Deep Learning for Healthcare Decision Making:
Health care today is known to suffer from siloed and fragmented data, delayed clinical communications, and disparate workflow tools due to the lack of interoperability caused by vendor-locked health care systems, lack of trust among data holders, and security/privacy concerns regarding data sharing....
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Gistrup, Denmark
River Publishers
2023
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| Schriftenreihe: | River Publishers series in biomedical engineering
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| Schlagworte: | |
| Online-Zugang: | FHI01 https://public.ebookcentral.proquest.com/choice/PublicFullRecord.aspx?p=30172500 Volltext |
| Zusammenfassung: | Health care today is known to suffer from siloed and fragmented data, delayed clinical communications, and disparate workflow tools due to the lack of interoperability caused by vendor-locked health care systems, lack of trust among data holders, and security/privacy concerns regarding data sharing. The health information industry is ready for big leaps and bounds in terms of growth and advancement. This book is an attempt to unveil the hidden potential of the enormous amount of health information and technology. Throughout this book, we attempt to combine numerous compelling views, guidelines, and frameworks to enable personalized health care service options through the successful application of deep learning frameworks. The progress of the health-care sector will be incremental as it learns from associations between data over time through the application of suitable AI, deep net frameworks, and patterns. The major challenge health care is facing is the effective and accurate learning of unstructured clinical data through the application of precise algorithms. Incorrect input data leading to erroneous outputs with false positives is intolerable in healthcare as patients’ lives are at stake. This book is written with the intent to uncover the stakes and possibilities involved in realizing personalized health-care services through efficient and effective deep learning algorithms. The specific focus of this book will be on the application of deep learning in any area of health care, including clinical trials, telemedicine, health records management, etc |
| Beschreibung: | Description based upon print version of record |
| Beschreibung: | 1 Online-Ressource (xxxvii, 272 Seiten) Illustrationen, Diagramme |
| ISBN: | 9788770223881 8770223882 |
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| 245 | 1 | 0 | |a Deep Learning for Healthcare Decision Making |c Vishal Jain, Jyotir Moy Chatterjee, Ishaani Priyadarshini, Fadi Al-Turjman |
| 264 | 1 | |a Gistrup, Denmark |b River Publishers |c 2023 | |
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| 505 | 8 | |a Preface xvii Acknowledgment xxi List of Figures xxiii List of Tables xxvii List of Contributors xxix List of Abbreviations xxxiii 1 Amalgamation of Deep Learning in Healthcare Systems 1 1.1 Introduction to Deep Learning 2 1.2 Deep Learning in Healthcare 4 1.3 Artificial Intelligence in the Healthcare System 6 1.4 Machine Learning in Healthcare 6 1.5 Natural Language Processing (NLP) in Healthcare 8 1.6 Deep Learning Models 9 1.6.1 Interpretation of deep learning models in medical images 10 1.6.1.1 Convolutional neural networks (CNNs) 11 1.6.1.2 Recurrent neural networks (RNNs) 12 1.6.1.3 Restricted boltzmann machines (RBMs) and deep belief networks (DBNs) 13 1.6.1.4 Deep neural network (DNN) 13 1.6.1.5 Generative adversarial network (GAN) 13 1.7 Radiologic Applications using Deep Learning 13 1.7.1 Image classification 14 1.7.2 Object detection 14 1.7.3 Image segmentation and registration 15 1.7.4 Image generation 15 1.7.5 Image transformation 16 1.7.5.1 Without the use of a | |
| 505 | 8 | |a generative adversarial network, | |
| 505 | 8 | |a image to image translation is possible 16 1.7.5.2 GAN for image-to-image translation 16 1.8 Predictive Analysis using Deep Learning and Machine Learning 17 1.9 Clinical Trials using Deep Learning 18 1.10 Applications of Deep Learning in the Healthcare System 19 1.10.1 Drug discovery 19 1.10.2 Medical imaging 19 1.10.3 Insurance fraud 19 1.10.4 Alzheimer’s disease 19 1.10.5 Genome 20 1.10.6 Healthcare data analytics 20 1.10.7 Mental health chatbots 20 1.10.8 Personalized medical treatments 20 1.10.9 Prescription audit 20 1.10.10 Responding to patient queries 21 Conclusion 21 References 21 2 Deep Neural Network Architecture and Applications in Healthcare 25 2.1 Introduction 26 2.2 Deep Neural Network 27 2.3 Deep Learning Architectures Applied in the Healthcare Field 32 2.3.1 Alzheimer’s disease 32 2.3.2 Brain mris 33 2.3.3 Osteoarthritis 33 2.3.4 Breast cancer 33 2.3.5 Diabetic retinopathy 33 2.3.6 Forecasting type of medicine based on patient history 33 2.3.7 Forecasting | |
| 505 | 8 | |a diseases through patient’s clinical status 34 2.3.8 Forecasting suicide 34 2.3.9 Forecasting readmission of patients after the discharge 34 2.3.10 Forecasting disease from lab test 34 2.3.11 Forecasting the quality of sleep by awake time activities 34 2.4 Pneumonia Detection using Deep Learning from X-ray Images 35 2.4.1 Overview 35 2.4.2 Methodology 37 2.4.2.1 Visualizing the images 37 2.4.2.2 Resizing 38 2.4.3 Results 39 2.4.3.1 ROC curve 39 2.4.3.2 Confusion matrix 39 Conclusion 39 References 41 3 The State of the Art of using Artificial Intelligence for Disease Identification and Diagnosis in Healthcare 47 3.1 Introduction 48 3.2 A Review of the Literature on Machine Learning and Artificial Intelligence in Healthcare 50 3.2.1 Machine learning applications in healthcare 50 3.2.2 Applications of artificial intelligence in healthcare 53 3.3 How to Develop Machine Learning Methods for Healthcare 54 3.3.1 Healthcare problem selection 54 3.3.2 Dataset construction 58 3.3.3 Model | |
| 505 | 8 | |a development 59 3.3.4 Model performance evaluation 59 3.3.5 Clinical impact evaluation 59 3.4 Disease Prediction and Diagnosis Using Artificial Intelligence 60 3.4.1 Disease prediction and diagnosis using machine learning 60 3.4.2 Artificial intelligence technology for clinical diagnosis 62 3.5 Challenges of using Artificial Intelligence Algorithms in Healthcare 66 Conclusion 67 References 68 4 Segmentation of MRI Images of Gliomas using Convolutional Neural Networks 77 4.1 Introduction 78 4.2 Aim 80 4.3 Objectives 80 4.4 Methodology 81 4.5 Implementation 82 4.5.1 Initial image processing 82 4.5.2 Training phase 82 4.5.3 Testing phase 83 4.5.4 Classification algorithm 84 4.6 Results 84 4.6.1 Training results 84 4.6.2 Test results 87 4.6.3 Dice scores 89 4.6.4 Positive Predictive Value (PPV) 90 4.6.5 Sensitivity 90 Conclusion 91 Future Prospects 92 References 93 5 Automatic Liver Tumor Segmentation from Computed Tomography Images Based on 2D and 3D Deep Neural Networks 97 5.1 | |
| 505 | 8 | |a Introduction 98 5.2 Related Concepts 99 5.2.1 Segmentation 99 5.2.2 Computed tomography 100 5.2.3 3D convolution 101 5.2.4 Separable convolution 102 5.2.5 Depthwise spatio-temporal separate 103 5.2.6 U-net 104 5.2.7 Efficientnet 105 5.2.8 Loss function 105 5.2.9 Metrics 106 5.2.9.1 Overlapping metrics 106 5.3 Related Work 108 5.4 Methodology 110 5.4.1 Data normalization and compression 111 5.4.2 Batch sampling 111 5.4.3 Data augmentation 112 5.4.4 Neural network architecture 113 5.4.5 Efficientnet modifications 113 5.4.6 Liver postprocessing 114 5.4.7 Mask combination 115 5.4.8 Tumor postprocessing 115 5.4.9 Network training 115 5.4.10 Dataset 117 5.5 Experiments 117 5.5.1 Local experiments setup 118 5.5.1.1 2D models 118 5.5.1.2 3D models 120 5.5.2 Local evaluation 121 5.5.3 LiTS challenge evaluation 124 Conclusion 126 References 127 6 Advancements in Deep Learning Techniques for Analyzing Electronic Medical Records 133 6.1 Introduction 134 6.2 Overview of EHR 135 6.2.1 | |
