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Deep Learning for Targeted Treatments

Transformation in Healthcare

Edited by Rishabha Malviya, Georghita Ghinea, Rajesh Kumar Dhanaraj, Balamurugan Balusamy and Sonali Sundram
Copyright: 2022   |   Status: Published
ISBN: 9781119857327  |  Hardcover  |  
454 pages
Price: $225 USD
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One Line Description
The book provides the direction for future research in deep learning
in terms of its role in targeted treatment, biological systems,
site-specific drug delivery, risk assessment in therapy, etc.

Audience
The book will be immensely interesting and useful to researchers and those working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry.

Description
Deep Learning for Targeted Treatments describes the importance of the deep learning framework for patient care, disease imaging/detection, and health management. Since deep learning can and does play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare.

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Author / Editor Details
Rishabha Malviya, PhD is an associate professor in the Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery, and characterization of natural polymers as pharmaceutical excipients. He has authored more than 150 research/review papers for national/international journals of repute. He has been granted more than 10 patents from different countries while a further 40 patents are published/under evaluation.

Gheorghita Ghinea, PhD, is a professor in Computing, Department of Computer Science Brunel University London. His research activities lie at the confluence of computer science, media, and psychology, and particularly interested in building semantically underpinned human-centered e-systems, particularly integrating human perceptual requirements. Has published more than 30+ articles and received 10+ research grants.

Rajesh Kumar Dhanaraj, PhD, is an associate professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed 20+ books on various technologies and 35+ articles and papers in various refereed journals and international conferences and contributed chapters to the books. His research interests include machine learning, cyber-physical systems, and wireless sensor networks. He is an Expert Advisory Panel Member of Texas Instruments Inc USA.

Balamurugan Balusamy, PhD, is a professor at Galgotias University. He has published 30+ books on various technologies as well as more than 150 journal articles, conferences, and book chapters.

Sonali Sundram completed B. Pharm & M. Pharm (pharmacology) from AKTU, Lucknow, and is working at Galgotias University, Greater Noida. Her areas of interests are neurodegeneration, clinical research, and artificial intelligence. She has more than 8 patents to her credit.

