Details

Deep Learning for Targeted Treatments

Transformation in Healthcare
1. Aufl.

von: Rishabha Malviya, Gheorghita Ghinea, Rajesh Kumar Dhanaraj, Balamurugan Balusamy, Sonali Sundram
173,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	08.09.2022
ISBN/EAN:	9781119857976
Sprache:	englisch
Anzahl Seiten:	464

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Beschreibungen

Titelbeschreibung

DEEP LEARNING FOR TREATMENTS 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. 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. 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.

Inhaltsverzeichnis

Preface xvii Acknowledgement xix 1 Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science 1 Dhanalekshmi Unnikrishnan Meenakshi, Selvasudha Nandakumar, Arul Prakash Francis, Pushpa Sweety, Shivkanya Fuloria, Neeraj Kumar Fuloria, Vetriselvan Subramaniyan and Shah Alam Khan 1.1 Introduction 2 1.2 Drug Discovery, Screening and Repurposing 5 1.3 DL and Pharmaceutical Formulation Strategy 11 1.3.1 DL in Dose and Formulation Prediction 11 1.3.2 DL in Dissolution and Release Studies 15 1.3.3 DL in the Manufacturing Process 16 1.4 Deep Learning Models for Nanoparticle-Based Drug Delivery 19 1.4.1 Nanoparticles With High Drug Delivery Capacities Using Perturbation Theory 20 1.4.2 Artificial Intelligence and Drug Delivery Algorithms 21 1.4.3 Nanoinformatics 22 1.5 Model Prediction for Site-Specific Drug Delivery 23 1.5.1 Prediction of Mode and a Site-Specific Action 23 1.5.2 Precision Medicine 26 1.6 Future Scope and Challenges 27 1.7 Conclusion 29 References 30 2 Role of Deep Learning, Blockchain and Internet of Things in Patient Care 39 Akanksha Sharma, Rishabha Malviya and Sonali Sundram 2.1 Introduction 40 2.2 IoT and WBAN in Healthcare Systems 42 2.2.1 IoT in Healthcare 42 2.2.2 WBAN 44 2.2.2.1 Key Features of Medical Networks in the Wireless Body Area 44 2.2.2.2 Data Transmission & Storage Health 45 2.2.2.3 Privacy and Security Concerns in Big Data 45 2.3 Blockchain Technology in Healthcare 46 2.3.1 Importance of Blockchain 46 2.3.2 Role of Blockchain in Healthcare 47 2.3.3 Benefits of Blockchain in Healthcare Applications 48 2.3.4 Elements of Blockchain 49 2.3.5 Situation Awareness and Healthcare Decision Support with Combined Machine Learning and Semantic Modeling 51 2.3.6 Mobile Health and Remote Monitoring 53 2.3.7 Different Mobile Health Application with Description of Usage in Area of Application 54 2.3.8 Patient-Centered Blockchain Mode 55 2.3.9 Electronic Medical Record 57 2.3.9.1 The Most Significant Barriers to Adoption Are 60 2.3.9.2 Concern Regarding Negative Unintended Consequences of Technology 60 2.4 Deep Learning in Healthcare 62 2.4.1 Deep Learning Models 63 2.4.1.1 Recurrent Neural Networks (RNN) 63 2.4.1.2 Convolutional Neural Networks (CNN) 64 2.4.1.3 Deep Belief Network (DBN) 65 2.4.1.4 Contrasts Between Models 66 2.4.1.5 Use of Deep Learning in Healthcare 66 2.5 Conclusion 70 2.6 Acknowledgments 70 References 70 3 Deep Learning on Site-Specific Drug Delivery System 77 Prem Shankar Mishra, Rakhi Mishra and Rupa