| 505 | 8 | |a Characteristics of EHR 135 6.2.2 Categories of EHR data 136 6.2.3 Doctor’s notes 136 6.3 Machine Learning and Deep Learning in EHR 136 6.3.1 Multilayer Perceptron (MLP) network 138 6.3.2 Convolutional Neural Network (CNN) 139 6.3.3 Recurrent Neural Network (RNN) 140 6.3.4 Restricted boltzmann machine 141 6.3.5 Autoencoders 141 6.4 Deep Learning Analysis of EHR 142 6.4.1 Extraction of information 144 6.4.1.1 Concept extraction 145 6.4.1.2 Time event extraction 146 6.4.1.3 Correlation extraction 146 6.4.1.4 Acronym expansion 146 6.4.2 Ehr representation 147 6.4.3 Evaluation of representation 148 6.4.4 Diagnosis of disease 148 6.4.5 Evaluation metric 149 6.4.6 Risk identification and survival prediction 149 6.5 EHR Data Set 149 6.6 Research Gap 150 6.7 Suggestions for Improvement 151 6.7.1 Tuning activation function 151 6.7.2 Constraints 151 6.7.3 Qualitative clustering 152 6.7.4 MIMIC learning 152 Conclusion 152 References 153 7 Telemedicine-based Development of M-Health | |
| 505 | 8 | |a Informatics using AI 159 7.1 Introduction 159 7.1 Objectives of Chapter 161 7.2 Literature Review 161 7.3 Wireless Technologies in m-Health 166 7.3.1 Wireless medical sensor technologies 167 7.4 Signals for Biomonitoring 168 7.4.1 Wireless communication for biomonitoring 169 7.5 Telemedicine Application Server 169 7.5.1 Server protocols 170 7.5.2 Server graphical user interface 170 7.6 Interface Program 171 7.6.1 Patient interface 171 7.6.2 Doctor browser interface 172 7.7 Experiment Work 172 Conclusion 175 References 175 8 Health Informatics System using Machine Learning Techniques 179 8.1 Introduction 180 8.1.1 COVID-19 pandemic 180 8.1.2 Necessity of AI 182 8.1.3 Artificial intelligence vs Machine learning vs deep learning 185 8.1.4 Healthcare informatics systems and analytics 187 8.2 Concept of Blockchain 188 8.2.1 Network architecture of blockchain 191 8.3 Concept of Data Leak and How It Is Overcome using Blockchain 193 8.3.1 Data breach targets 194 8.3.2 Data breach threats 195 | |
| 505 | 8 | |a 8.3.3 Data breach consequences 195 8.4 Present Situation and Future Perspective 196 8.4.1 Drug traceability 196 8.4.2 Clinical trials 197 8.4.3 Management of patient data 198 8.5 Existing Challenges in the Future 201 8.5.1 Data security and privacy 202 8.5.2 Storage capacity management 202 8.5.3 Interoperability issues 203 8.5.4 Standardization challenges 203 8.5.5 Social challenges 203 Conclusion 203 References 205 9 Blockchain in Healthcare: A Systematic Review and Future Perspectives 211 9.1 Introduction 212 9.2 Healthcare Challenges vs Blockchain Opportunities 213 9.2.1 Interoperability 213 9.2.2 Tampering/data security 214 9.2.3 Insurance fraud 215 9.2.4 Drug stealing 216 9.3 Introduction to Blockchain 216 9.3.1 Structure of blocks and blockchain 216 9.3.2 Types of blockchain networks 218 9.3.3 Consensus algorithms 219 9.3.4 Smart contracts 219 9.4 Research Methodology 220 9.5 Literature Review 220 9.6 Discussion 224 9.6.1 RQ1: What type of blockchain network and blockchain | |
| 505 | 8 | |a platform has been used by researchers in their healthcare applications? 224 9.6.2 RQ2: What are the important consensus algorithms used in blockchain-based healthcare systems? 226 9.6.3 RQ3: What proportion of research works have built smart contracts in their healthcare systems and which programming languages have they used? 228 9.6.4 RQ4: What are the evaluation methods used by researchers for analyzing their models? 229 9.6.5 RQ5: What are the drawbacks of the existing blockchain systems related to healthcare and what are the future perspectives? 229 9.7 Challenges and Future Scope 231 9.7.1 Lack of standardization 232 9.7.2 Scalability 232 9.7.3 Latency 232 9.7.4 Privacy of outsourced data 232 Conclusion 233 References 233 10 Fusion of Machine Learning and Blockchain Techniques in IoT-based Smart Healthcare Systems 245 10.1 Introduction 246 10.2 Literature Review 247 10.3 Issues and Challenges While Establishing IoT In Healthcare 249 10.4 Involvement of Blockchain Technique in the | |
| 505 | 8 | |a Healthcare System 251 10.4.1 Securing and tracking health supplies 251 10.4.2 Storing health information 252 10.4.3 Remote patient monitoring 252 10.4.4 Disease outbreak and tracking 253 10.5 Indulgement of AI and ML in Healthcare System 253 10.5.1 Supervised learning 254 10.5.2 Unsupervised learning 255 10.5.3 Semi-supervised learning 255 10.5.4 Reinforcement learning 256 10.6 Working Process 256 10.7 Machine Learning Algorithms in the Healthcare Industry 257 10.7.1 Support vector machine 257 10.7.2 Logistic Regression (LR) 258 10.7.3 Decision Tree (DT) 259 10.7.4 Random Forest Tree (RFT) 259 10.7.5 Discriminant Analysis (DA) 260 10.7.6 K-Nearest neighbor 260 10.8 Solution via Blockchain and Artificial Intelligence 260 Conclusion 261 Acknowledgement 262 References 262 Index 267 About the Editors 271 | |
| 520 | 3 | |a Health care today is known to suffer from siloed and fragmented data, delayed clinical communications, and disparate workflow tools due to the lack of interoperability caused by vendor-locked health care systems, lack of trust among data holders, and security/privacy concerns regarding data sharing. The health information industry is ready for big leaps and bounds in terms of growth and advancement. This book is an attempt to unveil the hidden potential of the enormous amount of health information and technology. Throughout this book, we attempt to combine numerous compelling views, guidelines, and frameworks to enable personalized health care service options through the successful application of deep learning frameworks. The progress of the health-care sector will be incremental as it learns from associations between data over time through the application of suitable AI, deep net frameworks, and patterns. The major challenge health care is facing is the effective and accurate learning of unstructured clinical data through the application of precise algorithms. Incorrect input data leading to erroneous outputs with false positives is intolerable in healthcare as patients’ lives are at stake. This book is written with the intent to uncover the stakes and possibilities involved in realizing personalized health-care services through efficient and effective deep learning algorithms. The specific focus of this book will be on the application of deep learning in any area of health care, including clinical trials, telemedicine, health records management, etc | |
| 653 | 0 | |a Deep learning (Machine learning) | |
| 653 | 0 | |a Medical care / Decision making / Data processing | |
| 653 | 0 | |a Artificial intelligence / Medical applications | |
| 653 | 0 | |a Deep Learning | |
| 653 | 0 | |a Apprentissage profond | |
| 653 | 0 | |a Soins médicaux / Prise de décision / Informatique | |
| 653 | 0 | |a Intelligence artificielle en médecine | |
| 653 | 0 | |a Artificial intelligence / Medical applications | |
| 653 | 0 | |a Deep learning (Machine learning) | |
| 776 | 0 | 8 | |i Print version |a Jain, Vishal |t Deep Learning for Healthcare Decision Making |d Aalborg : River Publishers,c2023 |z 9788770223898 |