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Table of Contents
Preface
Acknowledgement
1. Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science
Dhanalekshmi Unnikrishnan Meenakshi, Selvasudha Nandakumar, Arul Prakash Francis, Pushpa Sweety, Shivkanya Fuloria, Neeraj Kumar Fuloria, Vetriselvan Subramaniyan and Shah Alam Khan
1.1 Introduction
1.2 Drug Discovery, Screening and Repurposing
1.3 DL and Pharmaceutical Formulation Strategy
1.3.1 DL in Dose and Formulation Prediction
1.3.2 DL in Dissolution and Release Studies
1.3.3 DL in the Manufacturing Process
1.4 Deep Learning Models for Nanoparticle-Based Drug Delivery
1.4.1 Nanoparticles With High Drug Delivery Capacities Using Perturbation Theory
1.4.2 Artificial Intelligence and Drug Delivery Algorithms
1.4.3 Nanoinformatics
1.5 Model Prediction for Site-Specific Drug Delivery
1.5.1 Prediction of Mode and a Site-Specific Action
1.5.2 Precision Medicine
1.6 Future Scope and Challenges
1.7 Conclusion
References
2. Role of Deep Learning, Blockchain and Internet of Things in Patient Care
Akanksha Sharma, Rishabha Malviya and Sonali Sundram
2.1 Introduction
2.2 IoT and WBAN in Healthcare Systems
2.2.1 IoT in Healthcare
2.2.2 WBAN
2.2.2.1 Key Features of Medical Networks in the Wireless Body Area
2.2.2.2 Data Transmission & Storage Health
2.2.2.3 Privacy and Security Concerns in Big Data
2.3 Blockchain Technology in Healthcare
2.3.1 Importance of Blockchain
2.3.2 Role of Blockchain in Healthcare
2.3.3 Benefits of Blockchain in Healthcare Applications
2.3.4 Elements of Blockchain
2.3.5 Situation Awareness and Healthcare Decision Support with Combined Machine Learning and Semantic Modeling
2.3.6 Mobile Health and Remote Monitoring
2.3.7 Different Mobile Health Application with Description of Usage in Area of Application
2.3.8 Patient-Centered Blockchain Mode
2.3.9 Electronic Medical Record
2.3.9.1 The Most Significant Barriers to Adoption Are
2.3.9.2 Concern Regarding Negative Unintended Consequences of Technology
2.4 Deep Learning in Healthcare
2.4.1 Deep Learning Models
2.4.1.1 Recurrent Neural Networks (RNN)
2.4.1.2 Convolutional Neural Networks (CNN)
2.4.1.3 Deep Belief Network (DBN)
2.4.1.4 Contrasts Between Models
2.4.1.5 Use of Deep Learning in Healthcare
2.5 Conclusion
2.6 Acknowledgments
References
3. Deep Learning on Site-Specific Drug Delivery System
Prem Shankar Mishra, Rakhi Mishra and Rupa Mazumder
3.1 Introduction
3.2 Deep Learning
3.2.1 Types of Algorithms Used in Deep Learning
3.2.1.1 Convolutional Neural Networks (CNNs)
3.2.1.2 Long Short-Term Memory Networks (LSTMs)
3.2.1.3 Recurrent Neural Networks
3.2.1.4 Generative Adversarial Networks (GANs)
3.2.1.5 Radial Basis Function Networks
3.2.1.6 Multilayer Perceptron
3.2.1.7 Self-Organizing Maps
3.2.1.8 Deep Belief Networks
3.3 Machine Learning and Deep Learning Comparison
3.4 Applications of Deep Learning in Drug Delivery System
3.5 Conclusion
References
4. Deep Learning Advancements in Target Delivery
Sudhanshu Mishra, Palak Gupta, Smriti Ojha, Vijay Sharma, Vicky Anthony and Disha Sharma
4.1 Introduction: Deep Learning and Targeted Drug Delivery
4.2 Different Models/Approaches of Deep Learning and Targeting Drug
4.3 QSAR Model
4.3.1 Model of Deep Long-Term Short-Term Memory
4.3.2 RNN Model
4.3.3 CNN Model
4.4 Deep Learning Process Applications in Pharmaceutical