Mazumder 3.1 Introduction 78 3.2 Deep Learning 81 3.2.1 Types of Algorithms Used in Deep Learning 81 3.2.1.1 Convolutional Neural Networks (CNNs) 82 3.2.1.2 Long Short-Term Memory Networks (LSTMs) 83 3.2.1.3 Recurrent Neural Networks 83 3.2.1.4 Generative Adversarial Networks (GANs) 84 3.2.1.5 Radial Basis Function Networks 84 3.2.1.6 Multilayer Perceptron 85 3.2.1.7 Self-Organizing Maps 85 3.2.1.8 Deep Belief Networks 85 3.3 Machine Learning and Deep Learning Comparison 86 3.4 Applications of Deep Learning in Drug Delivery System 87 3.5 Conclusion 90 References 90 4 Deep Learning Advancements in Target Delivery 101 Sudhanshu Mishra, Palak Gupta, Smriti Ojha, Vijay Sharma, Vicky Anthony and Disha Sharma 4.1 Introduction: Deep Learning and Targeted Drug Delivery 102 4.2 Different Models/Approaches of Deep Learning and Targeting Drug 104 4.3 QSAR Model 105 4.3.1 Model of Deep Long-Term Short-Term Memory 105 4.3.2 RNN Model 107 4.3.3 CNN Model 108 4.4 Deep Learning Process Applications in Pharmaceutical 109 4.5 Techniques for Predicting Pharmacotherapy 109 4.6 Approach to Diagnosis 110 4.7 Application 113 4.7.1 Deep Learning in Drug Discovery 114 4.7.2 Medical Imaging and Deep Learning Process 115 4.7.3 Deep Learning in Diagnostic and Screening 116 4.7.4 Clinical Trials Using Deep Learning Models 116 4.7.5 Learning for Personalized Medicine 117 4.8 Conclusion 121 Acknowledgment 122 References 122 5 Deep Learning and Precision Medicine: Lessons to Learn for the Preeminent Treatment for Malignant Tumors 127 Selvasudha Nandakumar, Shah Alam Khan, Poovi Ganesan, Pushpa Sweety, Arul Prakash Francis, Mahendran Sekar, Rukkumani Rajagopalan and Dhanalekshmi Unnikrishnan Meenakshi 5.1 Introduction 128 5.2 Role of DL in Gene Identification, Unique Genomic Analysis, and Precise Cancer Diagnosis 132 5.2.1 Gene Identification and Genome Data 133 5.2.2 Image Diagnosis 135 5.2.3 Radiomics, Radiogenomics, and Digital Biopsy 137 5.2.4 Medical Image Analysis in Mammography 138 5.2.5 Magnetic Resonance Imaging 139 5.2.6 CT Imaging 140 5.3 dl in Next-Generation Sequencing, Biomarkers, and Clinical Validation 141 5.3.1 Next-Generation Sequencing 141 5.3.2 Biomarkers and Clinical Validation 142 5.4 dl and Translational Oncology 144 5.4.1 Prediction 144 5.4.2 Segmentation 146 5.4.3 Knowledge Graphs and Cancer Drug Repurposing 147 5.4.4 Automated Treatment Planning 149 5.4.5 Clinical Benefits 150 5.5 DL in Clinical Trials—A Necessary Paradigm Shift 152 5.6 Challenges and Limitations 155 5.7 Conclusion 157 References 157 6 Personalized Therapy Using Deep Learning Advances 171 Nishant Gaur, Rashmi Dharwadkar and Jinsu Thomas 6.1 Introduction 172 6.2 Deep Learning 174 6.2.1 Convolutional Neural Networks 175 6.2.2 Autoencoders 180 6.2.3 Deep Belief Network (DBN) 182 6.2.4 Deep Reinforcement Learning 184 6.2.5 Generative Adversarial Network 186 6.2.6 Long Short-Term Memory Networks 188 References 191 7 Tele-Health Monitoring Using Artificial Intelligence Deep Learning Framework 199 Swati Verma, Rishabha Malviya, Md Aftab Alam and Bhuneshwar Dutta Tripathi 7.1 Introduction 200 