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| contents | Preface xvii Acknowledgment xxi List of Figures xxiii List of Tables xxvii List of Contributors xxix List of Abbreviations xxxiii 1 Amalgamation of Deep Learning in Healthcare Systems 1 1.1 Introduction to Deep Learning 2 1.2 Deep Learning in Healthcare 4 1.3 Artificial Intelligence in the Healthcare System 6 1.4 Machine Learning in Healthcare 6 1.5 Natural Language Processing (NLP) in Healthcare 8 1.6 Deep Learning Models 9 1.6.1 Interpretation of deep learning models in medical images 10 1.6.1.1 Convolutional neural networks (CNNs) 11 1.6.1.2 Recurrent neural networks (RNNs) 12 1.6.1.3 Restricted boltzmann machines (RBMs) and deep belief networks (DBNs) 13 1.6.1.4 Deep neural network (DNN) 13 1.6.1.5 Generative adversarial network (GAN) 13 1.7 Radiologic Applications using Deep Learning 13 1.7.1 Image classification 14 1.7.2 Object detection 14 1.7.3 Image segmentation and registration 15 1.7.4 Image generation 15 1.7.5 Image transformation 16 1.7.5.1 Without the use of a generative adversarial network, image to image translation is possible 16 1.7.5.2 GAN for image-to-image translation 16 1.8 Predictive Analysis using Deep Learning and Machine Learning 17 1.9 Clinical Trials using Deep Learning 18 1.10 Applications of Deep Learning in the Healthcare System 19 1.10.1 Drug discovery 19 1.10.2 Medical imaging 19 1.10.3 Insurance fraud 19 1.10.4 Alzheimer’s disease 19 1.10.5 Genome 20 1.10.6 Healthcare data analytics 20 1.10.7 Mental health chatbots 20 1.10.8 Personalized medical treatments 20 1.10.9 Prescription audit 20 1.10.10 Responding to patient queries 21 Conclusion 21 References 21 2 Deep Neural Network Architecture and Applications in Healthcare 25 2.1 Introduction 26 2.2 Deep Neural Network 27 2.3 Deep Learning Architectures Applied in the Healthcare Field 32 2.3.1 Alzheimer’s disease 32 2.3.2 Brain mris 33 2.3.3 Osteoarthritis 33 2.3.4 Breast cancer 33 2.3.5 Diabetic retinopathy 33 2.3.6 Forecasting type of medicine based on patient history 33 2.3.7 Forecasting diseases through patient’s clinical status 34 2.3.8 Forecasting suicide 34 2.3.9 Forecasting readmission of patients after the discharge 34 2.3.10 Forecasting disease from lab test 34 2.3.11 Forecasting the quality of sleep by awake time activities 34 2.4 Pneumonia Detection using Deep Learning from X-ray Images 35 2.4.1 Overview 35 2.4.2 Methodology 37 2.4.2.1 Visualizing the images 37 2.4.2.2 Resizing 38 2.4.3 Results 39 2.4.3.1 ROC curve 39 2.4.3.2 Confusion matrix 39 Conclusion 39 References 41 3 The State of the Art of using Artificial Intelligence for Disease Identification and Diagnosis in Healthcare 47 3.1 Introduction 48 3.2 A Review of the Literature on Machine Learning and Artificial Intelligence in Healthcare 50 3.2.1 Machine learning applications in healthcare 50 3.2.2 Applications of artificial intelligence in healthcare 53 3.3 How to Develop Machine Learning Methods for Healthcare 54 3.3.1 Healthcare problem selection 54 3.3.2 Dataset construction 58 3.3.3 Model development 59 3.3.4 Model performance evaluation 59 3.3.5 Clinical impact evaluation 59 3.4 Disease Prediction and Diagnosis Using Artificial Intelligence 60 3.4.1 Disease prediction and diagnosis using machine learning 60 3.4.2 Artificial intelligence technology for clinical diagnosis 62 3.5 Challenges of using Artificial Intelligence Algorithms in Healthcare 66 Conclusion 67 References 68 4 Segmentation of MRI Images of Gliomas using Convolutional Neural Networks 77 4.1 Introduction 78 4.2 Aim 80 4.3 Objectives 80 4.4 Methodology 81 4.5 Implementation 82 4.5.1 Initial image processing 82 4.5.2 Training phase 82 4.5.3 Testing phase 83 4.5.4 Classification algorithm 84 4.6 Results 84 4.6.1 Training results 84 4.6.2 Test results 87 4.6.3 Dice scores 89 4.6.4 Positive Predictive Value (PPV) 90 4.6.5 Sensitivity 90 Conclusion 91 Future Prospects 92 References 93 5 Automatic Liver Tumor Segmentation from Computed Tomography Images Based on 2D and 3D Deep Neural Networks 97 5.1 Introduction 98 5.2 Related Concepts 99 5.2.1 Segmentation 99 5.2.2 Computed tomography 100 5.2.3 3D convolution 101 5.2.4 Separable convolution 102 5.2.5 Depthwise spatio-temporal separate 103 5.2.6 U-net 104 5.2.7 Efficientnet 105 5.2.8 Loss function 105 5.2.9 Metrics 106 5.2.9.1 Overlapping metrics 106 5.3 Related Work 108 5.4 Methodology 110 5.4.1 Data normalization and compression 111 5.4.2 Batch sampling 111 5.4.3 Data augmentation 112 5.4.4 Neural network architecture 113 5.4.5 Efficientnet modifications 113 5.4.6 Liver postprocessing 114 5.4.7 Mask combination 115 5.4.8 Tumor postprocessing 115 5.4.9 Network training 115 5.4.10 Dataset 117 5.5 Experiments 117 5.5.1 Local experiments setup 118 5.5.1.1 2D models 118 5.5.1.2 3D models 120 5.5.2 Local evaluation 121 5.5.3 LiTS challenge evaluation 124 Conclusion 126 References 127 6 Advancements in Deep Learning Techniques for Analyzing Electronic Medical Records 133 6.1 Introduction 134 6.2 Overview of EHR 135 6.2.1 Characteristics of EHR 135 6.2.2 Categories of EHR data 136 6.2.3 Doctor’s notes 136 6.3 Machine Learning and Deep Learning in EHR 136 6.3.1 Multilayer Perceptron (MLP) network 138 6.3.2 Convolutional Neural Network (CNN) 139 6.3.3 Recurrent Neural Network (RNN) 140 6.3.4 Restricted boltzmann machine 141 6.3.5 Autoencoders 141 6.4 Deep Learning Analysis of EHR 142 6.4.1 Extraction of information 144 6.4.1.1 Concept extraction 145 6.4.1.2 Time event extraction 146 6.4.1.3 Correlation extraction 146 6.4.1.4 Acronym expansion 146 6.4.2 Ehr representation 147 6.4.3 Evaluation of representation 148 6.4.4 Diagnosis of disease 148 6.4.5 Evaluation metric 149 6.4.6 Risk identification and survival prediction 149 6.5 EHR Data Set 149 6.6 Research Gap 150 6.7 Suggestions for Improvement 151 6.7.1 Tuning activation function 151 6.7.2 Constraints 151 6.7.3 Qualitative clustering 152 6.7.4 MIMIC learning 152 Conclusion 152 References 153 7 Telemedicine-based Development of M-Health Informatics using AI 159 7.1 Introduction 159 7.1 Objectives of Chapter 161 7.2 Literature Review 161 7.3 Wireless Technologies in m-Health 166 7.3.1 Wireless medical sensor technologies 167 7.4 Signals for Biomonitoring 168 7.4.1 Wireless communication for biomonitoring 169 7.5 Telemedicine Application Server 169 7.5.1 Server protocols 170 7.5.2 Server graphical user interface 170 7.6 Interface Program 171 7.6.1 Patient interface 171 7.6.2 Doctor browser interface 172 7.7 Experiment Work 172 Conclusion 175 References 175 8 Health Informatics System using Machine Learning Techniques 179 8.1 Introduction 180 8.1.1 COVID-19 pandemic 180 8.1.2 Necessity of AI 182 8.1.3 Artificial intelligence vs Machine learning vs deep learning 185 8.1.4 Healthcare informatics systems and analytics 187 8.2 Concept of Blockchain 188 8.2.1 Network architecture of blockchain 191 8.3 Concept of Data Leak and How It Is Overcome using Blockchain 193 8.3.1 Data breach targets 194 8.3.2 Data breach threats 195 8.3.3 Data breach consequences 195 8.4 Present Situation and Future Perspective 196 8.4.1 Drug traceability 196 8.4.2 Clinical trials 197 8.4.3 Management of patient data 198 8.5 Existing Challenges in the Future 201 8.5.1 Data security and privacy 202 8.5.2 Storage capacity management 202 8.5.3 Interoperability issues 203 8.5.4 Standardization challenges 203 8.5.5 Social challenges 203 Conclusion 203 References 205 9 Blockchain in Healthcare: A Systematic Review and Future Perspectives 211 9.1 Introduction 212 9.2 Healthcare Challenges vs Blockchain Opportunities 213 9.2.1 Interoperability 213 9.2.2 Tampering/data security 214 9.2.3 Insurance fraud 215 9.2.4 Drug stealing 216 9.3 Introduction to Blockchain 216 9.3.1 Structure of blocks and blockchain 216 9.3.2 Types of blockchain networks 218 9.3.3 Consensus algorithms 219 9.3.4 Smart contracts 219 9.4 Research Methodology 220 9.5 Literature Review 220 9.6 Discussion 224 9.6.1 RQ1: What type of blockchain network and blockchain platform has been used by researchers in their healthcare applications? 