4.5 Techniques for Predicting Pharmacotherapy
4.6 Approach to Diagnosis
4.7 Application
4.7.1 Deep Learning in Drug Discovery
4.7.2 Medical Imaging and Deep Learning Process
4.7.3 Deep Learning in Diagnostic and Screening
4.7.4 Clinical Trials Using Deep Learning Models
4.7.5 Learning for Personalized Medicine
4.8 Conclusion
Acknowledgment
References
5. Deep Learning and Precision Medicine: Lessons to Learn for the Preeminent Treatment for Malignant Tumors
Selvasudha Nandakumar, Shah Alam Khan, Poovi Ganesan, Pushpa Sweety, Arul Prakash Francis, Mahendran Sekar, Rukkumani Rajagopalan and Dhanalekshmi Unnikrishnan Meenakshi
5.1 Introduction
5.2 Role of DL in Gene Identification, Unique Genomic Analysis, and Precise Cancer Diagnosis
5.2.1 Gene Identification and Genome Data
5.2.2 Image Diagnosis
5.2.3 Radiomics, Radiogenomics, and Digital Biopsy
5.2.4 Medical Image Analysis in Mammography
5.2.5 Magnetic Resonance Imaging
5.2.6 CT Imaging
5.3 DL in Next-Generation Sequencing, Biomarkers, and Clinical Validation
5.3.1 Next-Generation Sequencing
5.3.2 Biomarkers and Clinical Validation
5.4 DL and Translational Oncology
5.4.1 Prediction
5.4.2 Segmentation
5.4.3 Knowledge Graphs and Cancer Drug Repurposing
5.4.4 Automated Treatment Planning
5.4.5 Clinical Benefits
5.5 DL in Clinical Trials—A Necessary Paradigm Shift
5.6 Challenges and Limitations
5.7 Conclusion
References
6. Personalized Therapy Using Deep Learning Advances
Nishant Gaur, Rashmi Dharwadkar and Jinsu Thomas
6.1 Introduction
6.2 Deep Learning
6.2.1 Convolutional Neural Networks
6.2.2 Autoencoders
6.2.3 Deep Belief Network (DBN)
6.2.4 Deep Reinforcement Learning
6.2.5 Generative Adversarial Network
6.2.6 Long Short-Term Memory Networks
References
7. Tele-Health Monitoring Using Artificial Intelligence Deep Learning Framework
Swati Verma, Rishabha Malviya, Md Aftab Alam and Bhuneshwar Dutta Tripathi
7.1 Introduction
7.2 Artificial Intelligence
7.2.1 Types of Artificial Intelligence
7.2.1.1 Machine Intelligence
7.2.1.2 Types of Machine Intelligence
7.2.2 Applications of Artificial Intelligence
7.2.2.1 Role in Healthcare Diagnostics
7.2.2.2 AI in Telehealth
7.2.2.3 Role in Structural Health Monitoring
7.2.2.4 Role in Remote Medicare Management
7.2.2.5 Predictive Analysis Using Big Data
7.2.2.6 AI’s Role in Virtual Monitoring of Patients
7.2.2.7 Functions of Devices
7.2.2.8 Clinical Outcomes Through Remote Patient Monitoring
7.2.2.9 Clinical Decision Support
7.2.3 Utilization of Artificial Intelligence in Telemedicine
7.2.3.1 Artificial Intelligence–Assisted Telemedicine
7.2.3.2 Telehealth and New Care Models
7.2.3.3 Strategy of Telecare Domain
7.2.3.4 Role of AI-Assisted Telemedicine in Various Domains
7.3 AI-Enabled Telehealth: Social and Ethical Considerations
7.4 Conclusion
References
8. Deep Learning Framework for Cancer Diagnosis and Treatment
Shiv Bahadur and Prashant Kumar
8.1 Deep Learning: An Emerging Field for Cancer Management
8.2 Deep Learning Framework in Diagnosis and Treatment of Cancer
8.3 Applications of Deep Learning in Cancer Diagnosis
8.3.1 Medical Imaging Through Artificial Intelligence
8.3.2 Biomarkers Identification in the Diagnosis of Cancer Through Deep Learning
8.3.3 Digital Pathology Through Deep Learning
8.3.4 Application of Artificial Intelligence in Surgery
8.3.5 Histopathological Images Using Deep Learning
