Deep Learning in Personalized Healthcare and Decision Support

1st Edition - July 20, 2023
Editors: Harish Garg, Jyotir Moy Chatterjee
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 9 4 1 3 - 9
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 9 4 1 4 - 6

Deep Learning in Personalized Healthcare and Decision Support discusses the potential of deep learning technologies in the healthcare sector. The book covers the applicati… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Acknowledgments
Chapter 1. The future of health diagnosis and treatment: an exploration of deep learning frameworks and innovative applications
1. Introduction
2. Computational deep learning frameworks for health monitoring
3. Advanced architectures and core concepts of deep learning in smart health
4. Comparative analysis of deep learning frameworks for different disease detection
5. Advantages of deep learning in smart medical healthcare analytics
6. Deep leering applications for disease prediction
7. Deep learning in research and development
8. Future challenges of deep learning in smart health diagnosis and treatment
9. Limitations of deep learning frameworks
10. Conclusion and future scope
Chapter 2. Fermatean fuzzy approach of diseases diagnosis based on new correlation coefficient operators
1. Introduction
2. Fermatean fuzzy sets and their correlation operators
3. New Fermatean fuzzy correlation operators
4. Application example of medical diagnosis
5. Conclusion
Chapter 3. Application of Deep-Q learning in personalized health care Internet of Things ecosystem
1. Introduction
2. Related work
3. Proposed mechanism
4. Experimental results
5. Future directions
6. Conclusion
Chapter 4. Dia-Glass: a calorie-calculating spectacles for diabetic patients using augmented reality and faster R-CNN
1. Introduction
2. Related works
3. Diabetes: categories, concerns and prevalence
4. System methodology
5. Result and analysis
6. Conclusion
Chapter 5. Synthetic medical image augmentation: a GAN-based approach for melanoma skin lesion classification with deep learning
1. Introduction
2. Skin lesion classification methodology
3. Generation of synthetic skin lesions
4. Experimental results
5. Performance comparison
6. Conclusion and future work
7. Conflict of interest
Chapter 6. Artificial intelligence representation model for drug–target interaction with contemporary knowledge and development
1. Introduction
2. AI privacy and security challenges
3. Ensuring transparency, explain ability, and intelligibility
4. Drug discovery and precision medicine with deep learning
5. Clinical decision support and predictive analytics
6. Natural language processing in drug
7. Predictive analytics has a wide range of practical applications, including the following
8. Conclusion
Chapter 7. Review of fog and edge computing–based smart health care system using deep learning approaches
1. Introduction
2. Literature review
3. Healthcare using artificial intelligence
4. Efficient health care system with improved performance
5. Conclusion
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
1. Introduction
2. Green infrastructure measures in the legislature [4]
3. Employment creation as part of the sustainable recovery
4. Carbon power
5. Smart buildings [15]
6. Climate change disclosure laws [16]
7. The action of biodiversity [17]
8. Case study
9. Future pandemic preparedness [22]
10. Recent literature
11. Conclusions
Chapter 9. Hybrid and automated segmentation algorithm for malignant melanoma using chain codes and active contours
1. Introduction
2. Materials and methods
3. Proposed methodology
4. Results and discussions
5. Conclusion and future scope
Chapter 10. Development of a predictive model for classifying colorectal cancer using principal component analysis
1. Introduction
2. Related works
3. Methodology
4. Results and discussions
5. Conclusion
Chapter 11. Using deep learning via long-short-term memory model prediction of COVID-19 situation in India
1. Introduction
2. Literature review
3. How to protect yourself from COVID-19
4. Facts about the vaccine against the COVID-19
5. Materials and methods
6. Results discussion
7. Conclusion
Chapter 12. Post-COVID-19 Indian healthcare system: Challenges and solutions
1. Creation of robust healthcare system—A nationwide priority and its emergence
2. Pandemonium scenes
3. Efforts for healthcare system development
4. Providing treatment to all amidst difficulties
5. Corona warriors and their woes
6. Transformation of Indian healthcare sector post COVID-19
7. Healthcare component 1—Hospitals
8. Healthcare component 2—Pharmaceutical industry
9. Healthcare component 3—Medical devices and equipment
10. Healthcare component 4—Diagnostics
11. Healthcare component 5—Telemedicine
12. Healthcare component 6—medical insurance
13. Healthcare component 7—Medical tourism
14. No need for complacency
15. Strength weakness threat opportunity (SWOT) analysis of Indian healthcare industry in the post-COVID-19 era
16. Future of Indian healthcare system
17. Conclusion
Chapter 13. SWOT Perspective of the Internet of Healthcare Things
1. Introduction
2. The IoT in healthcare systems
3. Key merits of the IoT during the COVID-19 pandemic
4. IoT processes for combatting COVID-19
5. The IoT's overall impacts in relation to COVID-19 concerns
6. Global technology developments to quickly treat COVID-19 cases
7. IoT applications to fight COVID-19
8. IoT challenges in the aftermath of COVID-19
9. Strengths, weaknesses, opportunities, and threats (SWOT) analysis
10. An answer to the problems of the COVID-19 battle
11. Conclusion
Chapter 14. Deep learning for clinical decision-making and improved healthcare outcome
1. Introduction
2. Application of deep learning machine (DLM) in clinical decision-making in the diagnosis of different diseases
3. Application of DLM in patient care plan in chronic disease management and rehabilitation
4. Application in cardiovascular medicine
5. Application in Pulmonary Disease
6. Role of DLM in population level future disease prediction
7. Effectiveness of DLM in screening and referral of clinical cases in remote and poor access areas
8. Conclusion
Chapter 15. Development of a no-regret deep learning framework for efficient clinical decision-making
1. Introduction
2. Related works
3. Proposed mechanism
4. Experimental results
5. Future directions
6. Conclusion
Chapter 16. Symptom-based diagnosis of diseases for primary health check-ups using biomedical text mining
1. Introduction
2. Natural language processing
3. System architecture of primary diagnosis of diseases
4. Case study
5. Results and discussion
Chapter 17. “Deep learning” for healthcare: Opportunities, threats, and challenges
1. Introduction
2. Machine learning and deep learning
3. Deep learning and its opportunities in healthcare
4. Data analysis and statistical modules
5. Deep learning and immunization management
6. Role in “COVID-19” vaccination
7. Role in future disease prediction
8. Public health misinformation
9. Contact tracing and pandemic management
10. Remote healthcare
11. Telemedicine
12. Virtual healthcare platforms
13. Online healthcare education: opportunities and challenges
14. Hospital, clinical research, and healthcare facilities
15. Electronic health records (EHRs)
16. Clinical imaging
17. Genomics
18. Drug discovery and precision medicine
19. Biobank
20. Conclusion
Chapter 18. Deep learning IoT in medical and healthcare
1. Introduction
2. Review of literature
3. Deep learning: Algorithms and its application
4. IoT and its applications
5. Conclusion
Chapter 19. Deep learning in drug discovery
1. Introduction
2. Literature review
3. Approaches of machine and deep learning for drug target
4. Practical applications of QSAR-based virtual screening
5. Conclusion and future scope
Chapter 20. Avant-garde techniques in machine for detecting financial fraud in healthcare
1. Introduction
2. Survey of frequently used algorithms for machine learning
3. Background study
4. Materials and methods
5. Conclusion
6. Future work
Chapter 21. Predicting mental health using social media: A roadmap for future development
1. Introduction
2. Mental disorders and big social data
3. Assessment strategies
4. Social data configuration
5. Prediction algorithms
6. Evaluation
7. Status, challenges, and future direction
8. Conclusion
Chapter 22. Applied picture fuzzy sets with its picture fuzzy database for identification of patients in a hospital
1. Introduction
2. Research background
3. The proposed model
4. Experimental results
5. Conclusions
Chapter 23. A deep learning framework for surgery action detection
1. Introduction
2. Literature review
3. Proposed approach
4. YOLOv5
5. EfficientDet
6. EfficientNet
7. Proposed ensemble network
8. Experiment
9. Result and discussion
10. Conclusion
Chapter 24. Understanding of healthcare problems and solutions using deep learning
1. Introduction
2. Related work
3. Methods
4. Standard methods for learning
5. Conclusion and future directions
Chapter 25. Deep convolution classification model-based COVID-19 chest CT image classification
1. Introduction
2. Related works
3. Proposed architecture and workflow
4. Experiment
5. Experimental results and discussions
6. Conclusions and future work
Chapter 26. Internet of Medical Things in curbing pandemics
1. Introduction
2. Cognitive Internet of Things (CIoT)
3. Internet of Medical Things
4. Smart e-healthcare
5. IoMTS emerging technologies
6. POC biosensing for infectious illnesses with IoMT assistance
7. IoT's primary benefits for the COVID-19 pandemic
8. Significant applications of IoT for COVID-19 pandemic
9. Technologies of IoT for the healthcare during COVID-19 pandemic
10. IoT-enabled healthcare helpful during COVID-19 pandemic
11. Conclusion
Index

