Applied Smart Health Care Informatics (eBook, PDF)
A Computational Intelligence Perspective
Redaktion: De, Sourav; Maulik, Ujjwal; Bhattacharyya, Siddhartha; Das, Rik
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Applied Smart Health Care Informatics (eBook, PDF)
A Computational Intelligence Perspective
Redaktion: De, Sourav; Maulik, Ujjwal; Bhattacharyya, Siddhartha; Das, Rik
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Applied Smart Health Care Informatics Explores how intelligent systems offer new opportunities for optimizing the acquisition, storage, retrieval, and use of information in healthcare Applied Smart Health Care Informatics explores how health information technology and intelligent systems can be integrated and deployed to enhance healthcare management. Edited and authored by leading experts in the field, this timely volume introduces modern approaches for managing existing data in the healthcare sector by utilizing artificial intelligence (AI), meta-heuristic algorithms, deep learning, the…mehr
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- Produktdetails
- Verlag: John Wiley & Sons
- Seitenzahl: 224
- Erscheinungstermin: 21. Februar 2022
- Englisch
- ISBN-13: 9781119743200
- Artikelnr.: 63511080
- Verlag: John Wiley & Sons
- Seitenzahl: 224
- Erscheinungstermin: 21. Februar 2022
- Englisch
- ISBN-13: 9781119743200
- Artikelnr.: 63511080
of Applied Smart Health Care Informatics in the Context of Computational
Intelligence 1 Sourav De and Rik Das 1.1 Introduction 1 1.2 Big Data
Analytics in Healthcare 2 1.3 AI in Healthcare 3 1.4 Cloud Computing in
Healthcare 4 1.5 IoT in Healthcare 4 1.6 Conclusion 5 References 5 2 A
Review on Deep Learning Method for Lung Cancer Stage Classification Using
PET-CT 9 Kaushik Pratim Das, Chandra J, and Dr Nachamai M 2.1 Introduction
9 2.1.1 Scope of the Research 10 2.1.2 TNM Staging 11 2.1.2.1 TNM
Descriptors for Staging per IASLC Guidelines 11 2.1.2.2 PET-CT Scan in Lung
Cancer Imaging 12 2.2 Related Works 12 2.2.1 Artificial Intelligence in
Medical Imaging 14 2.2.2 Classification for Medical Imaging 14 2.2.2.1 Deep
Learning 15 2.2.2.2 Image Classification Using Deep-learning Techniques 15
2.3 Methods 15 2.3.1 Transfer Learning 15 2.3.2 AlexNet 16 2.3.3 AlexNet
Architecture 16 2.3.4 Experimental Setup 17 2.3.4.1 Image Processing 18
2.3.4.2 Data Augmentation 19 2.3.4.3 Training and Validation 19 2.4 Results
and Discussion 19 2.4.1 Primary Tumor (T) 19 2.4.2 Metastasis (M) 21 2.4.3
Lymph Node (N) 21 2.4.4 Classification Accuracy of AlexNet 24 2.4.5
Comparative Analysis 25 2.4.6 Limitations 26 2.5 Conclusion 26 References
27 3 Formal Methods for the Security of Medical Devices 31 Srinivas
Pinisetty, Nathan Allen, Hammond Pearce, Mark Trew, Manoj Singh Gaur, and
Partha Roop 3.1 Introduction 31 3.1.1 Pacemaker Security 33 3.1.2 Overview
34 3.2 Background: Cardiac Pacemakers 34 3.2.1 Pacemakers 35 3.2.1.1
Operation of a DDD Mode Pacemaker 36 3.2.2 The Cardiac System 37 3.2.2.1
Electrograms and Electrocardiograms 38 3.3 State of the Art, Formal
Verification Techniques 39 3.3.1 Formal Verification Techniques 40 3.3.1.1
Static Verification Techniques 41 3.3.1.2 Dynamic Verification Techniques