8.3.6 MRI and Ultrasound Images Through Deep Learning
8.4 Clinical Applications of Deep Learning in the Management of Cancer
8.5 Ethical Considerations in Deep Learning–Based Robotic Therapy
8.6 Conclusion
Acknowledgments
References
9. Applications of Deep Learning in Radiation Therapy
Akanksha Sharma, Ashish Verma, Rishabha Malviya and Shalini Yadav
9.1 Introduction
9.2 History of Radiotherapy
9.3 Principal of Radiotherapy
9.4 Deep Learning
9.5 Radiation Therapy Techniques
9.5.1 External Beam Radiation Therapy
9.5.2 Three-Dimensional Conformal Radiation Therapy (3D-CRT)
9.5.3 Intensity Modulated Radiation Therapy (IMRT)
9.5.4 Image-Guided Radiation Therapy (IGRT)
9.5.5 Intraoperative Radiation Therapy (IORT)
9.5.6 Brachytherapy
9.5.7 Stereotactic Radiosurgery (SRS)
9.6 Different Role of Deep Learning with Corresponding Role of Medical Physicist
9.6.1 Deep Learning in Patient Assessment
9.6.1.1 Radiotherapy Results Prediction
9.6.1.2 Respiratory Signal Prediction
9.6.2 Simulation Computed Tomography
9.6.3 Targets and Organs-at-Risk Segmentation
9.6.4 Treatment Planning
9.6.4.1 Beam Angle Optimization
9.6.4.2 Dose Prediction
9.6.5 Other Role of Deep Learning in Corresponds with Medical Physicists
9.7 Conclusion
References
10. Application of Deep Learning in Radiation Therapy
Shilpa Rawat, Shilpa Singh, Md. Aftab Alam and Rishabha Malviya
10.1 Introduction
10.2 Radiotherapy
10.3 Principle of Deep Learning and Machine Learning
10.3.1 Deep Neural Networks (DNN)
10.3.2 Convolutional Neural Network
10.4 Role of AI and Deep Learning in Radiation Therapy
10.5 Platforms for Deep Learning and Tools for Radiotherapy
10.6 Radiation Therapy Implementation in Deep Learning
10.6.1 Deep Learning and Imaging Techniques
10.6.2 Image Segmentation
10.6.3 Lesion Segmentation
10.6.4 Computer-Aided Diagnosis
10.6.5 Computer-Aided Detection
10.6.6 Quality Assurance
10.6.7 Treatment Planning
10.6.8 Treatment Delivery
10.6.9 Response to Treatment
10.7 Prediction of Outcomes
10.7.1 Toxicity
10.7.2 Survival and the Ability to Respond
10.8 Deep Learning in Conjunction With Radiomoic
10.9 Planning for Treatment
10.9.1 Optimization of Beam Angle
10.9.2 Prediction of Dose
10.10 Deep Learning’s Challenges and Future Potential
10.11 Conclusion
References
11. Deep Learning Framework for Cancer
Pratishtha
11.1 Introduction
11.2 Brief History of Deep Learning
11.3 Types of Deep Learning Methods
11.4 Applications of Deep Learning
11.4.1 Toxicity Detection for Different Chemical Structures
11.4.2 Mitosis Detection
11.4.3 Radiology or Medical Imaging
11.4.4 Hallucination
11.4.5 Next-Generation Sequencing (NGS)
11.4.6 Drug Discovery
11.4.7 Sequence or Video Generation
11.4.8 Other Applications
11.5 Cancer
11.5.1 Factors
11.5.1.1 Heredity
11.5.1.2 Ionizing Radiation
11.5.1.3 Chemical Substances
11.5.1.4 Dietary Factors
11.5.1.5 Estrogen
11.5.1.6 Viruses
11.5.1.7 Stress
11.5.1.8 Age
11.5.2 Signs and Symptoms of Cancer
11.5.3 Types of Cancer Treatment Available
11.5.3.1 Surgery
11.5.3.2 Radiation Therapy
11.5.3.3 Chemotherapy
11.5.3.4 Immunotherapy
11.5.3.5 Targeted Therapy
11.5.3.6 Hormone Therapy
11.5.3.7 Stem Cell Transplant
11.5.3.8 Precision Medicine
11.5.4 Types of Cancer
11.5.4.1 Carcinoma
11.5.4.2 Sarcoma
11.5.4.3 Leukemia
11.5.4.4 Lymphoma and Myeloma
11.5.4.5 Central Nervous System (CNS) Cancers