Harish Garg

Dr. Garg is Associate Professor of Mathematics at Thapar Institute of Engineering and Technology, Patiala, Punjab, India. He is the recipient of the Obada-Prize 2022 – Young Distinguished Researchers. He is also the recipient of the Top-Cited paper by an India-based author (2015 – 2019) from Elsevier Publisher. He also serves as an advisory board member of the Universal Scientific Education and Research Network (USERN). Dr. Garg's research interests include computational intelligence, multi-criteria decision making, evolutionary algorithms, reliability analysis, expert systems, and decision support systems, computing with words, and soft computing. He has authored more than 400 papers published in refereed international journals. He has also authored seven book chapters. He has also edited 8 books from Elsevier, Springer, and other publishers. Dr. Garg also serves on editorial boards of several leading international journals, this includes the Founding Editor-in-Chief of the Journal of Computational and Cognitive Engineering. He is also the Associate Editor of IEEE Transaction of Fuzzy Systems, Soft Computing, Alexandria Engineering Journal, Journal of Intelligent & Fuzzy Systems, Complex and Intelligent Systems, Journal of Industrial & Management Optimization, and CAAI Transactions on Intelligence Technology.

Affiliations and expertise

Associate Professor, School of Mathematics, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

Jyotir Moy Chatterjee

Jyotir Moy Chatterjee is currently an Assistant Professor in Department of Computer Science and Engineering at Graphic Era (Deemed to be University), in Dehradun, India. He also serves as a Visiting Faculty member in Information Technology at Lord Buddha Education Foundation, which is affiliated with the Asia Pacific University of Technology & Innovation in Kathmandu, Nepal. His research interests focus on advancements in Machine Learning and Deep Learning.

Affiliations and expertise

Assistant Professor, Department of CSE, Graphic Era University, Dehradun, India

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