42 3.3.2 Runtime Verification 43 3.3.2.1 A Brief Overview of Some Runtime
Verification Frameworks 44 3.3.3 Correcting Execution of a System at
Runtime (Runtime Enforcement) 45 3.3.3.1 Runtime Enforcement of Untimed
Properties 46 3.3.3.2 Runtime Enforcement Approaches for Timed Properties
46 3.4 Formal Runtime-Based Approaches for Medical Device Security 47 3.4.1
Overview of the Approach 47 3.4.2 Mapping EGM Properties to ECG Properties
48 3.4.3 Security of Pacemakers Using Runtime Verification 49 3.4.3.1 Timed
Words, Timed Languages, and Defining Timed Properties 50 3.4.3.2 Runtime
Verification Monitor 51 3.4.3.3 Architecture of the Monitoring System 53
3.4.3.4 Implementation of the ECG Processing and RV Monitor Modules 53
3.4.3.5 Summary of Experiments and Results 54 3.4.4 Securing Pacemakers
with Runtime Enforcement Hardware 54 3.4.4.1 Preliminaries: Words,
Languages, and Defining Properties as DTA 55 3.4.4.2 Runtime Enforcement
Monitor 56 3.4.4.3 Verification of the Enforcer Hardware 58 3.4.4.4 How
Does the Enforcer Prevent Security Attacks? 58 3.4.4.5 Summary of
Experiments and Results 59 3.5 Summary 59 References 60 4 Integrating Two
Deep Learning Models to Identify Gene Signatures in Head and Neck Cancer
from Multi-Omics Data 67 Suparna Saha, Sumanta Ray, and Sanghamitra
Bandyopadhyay 4.1 Introduction 67 4.2 Related Work 68 4.3 Materials and
Methods 70 4.3.1 A Brief Introduction of the Capsule Network 70 4.3.2 An
Introduction to Autoencoders 71 4.4 Results 72 4.4.1 Data Set Details 72
4.4.1.1 Gene Expression Data (Illumina Hiseq) 72 4.4.1.2 Human Methylation
450K 73 4.4.2 Architecture of Autoencoder Model 73 4.4.3 Architecture of
the Proposed Capsule Network Model 74 4.4.4 Validation of Two Deep Learning
Models 75 4.4.5 Gene Signatures from Primary Capsules 76 4.5 Discussion 77
Acknowledgments 78 References 79 5 A Review of Computational Learning and
IoT Applications to High-Throughput Array-Based Sequencing and Medical
Imaging Data in Drug Discovery and Other Health Care Systems 83 Soham
Choudhuri, Saurav Mallik, Bhaswar Ghosh, Tapas Si, Tapas Bhadra, Ujjwal
Maulik, and Aimin Li 5.1 Introduction 83 5.2 Biological Terms 84 5.3
Single-Cell Sequencing (scRNA-seq) Data 86 5.3.1 Computational Methods for
Interpreting scRNA-seq Data 86 5.3.1.1 Visualizing and Clustering Cells 86
5.3.1.2 Inference and Branching Analysis of Cellular Trajectory 86 5.3.1.3
Identifying Highly Variable Genes 86 5.3.1.4 Identifying Marker and
Differentially Expressed Genes 90 5.4 Methods of Multi-Omic Data
Integration 90 5.4.1 Unsupervised Data Integration Methods 91 5.4.1.1
Matrix Factorization Methods 91 5.4.1.2 Bayesian Methods 91 5.4.1.3
Network-Based Methods 94 5.4.1.4 Multi-Step Analysis and Multiple Kernel
Learning 94 5.4.2 Supervised Data Integration 95 5.4.2.1 Network-Based
Methods 95 5.4.2.2 Multiple Kernel Learning 95 5.4.2.3 Multi-Step Analysis
95 5.4.3 Semi-Supervised Data Integration 95 5.4.3.1 GeneticInterPred 97
5.5 AI Drug Discovery 97 5.5.1 AI Primary Drug Screening 97 5.5.1.1 Cell
Sorting and Classification with Image Analysis 97 5.5.2 AI Secondary Drug
Screening 99 5.5.2.1 Physical Properties Predictions 99 5.5.2.2 Predictions
