Advancing Healthcare through Decision Intelligence: Machine Learning, Robotics, and Analytics in Biomedical Informatics demonstrates real-world applications of decision intelligence - specifically machine learning, robotics, and analytics - to drive innovation and improvements in healthcare delivery and outcomes. The book provides a comprehensive overview of the latest developments in decision intelligence technologies, and offers a practical resource that can help navigate through the complex landscape of healthcare decision-making. In 5 themes Advancing Healthcare through Decision Intelligence: Machine Learning, Robotics, and Analytics in Biomedical Informatics focuses on the use of data analytics aad management techniques in healthcare to improve decision-making and healthcare outcomes, on the use of robotics, robots and genomics to improve biomedical informatics, on the use of genomics, clinical decision support systems (CDSS), machine learning, and deep learning to improve precision medicine and personalized treatment and on the ethical implications of using artificial intelligence (AI) in healthcare, ensuring trust in the technology, and making sure that it can be explained and understood by all stakeholders. This book is a valuable resource for health professionals, scientists and researchers, health practitioners, students, and all those who wish to broaden their knowledge in the allied field.
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. xiii 7. Chapter 1. Foundations of decision intelligence in healthcare: Integrating machine learning, robotics, and biomedical analytics - p. 1 8. Theme 1: Biomedical informatics: data analytics for improved healthcare outcomes - p. 6 9. Chapter 2. Deep learning techniques for agricultural plant health assessment: a case study on tomato plant - p. 11 10. Chapter 3. A systematic analysis of effectiveness of music therapy in children with autism spectrum disorder - p. 33 11. Chapter 4. Bioinformatics, healthcare informatics and analytics: an imperative for improved healthcare system - p. 49 12. Chapter 5. Measuring the impact of predictive analytics on patient satisfaction - p. 77 13. Theme 2: Decision intelligence in biomedical data analytics and management - p. 90 14. Chapter 6. Hybrid optimization of bag composition for disease diagnosis: integrating teaching-learning-based optimization with genetic algorithm - p. 95 15. Theme 3: Robotics and robots in biomedical informatics - p. 102 16. Chapter 7. Blockchain in healthcare: Unveiling trends and applications through bibliometric and thematic analysis - p. 105 17. Chapter 8. Enhancing social skills development in children with autism through robotic interventions - p. 119 18. Chapter 9. Intelligent ankle-foot prosthetics: from engineering fundamentals to integrated artificial intelligence systems - p. 127 19. Theme 4: Precision medicine and personalized treatment - p. 142 20. Chapter 10. Precision medicine and personalized treatment - p. 151 21. Chapter 11. Voxel-based analysis and advanced techniques for MRI scan classification in early diagnosis of Parkinson’s disease - p. 175 22. Chapter 12. Predicting metallic implant degradation through patient-specific computational modeling - p. 195 23. Theme 5: Ethics, trust, and explainability in biomedical analytics - p. 206 24. Chapter 13. Ethical concerns in healthcare analytics: exploring the complexities of data-driven decision making - p. 213 25. Index - p. 235
Advancing Healthcare through Decision Intelligence: Machine Learning, Robotics, and Analytics in Biomedical Informatics demonstrates real-world applications of decision intelligence - specifically machine learning, robotics, and analytics - to drive innovation and improvements in healthcare delivery and outcomes. The book provides a comprehensive overview of the latest developments in decision intelligence technologies, and offers a practical resource that can help navigate through the complex landscape of healthcare decision-making. In 5 themes Advancing Healthcare through Decision Intelligence: Machine Learning, Robotics, and Analytics in Biomedical Informatics focuses on the use of data analytics aad management techniques in healthcare to improve decision-making and healthcare outcomes, on the use of robotics, robots and genomics to improve biomedical informatics, on the use of genomics, clinical decision support systems (CDSS), machine learning, and deep learning to improve precision medicine and personalized treatment and on the ethical implications of using artificial intelligence (AI) in healthcare, ensuring trust in the technology, and making sure that it can be explained and understood by all stakeholders. This book is a valuable resource for health professionals, scientists and researchers, health practitioners, students, and all those who wish to broaden their knowledge in the allied field.
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. xiii 7. Chapter 1. Foundations of decision intelligence in healthcare: Integrating machine learning, robotics, and biomedical analytics - p. 1 8. Theme 1: Biomedical informatics: data analytics for improved healthcare outcomes - p. 6 9. Chapter 2. Deep learning techniques for agricultural plant health assessment: a case study on tomato plant - p. 11 10. Chapter 3. A systematic analysis of effectiveness of music therapy in children with autism spectrum disorder - p. 33 11. Chapter 4. Bioinformatics, healthcare informatics and analytics: an imperative for improved healthcare system - p. 49 12. Chapter 5. Measuring the impact of predictive analytics on patient satisfaction - p. 77 13. Theme 2: Decision intelligence in biomedical data analytics and management - p. 90 14. Chapter 6. Hybrid optimization of bag composition for disease diagnosis: integrating teaching-learning-based optimization with genetic algorithm - p. 95 15. Theme 3: Robotics and robots in biomedical informatics - p. 102 16. Chapter 7. Blockchain in healthcare: Unveiling trends and applications through bibliometric and thematic analysis - p. 105 17. Chapter 8. Enhancing social skills development in children with autism through robotic interventions - p. 119 18. Chapter 9. Intelligent ankle-foot prosthetics: from engineering fundamentals to integrated artificial intelligence systems - p. 127 19. Theme 4: Precision medicine and personalized treatment - p. 142 20. Chapter 10. Precision medicine and personalized treatment - p. 151 21. Chapter 11. Voxel-based analysis and advanced techniques for MRI scan classification in early diagnosis of Parkinson’s disease - p. 175 22. Chapter 12. Predicting metallic implant degradation through patient-specific computational modeling - p. 195 23. Theme 5: Ethics, trust, and explainability in biomedical analytics - p. 206 24. Chapter 13. Ethical concerns in healthcare analytics: exploring the complexities of data-driven decision making - p. 213 25. Index - p. 235
