Deep Learning for Targeted Treatments: Transformation in Healthcare

Cover
	Title Page
	Copyright Page
Contents
Preface
Acknowledgement
1 Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	EULA