of Bio-Activity 99 5.5.2.3 Prediction of Toxicity 99 5.5.3 AI in Drug
Design 99 5.5.3.1 Prediction of Target Protein 3D Structures 99 5.5.3.2
Predicting Drug-Protein Interactions 100 5.5.4 Planning Chemical Synthesis
with AI 100 5.5.4.1 Retro-Synthesis Pathway Prediction 100 5.5.4.2 Reaction
Yield Predictions and Reaction Mechanism Insights 100 5.6 Medical Imaging
Data Analysis 100 5.6.1 Analysis: Radio-Mic Quantification 101 5.6.2
Analysis: Bio-Marker Identification 101 5.7 Applying IoT (Internet of
Things) to Biomedical Research 102 5.7.1 IoT and IoMT Applications for
Healthcare and Well-Being 102 5.7.1.1 Wireless Medical Devices 102 5.8
Conclusions 102 Acknowledgments 102 References 102 6 Association Rule
Mining Based on Ethnic Groups and Classification using Super Learning 111
Md Faisal Kabir and Simone A. Ludwig 6.1 Introduction 111 6.2 Background
112 6.3 Motivation and Contribution 114 6.4 Data Analysis 115 6.4.1 Data
Description 115 6.4.2 Data Preprocessing 115 6.4.3 Further Preprocessing
for Ethnic Group Rule Discovery with Multiple Consequences 115 6.4.3.1
Transaction-Like Database for Association Rule 115 6.4.4 Classification
Data Set 116 6.5 Methodology 117 6.5.1 Association Rule Mining 117 6.5.2
Super Learning 118 6.5.2.1 Ensemble or Super Learner Set-Up 118 6.6
Experiments and Results 119 6.6.1 Rules Discovery 120 6.6.1.1 Rules of
Breast Cancer Patients Based on Ethnic Groups 120 6.6.1.2 Interpreting
Rules 120 6.6.2 Evaluation Criteria of Classification Model 121 6.6.2.1
Super Learner Results 124 6.6.3 Discussion 125 6.7 Conclusion and Future
Work 126 References 127 7 Neuro-Rough Hybridization for Recognition of
Virus Particles from TEM Images 131 Debamita Kumar and Pradipta Maji 7.1
Introduction 131 7.2 Existing Approaches for Virus Particle Classification
132 7.3 Proposed Algorithm 134 7.3.1 Extraction of Local Textural Features
135 7.3.2 Selection of Class-Pair Relevant Features 135 7.3.3 Extraction of
Discriminating Features 138 7.3.4 Classification 139 7.4 Experimental
Results and Discussion 140 7.4.1 Experimental Setup 140 7.4.2 Methods
Compared 140 7.4.3 Database Considered 141 7.4.4 Effectiveness of Proposed
Approach 141 7.4.5 Comparative Performance Analysis 143 7.4.5.1 Comparison
with Deep Architectures 144 7.4.5.2 Comparison with Existing Approaches 145
7.5 Conclusion 146 References 147 8 Neural Network Optimizers for Brain
Tumor Image Detection 151 T. Kalaiselvi and S.T. Padmapriya 8.1
Introduction 151 8.2 Related Works 152 8.3 Background 153 8.3.1 Types of
Neural Networks 153 8.3.2 Tunable Elements of Neural Networks 154 8.3.2.1
Basic Parameters 154 8.3.2.2 Hyperparameters 154 8.3.2.3 Regularization
Techniques 155 8.3.2.4 Neural Network Optimizers 156 8.4 Case Study - Brain
Tumor Detection 157 8.4.1 Methodology 157 8.4.2 Data Sets and Metrics 157
8.4.3 Results and Discussion 159 8.5 Conclusion 162 References 162 9
Abnormal Slice Classification from MRI Volumes using the Bilateral Symmetry
of Human Head Scans 165 N. Kalaichelvi, T. Kalaiselvi, and K. Somasundaram
9.1 Introduction 165 9.1.1 MRIs of the Human Brain 165 9.1.2 Normal and
Abnormal Slices 166 9.1.3 Background 167 9.1.3.1 Decision Tree Classifiers