224 9.6.2 RQ2: What are the important consensus algorithms used in blockchain-based healthcare systems? 226 9.6.3 RQ3: What proportion of research works have built smart contracts in their healthcare systems and which programming languages have they used? 228 9.6.4 RQ4: What are the evaluation methods used by researchers for analyzing their models? 229 9.6.5 RQ5: What are the drawbacks of the existing blockchain systems related to healthcare and what are the future perspectives? 229 9.7 Challenges and Future Scope 231 9.7.1 Lack of standardization 232 9.7.2 Scalability 232 9.7.3 Latency 232 9.7.4 Privacy of outsourced data 232 Conclusion 233 References 233 10 Fusion of Machine Learning and Blockchain Techniques in IoT-based Smart Healthcare Systems 245 10.1 Introduction 246 10.2 Literature Review 247 10.3 Issues and Challenges While Establishing IoT In Healthcare 249 10.4 Involvement of Blockchain Technique in the Healthcare System 251 10.4.1 Securing and tracking health supplies 251 10.4.2 Storing health information 252 10.4.3 Remote patient monitoring 252 10.4.4 Disease outbreak and tracking 253 10.5 Indulgement of AI and ML in Healthcare System 253 10.5.1 Supervised learning 254 10.5.2 Unsupervised learning 255 10.5.3 Semi-supervised learning 255 10.5.4 Reinforcement learning 256 10.6 Working Process 256 10.7 Machine Learning Algorithms in the Healthcare Industry 257 10.7.1 Support vector machine 257 10.7.2 Logistic Regression (LR) 258 10.7.3 Decision Tree (DT) 259 10.7.4 Random Forest Tree (RFT) 259 10.7.5 Discriminant Analysis (DA) 260 10.7.6 K-Nearest neighbor 260 10.8 Solution via Blockchain and Artificial Intelligence 260 Conclusion 261 Acknowledgement 262 References 262 Index 267 About the Editors 271 |
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| format | Electronic eBook |
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Seiten)</subfield><subfield code="b">Illustrationen, Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">River Publishers series in biomedical engineering</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based upon print version of record</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Preface xvii Acknowledgment xxi List of Figures xxiii List of Tables xxvii List of Contributors xxix List of Abbreviations xxxiii 1 Amalgamation of Deep Learning in Healthcare Systems 1 1.1 Introduction to Deep Learning 2 1.2 Deep Learning in Healthcare 4 1.3 Artificial Intelligence in the Healthcare System 6 1.4 Machine Learning in Healthcare 6 1.5 Natural Language Processing (NLP) in Healthcare 8 1.6 Deep Learning Models 9 1.6.1 Interpretation of deep learning models in medical images 10 1.6.1.1 Convolutional neural networks (CNNs) 11 1.6.1.2 Recurrent neural networks (RNNs) 12 1.6.1.3 Restricted boltzmann machines (RBMs) and deep belief networks (DBNs) 13 1.6.1.4 Deep neural network (DNN) 13 1.6.1.5 Generative adversarial network (GAN) 13 1.7 Radiologic Applications using Deep Learning 13 1.7.1 Image classification 14 1.7.2 Object detection 14 1.7.3 Image segmentation and registration 15 1.7.4 Image generation 15 1.7.5 Image transformation 16 1.7.5.1 Without the use of a </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">generative adversarial network, </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">image to image translation is possible 16 1.7.5.2 GAN for image-to-image translation 16 1.8 Predictive Analysis using Deep Learning and Machine Learning 17 1.9 Clinical Trials using Deep Learning 18 1.10 Applications of Deep Learning in the Healthcare System 19 1.10.1 Drug discovery 19 1.10.2 Medical imaging 19 1.10.3 Insurance fraud 19 1.10.4 Alzheimer’s disease 19 1.10.5 Genome 20 1.10.6 Healthcare data analytics 20 1.10.7 Mental health chatbots 20 1.10.8 Personalized medical treatments 20 1.10.9 Prescription audit 20 1.10.10 Responding to patient queries 21 Conclusion 21 References 21 2 Deep Neural Network Architecture and Applications in Healthcare 25 2.1 Introduction 26 2.2 Deep Neural Network 27 2.3 Deep Learning Architectures Applied in the Healthcare Field 32 2.3.1 Alzheimer’s disease 32 2.3.2 Brain mris 33 2.3.3 Osteoarthritis 33 2.3.4 Breast cancer 33 2.3.5 Diabetic retinopathy 33 2.3.6 Forecasting type of medicine based on patient history 33 2.3.7 Forecasting </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">diseases through patient’s clinical status 34 2.3.8 Forecasting suicide 34 2.3.9 Forecasting readmission of patients after the discharge 34 2.3.10 Forecasting disease from lab test 34 2.3.11 Forecasting the quality of sleep by awake time activities 34 2.4 Pneumonia Detection using Deep Learning from X-ray Images 35 2.4.1 Overview 35 2.4.2 Methodology 37 2.4.2.1 Visualizing the images 37 2.4.2.2 Resizing 38 2.4.3 Results 39 2.4.3.1 ROC curve 39 2.4.3.2 Confusion matrix 39 Conclusion 39 References 41 3 The State of the Art of using Artificial Intelligence for Disease Identification and Diagnosis in Healthcare 47 3.1 Introduction 48 3.2 A Review of the Literature on Machine Learning and Artificial Intelligence in Healthcare 50 3.2.1 Machine learning applications in healthcare 50 3.2.2 Applications of artificial intelligence in healthcare 53 3.3 How to Develop Machine Learning Methods for Healthcare 54 3.3.1 Healthcare problem selection 54 3.3.2 Dataset construction 58 3.3.3 Model </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">development 59 3.3.4 Model performance evaluation 59 3.3.5 Clinical impact evaluation 59 3.4 Disease Prediction and Diagnosis Using Artificial Intelligence 60 3.4.1 Disease prediction and diagnosis using machine learning 60 3.4.2 Artificial intelligence technology for clinical diagnosis 62 3.5 Challenges of using Artificial Intelligence Algorithms in Healthcare 66 Conclusion 67 References 68 4 Segmentation of MRI Images of Gliomas using Convolutional Neural Networks 77 4.1 Introduction 78 4.2 Aim 80 4.3 Objectives 80 4.4 Methodology 81 4.5 Implementation 82 4.5.1 Initial image processing 82 4.5.2 Training phase 82 4.5.3 Testing phase 83 4.5.4 Classification algorithm 84 4.6 Results 84 4.6.1 Training results 84 4.6.2 Test results 87 4.6.3 Dice scores 89 4.6.4 Positive Predictive Value (PPV) 90 4.6.5 Sensitivity 90 Conclusion 91 Future Prospects 92 References 93 5 Automatic Liver Tumor Segmentation from Computed Tomography Images Based on 2D and 3D Deep Neural Networks 97 5.1 </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Introduction 98 5.2 Related Concepts 99 5.2.1 Segmentation 99 5.2.2 Computed tomography 100 5.2.3 3D convolution 101 5.2.4 Separable convolution 102 5.2.5 Depthwise spatio-temporal separate 103 5.2.6 U-net 104 5.2.7 Efficientnet 105 5.2.8 Loss function 105 5.2.9 Metrics 106 5.2.9.1 Overlapping metrics 106 5.3 Related Work 108 5.4 Methodology 110 5.4.1 Data normalization and compression 111 5.4.2 Batch sampling 111 5.4.3 Data