7.2 Artificial Intelligence 200 7.2.1 Types of Artificial Intelligence 201 7.2.1.1 Machine Intelligence 201 7.2.1.2 Types of Machine Intelligence 203 7.2.2 Applications of Artificial Intelligence 204 7.2.2.1 Role in Healthcare Diagnostics 205 7.2.2.2 AI in Telehealth 205 7.2.2.3 Role in Structural Health Monitoring 205 7.2.2.4 Role in Remote Medicare Management 206 7.2.2.5 Predictive Analysis Using Big Data 207 7.2.2.6 AI’s Role in Virtual Monitoring of Patients 208 7.2.2.7 Functions of Devices 208 7.2.2.8 Clinical Outcomes Through Remote Patient Monitoring 210 7.2.2.9 Clinical Decision Support 211 7.2.3 Utilization of Artificial Intelligence in Telemedicine 211 7.2.3.1 Artificial Intelligence–Assisted Telemedicine 212 7.2.3.2 Telehealth and New Care Models 213 7.2.3.3 Strategy of Telecare Domain 214 7.2.3.4 Role of AI-Assisted Telemedicine in Various Domains 216 7.3 AI-Enabled Telehealth: Social and Ethical Considerations 218 7.4 Conclusion 219 References 220 8 Deep Learning Framework for Cancer Diagnosis and Treatment 229 Shiv Bahadur and Prashant Kumar 8.1 Deep Learning: An Emerging Field for Cancer Management 230 8.2 Deep Learning Framework in Diagnosis and Treatment of Cancer 232 8.3 Applications of Deep Learning in Cancer Diagnosis 233 8.3.1 Medical Imaging Through Artificial Intelligence 234 8.3.2 Biomarkers Identification in the Diagnosis of Cancer Through Deep Learning 234 8.3.3 Digital Pathology Through Deep Learning 235 8.3.4 Application of Artificial Intelligence in Surgery 236 8.3.5 Histopathological Images Using Deep Learning 237 8.3.6 MRI and Ultrasound Images Through Deep Learning 237 8.4 Clinical Applications of Deep Learning in the Management of Cancer 238 8.5 Ethical Considerations in Deep Learning–Based Robotic Therapy 239 8.6 Conclusion 240 Acknowledgments 240 References 241 9 Applications of Deep Learning in Radiation Therapy 247 Akanksha Sharma, Ashish Verma, Rishabha Malviya and Shalini Yadav 9.1 Introduction 248 9.2 History of Radiotherapy 250 9.3 Principal of Radiotherapy 251 9.4 Deep Learning 251 9.5 Radiation Therapy Techniques 254 9.5.1 External Beam Radiation Therapy 257 9.5.2 Three-Dimensional Conformal Radiation Therapy (3D-CRT) 259 9.5.3 Intensity Modulated Radiation Therapy (IMRT) 260 9.5.4 Image-Guided Radiation Therapy (IGRT) 261 9.5.5 Intraoperative Radiation Therapy (IORT) 263 9.5.6 Brachytherapy 265 9.5.7 Stereotactic Radiosurgery (SRS) 268 9.6 Different Role of Deep Learning with Corresponding Role of Medical Physicist 269 9.6.1 Deep Learning in Patient Assessment 269 9.6.1.1 Radiotherapy Results Prediction 269 9.6.1.2 Respiratory Signal Prediction 271 9.6.2 Simulation Computed Tomography 271 9.6.3 Targets and Organs-at-Risk Segmentation 273 9.6.4 Treatment Planning 274 9.6.4.1 Beam Angle Optimization 274 9.6.4.2 Dose Prediction 276 9.6.5 Other Role of Deep Learning in Corresponds with Medical Physicists 277 9.7 Conclusion 280 References 281 10 Application of Deep Learning in Radiation Therapy 289 Shilpa Rawat, Shilpa Singh, Md. Aftab Alam and Rishabha Malviya 10.1 Introduction 290 10.2 Radiotherapy 291 10.3 