167 9.1.3.2 K-Nearest Neighbours (KNN) Classifiers 168 9.1.3.3 Support
Vector Machine (SVM) 168 9.1.3.4 Naive Bayes 169 9.1.3.5 Artificial Neural
Network (ANN) 169 9.1.3.6 Back-Propagation Neural Network (BPN) 170 9.1.3.7
Random Forest Classifiers 170 9.2 Literature Review 171 9.3 Methodology 172
9.3.1 Preprocessing 173 9.3.2 Feature Extraction 174 9.3.3 Feature
Selection 175 9.3.4 Classification 177 9.3.5 Cross-Validation 177 9.3.6
Training Validation and Testing 178 9.4 Materials and Metrics 179 9.4.1
Confusion Matrix 179 9.5 Results and Discussion 180 9.6 Conclusion 182
References 183 10 Conclusion 187 Siddhartha Bhattacharyya References 188
Index 191
of Applied Smart Health Care Informatics in the Context of Computational
Intelligence 1 Sourav De and Rik Das 1.1 Introduction 1 1.2 Big Data
Analytics in Healthcare 2 1.3 AI in Healthcare 3 1.4 Cloud Computing in
Healthcare 4 1.5 IoT in Healthcare 4 1.6 Conclusion 5 References 5 2 A
Review on Deep Learning Method for Lung Cancer Stage Classification Using
PET-CT 9 Kaushik Pratim Das, Chandra J, and Dr Nachamai M 2.1 Introduction
9 2.1.1 Scope of the Research 10 2.1.2 TNM Staging 11 2.1.2.1 TNM
Descriptors for Staging per IASLC Guidelines 11 2.1.2.2 PET-CT Scan in Lung
Cancer Imaging 12 2.2 Related Works 12 2.2.1 Artificial Intelligence in
Medical Imaging 14 2.2.2 Classification for Medical Imaging 14 2.2.2.1 Deep
Learning 15 2.2.2.2 Image Classification Using Deep-learning Techniques 15
2.3 Methods 15 2.3.1 Transfer Learning 15 2.3.2 AlexNet 16 2.3.3 AlexNet
Architecture 16 2.3.4 Experimental Setup 17 2.3.4.1 Image Processing 18
2.3.4.2 Data Augmentation 19 2.3.4.3 Training and Validation 19 2.4 Results
and Discussion 19 2.4.1 Primary Tumor (T) 19 2.4.2 Metastasis (M) 21 2.4.3
Lymph Node (N) 21 2.4.4 Classification Accuracy of AlexNet 24 2.4.5
Comparative Analysis 25 2.4.6 Limitations 26 2.5 Conclusion 26 References
27 3 Formal Methods for the Security of Medical Devices 31 Srinivas
Pinisetty, Nathan Allen, Hammond Pearce, Mark Trew, Manoj Singh Gaur, and
Partha Roop 3.1 Introduction 31 3.1.1 Pacemaker Security 33 3.1.2 Overview
34 3.2 Background: Cardiac Pacemakers 34 3.2.1 Pacemakers 35 3.2.1.1
Operation of a DDD Mode Pacemaker 36 3.2.2 The Cardiac System 37 3.2.2.1
Electrograms and Electrocardiograms 38 3.3 State of the Art, Formal
Verification Techniques 39 3.3.1 Formal Verification Techniques 40 3.3.1.1
Static Verification Techniques 41 3.3.1.2 Dynamic Verification Techniques
42 3.3.2 Runtime Verification 43 3.3.2.1 A Brief Overview of Some Runtime
Verification Frameworks 44 3.3.3 Correcting Execution of a System at
Runtime (Runtime Enforcement) 45 3.3.3.1 Runtime Enforcement of Untimed
Properties 46 3.3.3.2 Runtime Enforcement Approaches for Timed Properties
46 3.4 Formal Runtime-Based Approaches for Medical Device Security 47 3.4.1
Overview of the Approach 47 3.4.2 Mapping EGM Properties to ECG Properties
48 3.4.3 Security of Pacemakers Using Runtime Verification 49 3.4.3.1 Timed
Words, Timed Languages, and Defining Timed Properties 50 3.4.3.2 Runtime
Verification Monitor 51 3.4.3.3 Architecture of the Monitoring System 53
