of AI in modern medicine by integrating diverse data sources such as medical imaging, genomics, EHRs, and wearable sensors. It highlights how AI technologies are revolutionizing healthcare systems through personalized and proactive diagnostics. The book covers cutting-edge methodologies, real-world applications, and the challenges of multimodal data fusion. Topics include AI-driven diagnostics, precision medicine, real-time patient monitoring, and the integration of clinical, genomic, and wearable data, providing both theoretical foundations and practical insights. This book is essential for healthcare professionals, data scientists, and engineers, offering clear frameworks for integrating diverse data types. It addresses crucial issues like data interoperability, privacy, and technical constraints, providing practical solutions. It serves as an invaluable reference for understanding and applying AI advancements in diagnostic precision and personalized medicine.
1. Chapter 1. Introduction to multimodal data fusion in healthcare - p. 1 2. Chapter 2. DeepSeek and multimodal AI in healthcare: enabling precision diagnosis and smart clinical decision support - p. 21 3. Chapter 3. From data to diagnosis: enhancing medical predictions with explainable AI - p. 41 4. Chapter 4. Multimodal data fusion healthcare applications with Internet of Things - p. 63 5. Chapter 5. Integration of natural language processing with electronic health records - p. 81 6. Chapter 6. The neurological weather forecast: predicting cognitive storms before they arise - p. 95 7. Chapter 7. AI–driven models for early detection and prevention of cardiovascular diseases - p. 111 8. Chapter 8. Wearable sensor and clinical data fusion for cardiovascular risk prediction - p. 123 9. Chapter 9. ACUCARE—an analysis of multiple disease prediction system - p. 137 10. Chapter 10. MobileSwin-GI: a state-of-the-art hybrid deep learning model for gastrointestinal disease diagnosis - p. 155 11. Chapter 11. Accelerating rare disease detection using generative artificial intelligence, federated learning, and multimodal data integration - p. 181 12. Chapter 12. Enhancing brain tumor diagnosis using multimodal magnetic resonance imaging and computed tomography imaging with machine learning - p. 201 13. Chapter 13. Artificial intelligence–driven multimodal fusion for early detection of neurodegenerative diseases - p. 227 14. Chapter 14. Multimodal data fusion with machine learning and deep learning for improved attention–deficit hyperactivity disorder diagnosis - p. 249 15. Chapter 15. A coherent review of deep learning techniques for in-depth analysis of electroencephalogram signals - p. 267 16. Chapter 16. Multimodal sentiment analysis: combining bidirectional encoder representations from transformers for text and visual features - p. 291 17. Chapter 17. Social media bigotry detection - p. 309 18. Chapter 18. Challenges and ethics in multimodal healthcare artificial intelligence - p. 327 19. Chapter 19. Improving tumor diagnosis and prognosis through deep learning and medical imaging - p. 335 20. Chapter 20. Navigating risks: a deep dive into healthcare industry analysis - p. 347 21. Chapter 21. Conclusion and future directions in multimodal data fusion in healthcare - p. 361 22. Index - p. 371