augmentation 112 5.4.4 Neural network architecture 113 5.4.5 Efficientnet modifications 113 5.4.6 Liver postprocessing 114 5.4.7 Mask combination 115 5.4.8 Tumor postprocessing 115 5.4.9 Network training 115 5.4.10 Dataset 117 5.5 Experiments 117 5.5.1 Local experiments setup 118 5.5.1.1 2D models 118 5.5.1.2 3D models 120 5.5.2 Local evaluation 121 5.5.3 LiTS challenge evaluation 124 Conclusion 126 References 127 6 Advancements in Deep Learning Techniques for Analyzing Electronic Medical Records 133 6.1 Introduction 134 6.2 Overview of EHR 135 6.2.1 </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Characteristics of EHR 135 6.2.2 Categories of EHR data 136 6.2.3 Doctor’s notes 136 6.3 Machine Learning and Deep Learning in EHR 136 6.3.1 Multilayer Perceptron (MLP) network 138 6.3.2 Convolutional Neural Network (CNN) 139 6.3.3 Recurrent Neural Network (RNN) 140 6.3.4 Restricted boltzmann machine 141 6.3.5 Autoencoders 141 6.4 Deep Learning Analysis of EHR 142 6.4.1 Extraction of information 144 6.4.1.1 Concept extraction 145 6.4.1.2 Time event extraction 146 6.4.1.3 Correlation extraction 146 6.4.1.4 Acronym expansion 146 6.4.2 Ehr representation 147 6.4.3 Evaluation of representation 148 6.4.4 Diagnosis of disease 148 6.4.5 Evaluation metric 149 6.4.6 Risk identification and survival prediction 149 6.5 EHR Data Set 149 6.6 Research Gap 150 6.7 Suggestions for Improvement 151 6.7.1 Tuning activation function 151 6.7.2 Constraints 151 6.7.3 Qualitative clustering 152 6.7.4 MIMIC learning 152 Conclusion 152 References 153 7 Telemedicine-based Development of M-Health </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Informatics using AI 159 7.1 Introduction 159 7.1 Objectives of Chapter 161 7.2 Literature Review 161 7.3 Wireless Technologies in m-Health 166 7.3.1 Wireless medical sensor technologies 167 7.4 Signals for Biomonitoring 168 7.4.1 Wireless communication for biomonitoring 169 7.5 Telemedicine Application Server 169 7.5.1 Server protocols 170 7.5.2 Server graphical user interface 170 7.6 Interface Program 171 7.6.1 Patient interface 171 7.6.2 Doctor browser interface 172 7.7 Experiment Work 172 Conclusion 175 References 175 8 Health Informatics System using Machine Learning Techniques 179 8.1 Introduction 180 8.1.1 COVID-19 pandemic 180 8.1.2 Necessity of AI 182 8.1.3 Artificial intelligence vs Machine learning vs deep learning 185 8.1.4 Healthcare informatics systems and analytics 187 8.2 Concept of Blockchain 188 8.2.1 Network architecture of blockchain 191 8.3 Concept of Data Leak and How It Is Overcome using Blockchain 193 8.3.1 Data breach targets 194 8.3.2 Data breach threats 195 </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">8.3.3 Data breach consequences 195 8.4 Present Situation and Future Perspective 196 8.4.1 Drug traceability 196 8.4.2 Clinical trials 197 8.4.3 Management of patient data 198 8.5 Existing Challenges in the Future 201 8.5.1 Data security and privacy 202 8.5.2 Storage capacity management 202 8.5.3 Interoperability issues 203 8.5.4 Standardization challenges 203 8.5.5 Social challenges 203 Conclusion 203 References 205 9 Blockchain in Healthcare: A Systematic Review and Future Perspectives 211 9.1 Introduction 212 9.2 Healthcare Challenges vs Blockchain Opportunities 213 9.2.1 Interoperability 213 9.2.2 Tampering/data security 214 9.2.3 Insurance fraud 215 9.2.4 Drug stealing 216 9.3 Introduction to Blockchain 216 9.3.1 Structure of blocks and blockchain 216 9.3.2 Types of blockchain networks 218 9.3.3 Consensus algorithms 219 9.3.4 Smart contracts 219 9.4 Research Methodology 220 9.5 Literature Review 220 9.6 Discussion 224 9.6.1 RQ1: What type of blockchain network and blockchain </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">platform has been used by researchers in their healthcare applications? 224 9.6.2 RQ2: What are the important consensus algorithms used in blockchain-based healthcare systems? 226 9.6.3 RQ3: What proportion of research works have built smart contracts in their healthcare systems and which programming languages have they used? 228 9.6.4 RQ4: What are the evaluation methods used by researchers for analyzing their models? 229 9.6.5 RQ5: What are the drawbacks of the existing blockchain systems related to healthcare and what are the future perspectives? 229 9.7 Challenges and Future Scope 231 9.7.1 Lack of standardization 232 9.7.2 Scalability 232 9.7.3 Latency 232 9.7.4 Privacy of outsourced data 232 Conclusion 233 References 233 10 Fusion of Machine Learning and Blockchain Techniques in IoT-based Smart Healthcare Systems 245 10.1 Introduction 246 10.2 Literature Review 247 10.3 Issues and Challenges While Establishing IoT In Healthcare 249 10.4 Involvement of Blockchain Technique in the </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Healthcare System 251 10.4.1 Securing and tracking health supplies 251 10.4.2 Storing health information 252 10.4.3 Remote patient monitoring 252 10.4.4 Disease outbreak and tracking 253 10.5 Indulgement of AI and ML in Healthcare System 253 10.5.1 Supervised learning 254 10.5.2 Unsupervised learning 255 10.5.3 Semi-supervised learning 255 10.5.4 Reinforcement learning 256 10.6 Working Process 256 10.7 Machine Learning Algorithms in the Healthcare Industry 257 10.7.1 Support vector machine 257 10.7.2 Logistic Regression (LR) 258 10.7.3 Decision Tree (DT) 259 10.7.4 Random Forest Tree (RFT) 259 10.7.5 Discriminant Analysis (DA) 260 10.7.6 K-Nearest neighbor 260 10.8 Solution via Blockchain and Artificial Intelligence 260 Conclusion 261 Acknowledgement 262 References 262 Index 267 About the Editors 271</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Health care today is known to suffer from siloed and fragmented data, delayed clinical communications, and disparate workflow tools due to the lack of interoperability caused by vendor-locked health care systems, lack of trust among data holders, and security/privacy concerns regarding data sharing. 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This book is written with the intent to uncover the stakes and possibilities involved in realizing personalized health-care services through efficient and effective deep learning algorithms. 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| id | DE-604.BV049466605 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T23:15:55Z |
| indexdate | 2024-07-10T10:08:03Z |
| institution | BVB |
| isbn | 9788770223881 8770223882 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034812264 |
| oclc_num | 1414544386 |
| open_access_boolean | |
| owner | DE-573 |
| owner_facet | DE-573 |
| physical | 1 Online-Ressource (xxxvii, 272 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-37-RPEB |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | River Publishers |
| record_format | marc |
| series2 | River Publishers series in biomedical engineering |