3.4.3.4 Implementation of the ECG Processing and RV Monitor Modules 53
3.4.3.5 Summary of Experiments and Results 54 3.4.4 Securing Pacemakers
with Runtime Enforcement Hardware 54 3.4.4.1 Preliminaries: Words,
Languages, and Defining Properties as DTA 55 3.4.4.2 Runtime Enforcement
Monitor 56 3.4.4.3 Verification of the Enforcer Hardware 58 3.4.4.4 How
Does the Enforcer Prevent Security Attacks? 58 3.4.4.5 Summary of
Experiments and Results 59 3.5 Summary 59 References 60 4 Integrating Two
Deep Learning Models to Identify Gene Signatures in Head and Neck Cancer
from Multi-Omics Data 67 Suparna Saha, Sumanta Ray, and Sanghamitra
Bandyopadhyay 4.1 Introduction 67 4.2 Related Work 68 4.3 Materials and
Methods 70 4.3.1 A Brief Introduction of the Capsule Network 70 4.3.2 An
Introduction to Autoencoders 71 4.4 Results 72 4.4.1 Data Set Details 72
4.4.1.1 Gene Expression Data (Illumina Hiseq) 72 4.4.1.2 Human Methylation
450K 73 4.4.2 Architecture of Autoencoder Model 73 4.4.3 Architecture of
the Proposed Capsule Network Model 74 4.4.4 Validation of Two Deep Learning
Models 75 4.4.5 Gene Signatures from Primary Capsules 76 4.5 Discussion 77
Acknowledgments 78 References 79 5 A Review of Computational Learning and
IoT Applications to High-Throughput Array-Based Sequencing and Medical
Imaging Data in Drug Discovery and Other Health Care Systems 83 Soham
Choudhuri, Saurav Mallik, Bhaswar Ghosh, Tapas Si, Tapas Bhadra, Ujjwal
Maulik, and Aimin Li 5.1 Introduction 83 5.2 Biological Terms 84 5.3
Single-Cell Sequencing (scRNA-seq) Data 86 5.3.1 Computational Methods for
Interpreting scRNA-seq Data 86 5.3.1.1 Visualizing and Clustering Cells 86
5.3.1.2 Inference and Branching Analysis of Cellular Trajectory 86 5.3.1.3
Identifying Highly Variable Genes 86 5.3.1.4 Identifying Marker and
Differentially Expressed Genes 90 5.4 Methods of Multi-Omic Data
Integration 90 5.4.1 Unsupervised Data Integration Methods 91 5.4.1.1
Matrix Factorization Methods 91 5.4.1.2 Bayesian Methods 91 5.4.1.3
Network-Based Methods 94 5.4.1.4 Multi-Step Analysis and Multiple Kernel
Learning 94 5.4.2 Supervised Data Integration 95 5.4.2.1 Network-Based
Methods 95 5.4.2.2 Multiple Kernel Learning 95 5.4.2.3 Multi-Step Analysis
95 5.4.3 Semi-Supervised Data Integration 95 5.4.3.1 GeneticInterPred 97
5.5 AI Drug Discovery 97 5.5.1 AI Primary Drug Screening 97 5.5.1.1 Cell
Sorting and Classification with Image Analysis 97 5.5.2 AI Secondary Drug
Screening 99 5.5.2.1 Physical Properties Predictions 99 5.5.2.2 Predictions
of Bio-Activity 99 5.5.2.3 Prediction of Toxicity 99 5.5.3 AI in Drug
Design 99 5.5.3.1 Prediction of Target Protein 3D Structures 99 5.5.3.2
Predicting Drug-Protein Interactions 100 5.5.4 Planning Chemical Synthesis
with AI 100 5.5.4.1 Retro-Synthesis Pathway Prediction 100 5.5.4.2 Reaction
Yield Predictions and Reaction Mechanism Insights 100 5.6 Medical Imaging
Data Analysis 100 5.6.1 Analysis: Radio-Mic Quantification 101 5.6.2
Analysis: Bio-Marker Identification 101 5.7 Applying IoT (Internet of
Things) to Biomedical Research 102 5.7.1 IoT and IoMT Applications for
Healthcare and Well-Being 102 5.7.1.1 Wireless Medical Devices 102 5.8