11.5.5 The Development of Cancer (Pathogenesis) Cancer
11.6 Role of Deep Learning in Various Types of Cancer
11.6.1 Skin Cancer
11.6.1.1 Common Symptoms of Melanoma
11.6.1.2 Types of Skin Cancer
11.6.1.3 Prevention
11.6.1.4 Treatment
11.6.2 Deep Learning in Skin Cancer
11.6.3 Pancreatic Cancer
11.6.3.1 Symptoms of Pancreatic Cancer
11.6.3.2 Causes or Risk Factors of Pancreatic Cancer
11.6.3.3 Treatments of Pancreatic Cancer
11.6.4 Deep Learning in Pancreatic Cancer
11.6.5 Tobacco-Driven Lung Cancer
11.6.5.1 Symptoms of Lung Cancer
11.6.5.2 Causes or Risk Factors of Lung Cancer
11.6.5.3 Treatments Available for Lung Cancer
11.6.5.4 Deep Learning in Lung Cancer
11.6.6 Breast Cancer
11.6.6.1 Symptoms of Breast Cancer
11.6.6.2 Causes or Risk Factors of Breast Cancer
11.6.6.3 Treatments Available for Breast Cancer
11.6.7 Deep Learning in Breast Cancer
11.6.8 Prostate Cancer
11.6.9 Deep Learning in Prostate Cancer
11.7 Future Aspects of Deep Learning in Cancer
11.8 Conclusion
References
12. Cardiovascular Disease Prediction Using Deep Neural Network for Older People
Nagarjuna Telagam, B.Venkata Kranti and Nikhil Chandra Devarasetti
12.1 Introduction
12.2 Proposed System Model
12.2.1 Decision Tree Algorithm
12.2.1.1 Confusion Matrix
12.3 Random Forest Algorithm
12.4 Variable Importance for Random Forests
12.5 The Proposed Method Using a Deep Learning Model
12.5.1 Prevention of Overfitting
12.5.2 Batch Normalization
12.5.3 Dropout Technique
12.6 Results and Discussions
12.6.1 Linear Regression
12.6.2 Decision Tree Classifier
12.6.3 Voting Classifier
12.6.4 Bagging Classifier
12.6.5 Naïve Bayes
12.6.6 Logistic Regression
12.6.7 Extra Trees Classifier
12.6.8 K-Nearest Neighbor [KNN] Algorithm
12.6.9 Adaboost Classifier
12.6.10 Light Gradient Boost Classifier
12.6.11 Gradient Boosting Classifier
12.6.12 Stochastic Gradient Descent Algorithm
12.6.13 Linear Support Vector Classifier
12.6.14 Support Vector Machines
12.6.15 Gaussian Process Classification
12.6.16 Random Forest Classifier
12.7 Evaluation Metrics
12.8 Conclusion
References
13. Machine Learning: The Capabilities and Efficiency of Computers in Life Sciences
Shalini Yadav, Saurav Yadav, Shobhit Prakash Srivastava, Saurabh Kumar Gupta and Sudhanshu Mishra
13.1 Introduction
13.2 Supervised Learning
13.2.1 Workflow of Supervised Learning
13.2.2 Decision Tree
13.2.3 Support Vector Machine (SVM)
13.2.4 Naive Bayes
13.3 Deep Learning: A New Era of Machine Learning
13.4 Deep Learning in Artificial Intelligence (AI)
13.5 Using ML to Enhance Preventive and Treatment Insights
13.6 Different Additional Emergent Machine Learning Uses
13.6.1 Education
13.6.2 Pharmaceuticals
13.6.3 Manufacturing
13.7 Machine Learning
13.7.1 Neuroscience Research Advancements
13.7.2 Finding Patterns in Astronomical Data
13.8 Ethical and Social Issues Raised.... ! ! !
13.8.1 Reliability and Safety
13.8.2 Transparency and Accountability
13.8.3 Data Privacy and Security
13.8.4 Malicious Use of AI
13.8.5 Effects on Healthcare Professionals
13.9 Future of Machine Learning in Healthcare
13.9.1 A Better Patient Journey
13.9.2 New Ways to Deliver Care
13.10 Challenges and Hesitations
13.10.1 Not Overlord Assistant Intelligent
13.10.2 Issues with Unlabeled Data
13.11 Concluding Thoughts
Acknowledgments
References
Index

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