| spelling | Jain, Vishal 1983- Verfasser (DE-588)1222050188 aut Deep Learning for Healthcare Decision Making Vishal Jain, Jyotir Moy Chatterjee, Ishaani Priyadarshini, Fadi Al-Turjman Gistrup, Denmark River Publishers 2023 1 Online-Ressource (xxxvii, 272 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier River Publishers series in biomedical engineering Description based upon print version of record Preface xvii Acknowledgment xxi List of Figures xxiii List of Tables xxvii List of Contributors xxix List of Abbreviations xxxiii 1 Amalgamation of Deep Learning in Healthcare Systems 1 1.1 Introduction to Deep Learning 2 1.2 Deep Learning in Healthcare 4 1.3 Artificial Intelligence in the Healthcare System 6 1.4 Machine Learning in Healthcare 6 1.5 Natural Language Processing (NLP) in Healthcare 8 1.6 Deep Learning Models 9 1.6.1 Interpretation of deep learning models in medical images 10 1.6.1.1 Convolutional neural networks (CNNs) 11 1.6.1.2 Recurrent neural networks (RNNs) 12 1.6.1.3 Restricted boltzmann machines (RBMs) and deep belief networks (DBNs) 13 1.6.1.4 Deep neural network (DNN) 13 1.6.1.5 Generative adversarial network (GAN) 13 1.7 Radiologic Applications using Deep Learning 13 1.7.1 Image classification 14 1.7.2 Object detection 14 1.7.3 Image segmentation and registration 15 1.7.4 Image generation 15 1.7.5 Image transformation 16 1.7.5.1 Without the use of a generative adversarial network, image to image translation is possible 16 1.7.5.2 GAN for image-to-image translation 16 1.8 Predictive Analysis using Deep Learning and Machine Learning 17 1.9 Clinical Trials using Deep Learning 18 1.10 Applications of Deep Learning in the Healthcare System 19 1.10.1 Drug discovery 19 1.10.2 Medical imaging 19 1.10.3 Insurance fraud 19 1.10.4 Alzheimer’s disease 19 1.10.5 Genome 20 1.10.6 Healthcare data analytics 20 1.10.7 Mental health chatbots 20 1.10.8 Personalized medical treatments 20 1.10.9 Prescription audit 20 1.10.10 Responding to patient queries 21 Conclusion 21 References 21 2 Deep Neural Network Architecture and Applications in Healthcare 25 2.1 Introduction 26 2.2 Deep Neural Network 27 2.3 Deep Learning Architectures Applied in the Healthcare Field 32 2.3.1 Alzheimer’s disease 32 2.3.2 Brain mris 33 2.3.3 Osteoarthritis 33 2.3.4 Breast cancer 33 2.3.5 Diabetic retinopathy 33 2.3.6 Forecasting type of medicine based on patient history 33 2.3.7 Forecasting diseases through patient’s clinical status 34 2.3.8 Forecasting suicide 34 2.3.9 Forecasting readmission of patients after the discharge 34 2.3.10 Forecasting disease from lab test 34 2.3.11 Forecasting the quality of sleep by awake time activities 34 2.4 Pneumonia Detection using Deep Learning from X-ray Images 35 2.4.1 Overview 35 2.4.2 Methodology 37 2.4.2.1 Visualizing the images 37 2.4.2.2 Resizing 38 2.4.3 Results 39 2.4.3.1 ROC curve 39 2.4.3.2 Confusion matrix 39 Conclusion 39 References 41 3 The State of the Art of using Artificial Intelligence for Disease Identification and Diagnosis in Healthcare 47 3.1 Introduction 48 3.2 A Review of the Literature on Machine Learning and Artificial Intelligence in Healthcare 50 3.2.1 Machine learning applications in healthcare 50 3.2.2 Applications of artificial intelligence in healthcare 53 3.3 How to Develop Machine Learning Methods for Healthcare 54 3.3.1 Healthcare problem selection 54 3.3.2 Dataset construction 58 3.3.3 Model development 59 3.3.4 Model performance evaluation 59 3.3.5 Clinical impact evaluation 59 3.4 Disease Prediction and Diagnosis Using Artificial Intelligence 60 3.4.1 Disease prediction and diagnosis using machine learning 60 3.4.2 Artificial intelligence technology for clinical diagnosis 62 3.5 Challenges of using Artificial Intelligence Algorithms in Healthcare 66 Conclusion 67 References 68 4 Segmentation of MRI Images of Gliomas using Convolutional Neural Networks 77 4.1 Introduction 78 4.2 Aim 80 4.3 Objectives 80 4.4 Methodology 81 4.5 Implementation 82 4.5.1 Initial image processing 82 4.5.2 Training phase 82 4.5.3 Testing phase 83 4.5.4 Classification algorithm 84 4.6 Results 84 4.6.1 Training results 84 4.6.2 Test results 87 4.6.3 Dice scores 89 4.6.4 Positive Predictive Value (PPV) 90 4.6.5 Sensitivity 90 Conclusion 91 Future Prospects 92 References 93 5 Automatic Liver Tumor Segmentation from Computed Tomography Images Based on 2D and 3D Deep Neural Networks 97 5.1 Introduction 98 5.2 Related Concepts 99 5.2.1 Segmentation 99 5.2.2 Computed tomography 100 5.2.3 3D convolution 101 5.2.4 Separable convolution 102 5.2.5 Depthwise spatio-temporal separate 103 5.2.6 U-net 104 5.2.7 Efficientnet 105 5.2.8 Loss function 105 5.2.9 Metrics 106 5.2.9.1 Overlapping metrics 106 5.3 Related Work 108 5.4 Methodology 110 5.4.1 Data normalization and compression 111 5.4.2 Batch sampling 111 5.4.3 Data augmentation 112 5.4.4 Neural network architecture 113 5.4.5 Efficientnet modifications 113 5.4.6 Liver postprocessing 114 5.4.7 Mask combination 115 5.4.8 Tumor postprocessing 115 5.4.9 Network training 115 5.4.10 Dataset 117 5.5 Experiments 117 5.5.1 Local experiments setup 118 5.5.1.1 2D models 118 5.5.1.2 3D models 120 5.5.2 Local evaluation 121 5.5.3 LiTS challenge evaluation 124 Conclusion 126 References 127 6 Advancements in Deep Learning Techniques for Analyzing Electronic Medical Records 133 6.1 Introduction 134 6.2 Overview of EHR 135 6.2.1 Characteristics of EHR 135 6.2.2 Categories of EHR data 136 6.2.3 Doctor’s notes 136 6.3 Machine Learning and Deep Learning in EHR 136 6.3.1 Multilayer Perceptron (MLP) network 138 6.3.2 Convolutional Neural Network (CNN) 139 6.3.3 Recurrent Neural Network (RNN) 140 6.3.4 Restricted boltzmann machine 141 6.3.5 Autoencoders 141 6.4 Deep Learning Analysis of EHR 142 6.4.1 Extraction of information 144 6.4.1.1 Concept extraction 145 6.4.1.2 Time event extraction 146 6.4.1.3 Correlation extraction 146 6.4.1.4 Acronym expansion 146 6.4.2 Ehr representation 147 6.4.3 Evaluation of representation 148 6.4.4 Diagnosis of disease 148 6.4.5 Evaluation metric 149 6.4.6 Risk identification and survival prediction 149 6.5 EHR Data Set 149 6.6 Research Gap 150 6.7 Suggestions for Improvement 151 6.7.1 Tuning activation function 151 6.7.2 Constraints 151 6.7.3 Qualitative clustering 152 6.7.4 MIMIC learning 152 Conclusion 152 References 153 7 Telemedicine-based Development of M-Health Informatics using AI 159 7.1 Introduction 159 7.1 Objectives of Chapter 161 7.2 Literature Review 161 7.3 Wireless Technologies in m-Health 166 7.3.1 Wireless medical sensor technologies 167 7.4 Signals for Biomonitoring 168 7.4.1 Wireless communication for biomonitoring 169 7.5 Telemedicine Application Server 169 7.5.1 Server protocols 170 7.5.2 Server graphical user interface 170 7.6 Interface Program 171 7.6.1 Patient interface 171 7.6.2 Doctor browser interface 172 7.7 Experiment Work 172 Conclusion 175 References 175 8 Health Informatics System using Machine Learning Techniques 179 8.1 Introduction 180 8.1.1 COVID-19 pandemic 180 8.1.2 Necessity of AI 182 8.1.3 Artificial intelligence vs Machine learning vs deep learning 185 8.1.4 Healthcare informatics systems and analytics 187 8.2 Concept of Blockchain 188 8.2.1 Network architecture of blockchain 191 8.3 Concept of Data Leak and How It Is Overcome using Blockchain 193 8.3.1 Data breach targets 194 8.3.2 Data breach threats 195 8.3.3 Data breach consequences 195 8.4 Present Situation and Future Perspective 196 8.4.1 Drug traceability 196 8.4.2 Clinical trials 197 8.4.3 Management of patient data 198 8.5 Existing Challenges in the Future 201 8.5.1 Data security and privacy 202 8.5.2 Storage capacity management 202 8.5.3 Interoperability issues 203 8.5.4 Standardization challenges 203 8.5.5 Social challenges 203 Conclusion 203 References 205 9 Blockchain in Healthcare: A Systematic Review and Future Perspectives 211 9.1 Introduction 212 9.2 Healthcare Challenges vs Blockchain Opportunities 213 9.2.1 Interoperability 213 9.2.2 Tampering/data security 214 9.2.3 Insurance fraud 215 9.2.4 Drug stealing 216 9.3 Introduction to Blockchain 216 9.3.1 Structure of blocks and blockchain 216 9.3.2 Types of blockchain networks 218 9.3.3 Consensus algorithms 219 9.3.4 Smart contracts 219 9.4 Research Methodology 220 9.5 Literature Review 220 9.6 Discussion 224 9.6.1 RQ1: What type of blockchain network and blockchain platform has been used by researchers in their healthcare applications? 