Conclusions 102 Acknowledgments 102 References 102 6 Association Rule
Mining Based on Ethnic Groups and Classification using Super Learning 111
Md Faisal Kabir and Simone A. Ludwig 6.1 Introduction 111 6.2 Background
112 6.3 Motivation and Contribution 114 6.4 Data Analysis 115 6.4.1 Data
Description 115 6.4.2 Data Preprocessing 115 6.4.3 Further Preprocessing
for Ethnic Group Rule Discovery with Multiple Consequences 115 6.4.3.1
Transaction-Like Database for Association Rule 115 6.4.4 Classification
Data Set 116 6.5 Methodology 117 6.5.1 Association Rule Mining 117 6.5.2
Super Learning 118 6.5.2.1 Ensemble or Super Learner Set-Up 118 6.6
Experiments and Results 119 6.6.1 Rules Discovery 120 6.6.1.1 Rules of
Breast Cancer Patients Based on Ethnic Groups 120 6.6.1.2 Interpreting
Rules 120 6.6.2 Evaluation Criteria of Classification Model 121 6.6.2.1
Super Learner Results 124 6.6.3 Discussion 125 6.7 Conclusion and Future
Work 126 References 127 7 Neuro-Rough Hybridization for Recognition of
Virus Particles from TEM Images 131 Debamita Kumar and Pradipta Maji 7.1
Introduction 131 7.2 Existing Approaches for Virus Particle Classification
132 7.3 Proposed Algorithm 134 7.3.1 Extraction of Local Textural Features
135 7.3.2 Selection of Class-Pair Relevant Features 135 7.3.3 Extraction of
Discriminating Features 138 7.3.4 Classification 139 7.4 Experimental
Results and Discussion 140 7.4.1 Experimental Setup 140 7.4.2 Methods
Compared 140 7.4.3 Database Considered 141 7.4.4 Effectiveness of Proposed
Approach 141 7.4.5 Comparative Performance Analysis 143 7.4.5.1 Comparison
with Deep Architectures 144 7.4.5.2 Comparison with Existing Approaches 145
7.5 Conclusion 146 References 147 8 Neural Network Optimizers for Brain
Tumor Image Detection 151 T. Kalaiselvi and S.T. Padmapriya 8.1
Introduction 151 8.2 Related Works 152 8.3 Background 153 8.3.1 Types of
Neural Networks 153 8.3.2 Tunable Elements of Neural Networks 154 8.3.2.1
Basic Parameters 154 8.3.2.2 Hyperparameters 154 8.3.2.3 Regularization
Techniques 155 8.3.2.4 Neural Network Optimizers 156 8.4 Case Study - Brain
Tumor Detection 157 8.4.1 Methodology 157 8.4.2 Data Sets and Metrics 157
8.4.3 Results and Discussion 159 8.5 Conclusion 162 References 162 9
Abnormal Slice Classification from MRI Volumes using the Bilateral Symmetry
of Human Head Scans 165 N. Kalaichelvi, T. Kalaiselvi, and K. Somasundaram
9.1 Introduction 165 9.1.1 MRIs of the Human Brain 165 9.1.2 Normal and
Abnormal Slices 166 9.1.3 Background 167 9.1.3.1 Decision Tree Classifiers
167 9.1.3.2 K-Nearest Neighbours (KNN) Classifiers 168 9.1.3.3 Support
Vector Machine (SVM) 168 9.1.3.4 Naive Bayes 169 9.1.3.5 Artificial Neural
Network (ANN) 169 9.1.3.6 Back-Propagation Neural Network (BPN) 170 9.1.3.7
Random Forest Classifiers 170 9.2 Literature Review 171 9.3 Methodology 172
9.3.1 Preprocessing 173 9.3.2 Feature Extraction 174 9.3.3 Feature
Selection 175 9.3.4 Classification 177 9.3.5 Cross-Validation 177 9.3.6
Training Validation and Testing 178 9.4 Materials and Metrics 179 9.4.1
Confusion Matrix 179 9.5 Results and Discussion 180 9.6 Conclusion 182
References 183 10 Conclusion 187 Siddhartha Bhattacharyya References 188
Index 191