Deep Learning in Personalized Healthcare and Decision Support discusses the potential of deep learning technologies in the healthcare sector. The book covers the application of deep learning tools and techniques in diverse areas of healthcare, such as medical image classification, telemedicine, clinical decision support system, clinical trials, electronic health records, precision medication, Parkinson disease detection, genomics, and drug discovery. In addition, it discusses the use of DL for fraud detection and internet of things. This is a valuable resource for researchers, graduate students and healthcare professionals who are interested in learning more about deep learning applied to the healthcare sector.
Although there is an increasing interest by clinicians and healthcare workers, they still lack enough knowledge to efficiently choose and make use of technologies currently available. This book fills that knowledge gap by bringing together experts from technology and clinical fields to cover the topics in depth.
1. Deep Learning in Personalized Healthcare and Decision Support - p. i 2. Cover - p. i 3. Title Page - p. ii 4. Copyright - p. iii 5. Table of Contents - p. x 6. Contributors - p. xi 7. Preface - p. xiv 8. Acknowledgments - p. xvi 9. Chapter 1 The future of health diagnosis and treatment: an exploration of deep learning frameworks and innovative applications - p. 2 10. Chapter 2 Fermatean fuzzy approach of diseases diagnosis based on new correlation coefficient operators - p. 24 11. Chapter 3 Application of Deep-Q learning in personalized health care Internet of Things ecosystem - p. 40 12. Chapter 4 Dia-Glass: a calorie-calculating spectacles for diabetic patients using augmented reality and faster R-CNN - p. 50 13. Chapter 5 Synthetic medical image augmentation: a GAN-based approach for melanoma skin lesion classification with deep learning - p. 70 14. Chapter 6 Artificial intelligence representation model for drug–target interaction with contemporary knowledge and development - p. 82 15. Chapter 7 Review of fog and edge computing–based smart health care system using deep learning approaches - p. 96 16. Chapter 8 Deep learning in healthcare: opportunities, threats, and challenges in a green smart environment solution for smart buildings and green cities—Towards combating COVID-19 - p. 108 17. Chapter 9 Hybrid and automated segmentation algorithm for malignant melanoma using chain codes and active contours - p. 120 18. Chapter 10 Development of a predictive model for classifying colorectal cancer using principal component analysis - p. 132 19. Chapter 11 Using deep learning via long-short-term memory model prediction of COVID-19 situation in India - p. 144 20. Chapter 12 Post-COVID-19 Indian healthcare system: Challenges and solutions - p. 164 21. Chapter 13 SWOT Perspective of the Internet of Healthcare Things - p. 176 22. Chapter 14 Deep learning for clinical decision-making and improved healthcare outcome - p. 188 23. Chapter 15 Development of a no-regret deep learning framework for efficient clinical decision-making - p. 204 24. Chapter 16 Symptom-based diagnosis of diseases for primary health check-ups using biomedical text mining - p. 216 25. Chapter 17 “Deep learning” for healthcare: Opportunities, threats, and challenges - p. 226 26. Chapter 18 Deep learning IoT in medical and healthcare - p. 246 27. Chapter 19 Deep learning in drug discovery - p. 264 28. Chapter 20 Avant-garde techniques in machine for detecting financial fraud in healthcare - p. 278 29. Chapter 21 Predicting mental health using social media: A roadmap for future development - p. 286 30. Chapter 22 Applied picture fuzzy sets with its picture fuzzy database for identification of patients in a hospital - p. 306 31. Chapter 23 A deep learning framework for surgery action detection - p. 316 32. Chapter 24 Understanding of healthcare problems and solutions using deep learning - p. 330 33. Chapter 25 Deep convolution classification model-based COVID-19 chest CT image classification - p. 344 34. Chapter 26 Internet of Medical Things in curbing pandemics - p. 358 35. Index - p. 373
Deep Learning in Personalized Healthcare and Decision Support discusses the potential of deep learning technologies in the healthcare sector. The book covers the application of deep learning tools and techniques in diverse areas of healthcare, such as medical image classification, telemedicine, clinical decision support system, clinical trials, electronic health records, precision medication, Parkinson disease detection, genomics, and drug discovery. In addition, it discusses the use of DL for fraud detection and internet of things. This is a valuable resource for researchers, graduate students and healthcare professionals who are interested in learning more about deep learning applied to the healthcare sector.
Although there is an increasing interest by clinicians and healthcare workers, they still lack enough knowledge to efficiently choose and make use of technologies currently available. This book fills that knowledge gap by bringing together experts from technology and clinical fields to cover the topics in depth.
1. Deep Learning in Personalized Healthcare and Decision Support - p. i 2. Cover - p. i 3. Title Page - p. ii 4. Copyright - p. iii 5. Table of Contents - p. x 6. Contributors - p. xi 7. Preface - p. xiv 8. Acknowledgments - p. xvi 9. Chapter 1 The future of health diagnosis and treatment: an exploration of deep learning frameworks and innovative applications - p. 2 10. Chapter 2 Fermatean fuzzy approach of diseases diagnosis based on new correlation coefficient operators - p. 24 11. Chapter 3 Application of Deep-Q learning in personalized health care Internet of Things ecosystem - p. 40 12. Chapter 4 Dia-Glass: a calorie-calculating spectacles for diabetic patients using augmented reality and faster R-CNN - p. 50 13. Chapter 5 Synthetic medical image augmentation: a GAN-based approach for melanoma skin lesion classification with deep learning - p. 70 14. Chapter 6 Artificial intelligence representation model for drug–target interaction with contemporary knowledge and development - p. 82 15. Chapter 7 Review of fog and edge computing–based smart health care system using deep learning approaches - p. 96 16. Chapter 8 Deep learning in healthcare: opportunities, threats, and challenges in a green smart environment solution for smart buildings and green cities—Towards combating COVID-19 - p. 108 17. Chapter 9 Hybrid and automated segmentation algorithm for malignant melanoma using chain codes and active contours - p. 120 18. Chapter 10 Development of a predictive model for classifying colorectal cancer using principal component analysis - p. 132 19. Chapter 11 Using deep learning via long-short-term memory model prediction of COVID-19 situation in India - p. 144 20. Chapter 12 Post-COVID-19 Indian healthcare system: Challenges and solutions - p. 164 21. Chapter 13 SWOT Perspective of the Internet of Healthcare Things - p. 176 22. Chapter 14 Deep learning for clinical decision-making and improved healthcare outcome - p. 188 23. Chapter 15 Development of a no-regret deep learning framework for efficient clinical decision-making - p. 204 24. Chapter 16 Symptom-based diagnosis of diseases for primary health check-ups using biomedical text mining - p. 216 25. Chapter 17 “Deep learning” for healthcare: Opportunities, threats, and challenges - p. 226 26. Chapter 18 Deep learning IoT in medical and healthcare - p. 246 27. Chapter 19 Deep learning in drug discovery - p. 264 28. Chapter 20 Avant-garde techniques in machine for detecting financial fraud in healthcare - p. 278 29. Chapter 21 Predicting mental health using social media: A roadmap for future development - p. 286 30. Chapter 22 Applied picture fuzzy sets with its picture fuzzy database for identification of patients in a hospital - p. 306 31. Chapter 23 A deep learning framework for surgery action detection - p. 316 32. Chapter 24 Understanding of healthcare problems and solutions using deep learning - p. 330 33. Chapter 25 Deep convolution classification model-based COVID-19 chest CT image classification - p. 344 34. Chapter 26 Internet of Medical Things in curbing pandemics - p. 358 35. Index - p. 373