224 9.6.2 RQ2: What are the important consensus algorithms used in blockchain-based healthcare systems? 226 9.6.3 RQ3: What proportion of research works have built smart contracts in their healthcare systems and which programming languages have they used? 228 9.6.4 RQ4: What are the evaluation methods used by researchers for analyzing their models? 229 9.6.5 RQ5: What are the drawbacks of the existing blockchain systems related to healthcare and what are the future perspectives? 229 9.7 Challenges and Future Scope 231 9.7.1 Lack of standardization 232 9.7.2 Scalability 232 9.7.3 Latency 232 9.7.4 Privacy of outsourced data 232 Conclusion 233 References 233 10 Fusion of Machine Learning and Blockchain Techniques in IoT-based Smart Healthcare Systems 245 10.1 Introduction 246 10.2 Literature Review 247 10.3 Issues and Challenges While Establishing IoT In Healthcare 249 10.4 Involvement of Blockchain Technique in the Healthcare System 251 10.4.1 Securing and tracking health supplies 251 10.4.2 Storing health information 252 10.4.3 Remote patient monitoring 252 10.4.4 Disease outbreak and tracking 253 10.5 Indulgement of AI and ML in Healthcare System 253 10.5.1 Supervised learning 254 10.5.2 Unsupervised learning 255 10.5.3 Semi-supervised learning 255 10.5.4 Reinforcement learning 256 10.6 Working Process 256 10.7 Machine Learning Algorithms in the Healthcare Industry 257 10.7.1 Support vector machine 257 10.7.2 Logistic Regression (LR) 258 10.7.3 Decision Tree (DT) 259 10.7.4 Random Forest Tree (RFT) 259 10.7.5 Discriminant Analysis (DA) 260 10.7.6 K-Nearest neighbor 260 10.8 Solution via Blockchain and Artificial Intelligence 260 Conclusion 261 Acknowledgement 262 References 262 Index 267 About the Editors 271 Health care today is known to suffer from siloed and fragmented data, delayed clinical communications, and disparate workflow tools due to the lack of interoperability caused by vendor-locked health care systems, lack of trust among data holders, and security/privacy concerns regarding data sharing. The health information industry is ready for big leaps and bounds in terms of growth and advancement. This book is an attempt to unveil the hidden potential of the enormous amount of health information and technology. Throughout this book, we attempt to combine numerous compelling views, guidelines, and frameworks to enable personalized health care service options through the successful application of deep learning frameworks. The progress of the health-care sector will be incremental as it learns from associations between data over time through the application of suitable AI, deep net frameworks, and patterns. The major challenge health care is facing is the effective and accurate learning of unstructured clinical data through the application of precise algorithms. Incorrect input data leading to erroneous outputs with false positives is intolerable in healthcare as patients’ lives are at stake. This book is written with the intent to uncover the stakes and possibilities involved in realizing personalized health-care services through efficient and effective deep learning algorithms. The specific focus of this book will be on the application of deep learning in any area of health care, including clinical trials, telemedicine, health records management, etc Deep learning (Machine learning) Medical care / Decision making / Data processing Artificial intelligence / Medical applications Deep Learning Apprentissage profond Soins médicaux / Prise de décision / Informatique Intelligence artificielle en médecine Print version Jain, Vishal Deep Learning for Healthcare Decision Making Aalborg : River Publishers,c2023 9788770223898 https://public.ebookcentral.proquest.com/choice/PublicFullRecord.aspx?p=30172500 https://ieeexplore.ieee.org/book/9933494 Aggregator URL des Erstveröffentlichers Volltext |
| spellingShingle | Jain, Vishal 1983- Deep Learning for Healthcare Decision Making Preface xvii Acknowledgment xxi List of Figures xxiii List of Tables xxvii List of Contributors xxix List of Abbreviations xxxiii 1 Amalgamation of Deep Learning in Healthcare Systems 1 1.1 Introduction to Deep Learning 2 1.2 Deep Learning in Healthcare 4 1.3 Artificial Intelligence in the Healthcare System 6 1.4 Machine Learning in Healthcare 6 1.5 Natural Language Processing (NLP) in Healthcare 8 1.6 Deep Learning Models 9 1.6.1 Interpretation of deep learning models in medical images 10 1.6.1.1 Convolutional neural networks (CNNs) 11 1.6.1.2 Recurrent neural networks (RNNs) 12 1.6.1.3 Restricted boltzmann machines (RBMs) and deep belief networks (DBNs) 13 1.6.1.4 Deep neural network (DNN) 13 1.6.1.5 Generative adversarial network (GAN) 13 1.7 Radiologic Applications using Deep Learning 13 1.7.1 Image classification 14 1.7.2 Object detection 14 1.7.3 Image segmentation and registration 15 1.7.4 Image generation 15 1.7.5 Image transformation 16 1.7.5.1 Without the use of a generative adversarial network, image to image translation is possible 16 1.7.5.2 GAN for image-to-image translation 16 1.8 Predictive Analysis using Deep Learning and Machine Learning 17 1.9 Clinical Trials using Deep Learning 18 1.10 Applications of Deep Learning in the Healthcare System 19 1.10.1 Drug discovery 19 1.10.2 Medical imaging 19 1.10.3 Insurance fraud 19 1.10.4 Alzheimer’s disease 19 1.10.5 Genome 20 1.10.6 Healthcare data analytics 20 1.10.7 Mental health chatbots 20 1.10.8 Personalized medical treatments 20 1.10.9 Prescription audit 20 1.10.10 Responding to patient queries 21 Conclusion 21 References 21 2 Deep Neural Network Architecture and Applications in Healthcare 25 2.1 Introduction 26 2.2 Deep Neural Network 27 2.3 Deep Learning Architectures Applied in the Healthcare Field 32 2.3.1 Alzheimer’s disease 32 2.3.2 Brain mris 33 2.3.3 Osteoarthritis 33 2.3.4 Breast cancer 33 2.3.5 Diabetic retinopathy 33 2.3.6 Forecasting type of medicine based on patient history 33 2.3.7 Forecasting diseases through patient’s clinical status 34 2.3.8 Forecasting suicide 34 2.3.9 Forecasting readmission of patients after the discharge 34 2.3.10 Forecasting disease from lab test 34 2.3.11 Forecasting the quality of sleep by awake time activities 34 2.4 Pneumonia Detection using Deep Learning from X-ray Images 35 2.4.1 Overview 35 2.4.2 Methodology 37 2.4.2.1 Visualizing the images 37 2.4.2.2 Resizing 38 2.4.3 Results 39 2.4.3.1 ROC curve 39 2.4.3.2 Confusion matrix 39 Conclusion 39 References 41 3 The State of the Art of using Artificial Intelligence for Disease Identification and Diagnosis in Healthcare 47 3.1 Introduction 48 3.2 A Review of the Literature on Machine Learning and Artificial Intelligence in Healthcare 50 3.2.1 Machine learning applications in healthcare 50 3.2.2 Applications of artificial intelligence in healthcare 53 3.3 How to Develop Machine Learning Methods for Healthcare 54 3.3.1 Healthcare problem selection 54 3.3.2 Dataset construction 58 3.3.3 Model development 59 3.3.4 Model performance evaluation 59 3.3.5 Clinical impact evaluation 59 3.4 Disease Prediction and Diagnosis Using Artificial Intelligence 60 3.4.1 Disease prediction and diagnosis using machine learning 60 3.4.2 Artificial intelligence technology for clinical