Principle of Deep Learning and Machine Learning 293 10.3.1 Deep Neural Networks (DNN) 294 10.3.2 Convolutional Neural Network 295 10.4 Role of AI and Deep Learning in Radiation Therapy 295 10.5 Platforms for Deep Learning and Tools for Radiotherapy 297 10.6 Radiation Therapy Implementation in Deep Learning 300 10.6.1 Deep Learning and Imaging Techniques 301 10.6.2 Image Segmentation 301 10.6.3 Lesion Segmentation 302 10.6.4 Computer-Aided Diagnosis 302 10.6.5 Computer-Aided Detection 303 10.6.6 Quality Assurance 304 10.6.7 Treatment Planning 305 10.6.8 Treatment Delivery 305 10.6.9 Response to Treatment 306 10.7 Prediction of Outcomes 307 10.7.1 Toxicity 309 10.7.2 Survival and the Ability to Respond 310 10.8 Deep Learning in Conjunction With Radiomoic 312 10.9 Planning for Treatment 314 10.9.1 Optimization of Beam Angle 315 10.9.2 Prediction of Dose 315 10.10 Deep Learning’s Challenges and Future Potential 316 10.11 Conclusion 317 References 318 11 Deep Learning Framework for Cancer 333 Pratishtha 11.1 Introduction 334 11.2 Brief History of Deep Learning 335 11.3 Types of Deep Learning Methods 336 11.4 Applications of Deep Learning 339 11.4.1 Toxicity Detection for Different Chemical Structures 339 11.4.2 Mitosis Detection 340 11.4.3 Radiology or Medical Imaging 341 11.4.4 Hallucination 342 11.4.5 Next-Generation Sequencing (NGS) 342 11.4.6 Drug Discovery 343 11.4.7 Sequence or Video Generation 343 11.4.8 Other Applications 343 11.5 Cancer 343 11.5.1 Factors 344 11.5.1.1 Heredity 345 11.5.1.2 Ionizing Radiation 345 11.5.1.3 Chemical Substances 345 11.5.1.4 Dietary Factors 345 11.5.1.5 Estrogen 346 11.5.1.6 Viruses 346 11.5.1.7 Stress 347 11.5.1.8 Age 347 11.5.2 Signs and Symptoms of Cancer 347 11.5.3 Types of Cancer Treatment Available 348 11.5.3.1 Surgery 348 11.5.3.2 Radiation Therapy 348 11.5.3.3 Chemotherapy 348 11.5.3.4 Immunotherapy 348 11.5.3.5 Targeted Therapy 349 11.5.3.6 Hormone Therapy 349 11.5.3.7 Stem Cell Transplant 349 11.5.3.8 Precision Medicine 349 11.5.4 Types of Cancer 349 11.5.4.1 Carcinoma 349 11.5.4.2 Sarcoma 349 11.5.4.3 Leukemia 350 11.5.4.4 Lymphoma and Myeloma 350 11.5.4.5 Central Nervous System (CNS) Cancers 350 11.5.5 The Development of Cancer (Pathogenesis) Cancer 350 11.6 Role of Deep Learning in Various Types of Cancer 350 11.6.1 Skin Cancer 351 11.6.1.1 Common Symptoms of Melanoma 351 11.6.1.2 Types of Skin Cancer 352 11.6.1.3 Prevention 353 11.6.1.4 Treatment 353 11.6.2 Deep Learning in Skin Cancer 354 11.6.3 Pancreatic Cancer 354 11.6.3.1 Symptoms of Pancreatic Cancer 355 11.6.3.2 Causes or Risk Factors of Pancreatic Cancer 355 11.6.3.3 Treatments of Pancreatic Cancer 355 11.6.4 Deep Learning in Pancreatic Cancer 355 11.6.5 Tobacco-Driven Lung Cancer 357 11.6.5.1 Symptoms of Lung Cancer 357 11.6.5.2 Causes or Risk Factors of Lung Cancer 358 11.6.5.3 Treatments Available for Lung Cancer 358 11.6.5.4 Deep Learning in Lung Cancer 358 11.6.6 Breast Cancer 359 11.6.6.1 Symptoms of Breast Cancer 360 11.6.6.2 Causes or Risk Factors of Breast Cancer 360 11.6.6.3 Treatments Available for Breast Cancer 361 11.6.7 Deep Learning in Breast Cancer 361 11.6.8 Prostate Cancer 362 11.6.9 Deep Learning in Prostate Cancer 362 11.7 Future Aspects of Deep Learning in Cancer 363 11.8 Conclusion 363 References 363 12 Cardiovascular Disease Prediction Using Deep Neural Network for Older People 369 Nagarjuna Telagam, B.Venkata Kranti and Nikhil Chandra Devarasetti 12.1 Introduction 370 12.2 Proposed System Model 375 12.2.1 Decision Tree Algorithm 375 12.2.1.1 Confusion Matrix 376 12.3 Random Forest Algorithm 381 12.4 Variable Importance for Random Forests 383 12.5 The Proposed Method Using a Deep Learning Model 384 12.5.1 Prevention of Overfitting 386 12.5.2 Batch Normalization 386 12.5.3 Dropout Technique 386 12.6 Results and Discussions 386 12.6.1 Linear Regression 386 12.6.2 Decision Tree Classifier 388 12.6.3 Voting Classifier 389 12.6.4 Bagging Classifier 389 12.6.5 Naïve Bayes 390 12.6.6 Logistic Regression 390 12.6.7 Extra Trees Classifier 391 12.6.8 K-Nearest Neighbor [KNN] Algorithm 391 12.6.9 Adaboost Classifier 392 12.6.10 Light Gradient Boost Classifier 393 12.6.11 Gradient Boosting Classifier 393 12.6.12 Stochastic Gradient Descent Algorithm 393 12.6.13 Linear Support Vector Classifier 394 12.6.14 Support Vector Machines 394 12.6.15 Gaussian Process Classification 395 12.6.16 Random Forest Classifier 395 12.7 Evaluation Metrics 396 12.8 Conclusion 401 References 402 13 Machine Learning: The Capabilities and Efficiency of Computers in Life Sciences 407 Shalini Yadav, Saurav Yadav, Shobhit Prakash Srivastava, Saurabh Kumar Gupta and Sudhanshu Mishra 13.1 Introduction 408 13.2 Supervised Learning 410 13.2.1 Workflow of Supervised Learning 410 13.2.2 Decision Tree 410 13.2.3 Support Vector Machine (SVM) 411 13.2.4 Naive Bayes 413 13.3 Deep Learning: A New Era of Machine Learning 414 13.4 Deep Learning in Artificial Intelligence (AI) 416 13.5 Using ML to Enhance Preventive and Treatment Insights 417 13.6 Different Additional Emergent Machine Learning Uses 418 13.6.1 Education 418 13.6.2 Pharmaceuticals 419 13.6.3 Manufacturing 419 13.7 Machine Learning 419 13.7.1 Neuroscience Research Advancements 420 13.7.2 Finding Patterns in Astronomical Data 420 13.8 Ethical and Social Issues Raised ! ! ! 421 13.8.1 Reliability and Safety 421 13.8.2 Transparency and Accountability 421 13.8.3 Data Privacy and Security 421 13.8.4 Malicious Use of AI 422 13.8.5 Effects on Healthcare Professionals 422 13.9 Future of Machine Learning in Healthcare 422 13.9.1 A Better Patient Journey 422 13.9.2 New Ways to Deliver Care 424 13.10 Challenges and Hesitations 424 13.10.1 Not Overlord Assistant Intelligent 424 13.10.2 Issues with Unlabeled Data 425 13.11 Concluding Thoughts 425 Acknowledgments 426 References 426 Index 431

Autorenportrait

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 interest are neurodegeneration, clinical research, and artificial intelligence. She has more than 8 patents to her credit.

Back cover copy

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. 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. 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.