diagnosis 62 3.5 Challenges of using Artificial Intelligence Algorithms in Healthcare 66 Conclusion 67 References 68 4 Segmentation of MRI Images of Gliomas using Convolutional Neural Networks 77 4.1 Introduction 78 4.2 Aim 80 4.3 Objectives 80 4.4 Methodology 81 4.5 Implementation 82 4.5.1 Initial image processing 82 4.5.2 Training phase 82 4.5.3 Testing phase 83 4.5.4 Classification algorithm 84 4.6 Results 84 4.6.1 Training results 84 4.6.2 Test results 87 4.6.3 Dice scores 89 4.6.4 Positive Predictive Value (PPV) 90 4.6.5 Sensitivity 90 Conclusion 91 Future Prospects 92 References 93 5 Automatic Liver Tumor Segmentation from Computed Tomography Images Based on 2D and 3D Deep Neural Networks 97 5.1 Introduction 98 5.2 Related Concepts 99 5.2.1 Segmentation 99 5.2.2 Computed tomography 100 5.2.3 3D convolution 101 5.2.4 Separable convolution 102 5.2.5 Depthwise spatio-temporal separate 103 5.2.6 U-net 104 5.2.7 Efficientnet 105 5.2.8 Loss function 105 5.2.9 Metrics 106 5.2.9.1 Overlapping metrics 106 5.3 Related Work 108 5.4 Methodology 110 5.4.1 Data normalization and compression 111 5.4.2 Batch sampling 111 5.4.3 Data augmentation 112 5.4.4 Neural network architecture 113 5.4.5 Efficientnet modifications 113 5.4.6 Liver postprocessing 114 5.4.7 Mask combination 115 5.4.8 Tumor postprocessing 115 5.4.9 Network training 115 5.4.10 Dataset 117 5.5 Experiments 117 5.5.1 Local experiments setup 118 5.5.1.1 2D models 118 5.5.1.2 3D models 120 5.5.2 Local evaluation 121 5.5.3 LiTS challenge evaluation 124 Conclusion 126 References 127 6 Advancements in Deep Learning Techniques for Analyzing Electronic Medical Records 133 6.1 Introduction 134 6.2 Overview of EHR 135 6.2.1 Characteristics of EHR 135 6.2.2 Categories of EHR data 136 6.2.3 Doctor’s notes 136 6.3 Machine Learning and Deep Learning in EHR 136 6.3.1 Multilayer Perceptron (MLP) network 138 6.3.2 Convolutional Neural Network (CNN) 139 6.3.3 Recurrent Neural Network (RNN) 140 6.3.4 Restricted boltzmann machine 141 6.3.5 Autoencoders 141 6.4 Deep Learning Analysis of EHR 142 6.4.1 Extraction of information 144 6.4.1.1 Concept extraction 145 6.4.1.2 Time event extraction 146 6.4.1.3 Correlation extraction 146 6.4.1.4 Acronym expansion 146 6.4.2 Ehr representation 147 6.4.3 Evaluation of representation 148 6.4.4 Diagnosis of disease 148 6.4.5 Evaluation metric 149 6.4.6 Risk identification and survival prediction 149 6.5 EHR Data Set 149 6.6 Research Gap 150 6.7 Suggestions for Improvement 151 6.7.1 Tuning activation function 151 6.7.2 Constraints 151 6.7.3 Qualitative clustering 152 6.7.4 MIMIC learning 152 Conclusion 152 References 153 7 Telemedicine-based Development of M-Health Informatics using AI 159 7.1 Introduction 159 7.1 Objectives of Chapter 161 7.2 Literature Review 161 7.3 Wireless Technologies in m-Health 166 7.3.1 Wireless medical sensor technologies 167 7.4 Signals for Biomonitoring 168 7.4.1 Wireless communication for biomonitoring 169 7.5 Telemedicine Application Server 169 7.5.1 Server protocols 170 7.5.2 Server graphical user interface 170 7.6 Interface Program 171 7.6.1 Patient interface 171 7.6.2 Doctor browser interface 172 7.7 Experiment Work 172 Conclusion 175 References 175 8 Health Informatics System using Machine Learning Techniques 179 8.1 Introduction 180 8.1.1 COVID-19 pandemic 180 8.1.2 Necessity of AI 182 8.1.3 Artificial intelligence vs Machine learning vs deep learning 185 8.1.4 Healthcare informatics systems and analytics 187 8.2 Concept of Blockchain 188 8.2.1 Network architecture of blockchain 191 8.3 Concept of Data Leak and How It Is Overcome using Blockchain 193 8.3.1 Data breach targets 194 8.3.2 Data breach threats 195 8.3.3 Data breach consequences 195 8.4 Present Situation and Future Perspective 196 8.4.1 Drug traceability 196 8.4.2 Clinical trials 197 8.4.3 Management of patient data 198 8.5 Existing Challenges in the Future 201 8.5.1 Data security and privacy 202 8.5.2 Storage capacity management 202 8.5.3 Interoperability issues 203 8.5.4 Standardization challenges 203 8.5.5 Social challenges 203 Conclusion 203 References 205 9 Blockchain in Healthcare: A Systematic Review and Future Perspectives 211 9.1 Introduction 212 9.2 Healthcare Challenges vs Blockchain Opportunities 213 9.2.1 Interoperability 213 9.2.2 Tampering/data security 214 9.2.3 Insurance fraud 215 9.2.4 Drug stealing 216 9.3 Introduction to Blockchain 216 9.3.1 Structure of blocks and blockchain 216 9.3.2 Types of blockchain networks 218 9.3.3 Consensus algorithms 219 9.3.4 Smart contracts 219 9.4 Research Methodology 220 9.5 Literature Review 220 9.6 Discussion 224 9.6.1 RQ1: What type of blockchain network and blockchain platform has been used by researchers in their healthcare applications? 224 9.6.2 RQ2: What are the important consensus algorithms used in blockchain-based healthcare systems? 226 9.6.3 RQ3: What proportion of research works have built smart contracts in their healthcare systems and which programming languages have they used? 228 9.6.4 RQ4: What are the evaluation methods used by researchers for analyzing their models? 229 9.6.5 RQ5: What are the drawbacks of the existing blockchain systems related to healthcare and what are the future perspectives? 229 9.7 Challenges and Future Scope 231 9.7.1 Lack of standardization 232 9.7.2 Scalability 232 9.7.3 Latency 232 9.7.4 Privacy of outsourced data 232 Conclusion 233 References 233 10 Fusion of Machine Learning and Blockchain Techniques in IoT-based Smart Healthcare Systems 245 10.1 Introduction 246 10.2 Literature Review 247 10.3 Issues and Challenges While Establishing IoT In Healthcare 249 10.4 Involvement of Blockchain Technique in the Healthcare System 251 10.4.1 Securing and tracking health supplies 251 10.4.2 Storing health information 252 10.4.3 Remote patient monitoring 252 10.4.4 Disease outbreak and tracking 253 10.5 Indulgement of AI and ML in Healthcare System 253 10.5.1 Supervised learning 254 10.5.2 Unsupervised learning 255 10.5.3 Semi-supervised learning 255 10.5.4 Reinforcement learning 256 10.6 Working Process 256 10.7 Machine Learning Algorithms in the Healthcare Industry 257 10.7.1 Support vector machine 257 10.7.2 Logistic Regression (LR) 258 10.7.3 Decision Tree (DT) 259 10.7.4 Random Forest Tree (RFT) 259 10.7.5 Discriminant Analysis (DA) 260 10.7.6 K-Nearest neighbor 260 10.8 Solution via Blockchain and Artificial Intelligence 260 Conclusion 261 Acknowledgement 262 References 262 Index 267 About the Editors 271 |
| title | Deep Learning for Healthcare Decision Making |
| title_auth | Deep Learning for Healthcare Decision Making |
| title_exact_search | Deep Learning for Healthcare Decision Making |
| title_exact_search_txtP | Deep Learning for Healthcare Decision Making |
| title_full | Deep Learning for Healthcare Decision Making Vishal Jain, Jyotir Moy Chatterjee, Ishaani Priyadarshini, Fadi Al-Turjman |
| title_fullStr | Deep Learning for Healthcare Decision Making Vishal Jain, Jyotir Moy Chatterjee, Ishaani Priyadarshini, Fadi Al-Turjman |
| title_full_unstemmed | Deep Learning for Healthcare Decision Making Vishal Jain, Jyotir Moy Chatterjee, Ishaani Priyadarshini, Fadi Al-Turjman |
| title_short | Deep Learning for Healthcare Decision Making |
| title_sort | deep learning for healthcare decision making |
| url | https://public.ebookcentral.proquest.com/choice/PublicFullRecord.aspx?p=30172500 https://ieeexplore.ieee.org/book/9933494 |
| work_keys_str_mv | AT jainvishal deeplearningforhealthcaredecisionmaking |