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E-raamat: Applied Smart Health Care Informatics: A Computational Intelligence Perspective

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"Health care informatics aka medical informatics refers to application of information engineering and management to the field of health care covering essentially the management and use of patient health care information. By means of a multidisciplinary approach, it uses health information technology for the purpose of improving health care by resorting to newer and higher quality opportunities. As per the United States National Library of Medicine (NLM) health informatics is defined as "an interdisciplinary study of the design, development, adoption and application of IT-based innovations in health care services delivery, management and planning. Essentially, it effects the optimization of the acquisition, storage, retrieval, and use of information in health and bio-medicine. Intelligent health care informatics augments the purview of the existing health care amenities by enveloping intelligent technologies to the information engineering aspects. Intelligent analysis of the information therein enhances the overall management as far as usage of resources is concerned"--

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 Internet of Things (IoT), and other smart technologies.

Detailed chapters review advances in areas including machine learning, computer vision, and soft computing techniques, and discuss various applications of healthcare management systems such as medical imaging, electronic medical records (EMR), and drug development assistance. Throughout the text, the authors propose new research directions and highlight the smart technologies that are central to establishing proactive health management, supporting enhanced coordination of care, and improving the overall quality of healthcare services.

  • Provides an overview of different deep learning applications for intelligent healthcare informatics management
  • Describes novel methodologies and emerging trends in artificial intelligence and computational intelligence and their relevance to health information engineering and management
  • Proposes IoT solutions that disseminate essential medical information for intelligent healthcare management
  • Discusses mobile-based healthcare management, content-based image retrieval, and computer-aided diagnosis using machine and deep learning techniques
  • Examines the use of exploratory data analysis in intelligent healthcare informatics systems

Applied Smart Health Care Informatics: A Computational Intelligence Perspective is an invaluable text for graduate students, postdoctoral researchers, academic lecturers, and industry professionals working in the area of healthcare and intelligent soft computing.

Preface xiii
About the Editors xix
List of Contributors
xxv
1 An Overview of Applied Smart Health Care Informatics in the Context of Computational Intelligence
1(8)
Sourav De
Rik Das
1.1 Introduction
1(1)
1.2 Big Data Analytics in Healthcare
2(1)
1.3 AI in Healthcare
3(1)
1.4 Cloud Computing in Healthcare
4(1)
1.5 IoT in Healthcare
4(1)
1.6 Conclusion
5(1)
References
5(4)
2 A Review on Deep Learning Method for Lung Cancer Stage Classification Using PET-CT
9(22)
Kaushik Pratim Das
J. Chandra
Dr M. Nachamai
2.1 Introduction
9(3)
2.1.1 Scope of the Research
10(1)
2.1.2 TNM Staging
11(1)
2.1.2.1 TNM Descriptors for Staging per IASLC Guidelines
11(1)
2.1.2.2 PET-CT Scan in Lung Cancer Imaging
12(1)
2.2 Related Works
12(3)
2.2.1 Artificial Intelligence in Medical Imaging
14(1)
2.2.2 Classification for Medical Imaging
14(1)
2.2.2.1 Deep Learning
15(1)
2.2.2.2 Image Classification Using Deep-learning Techniques
15(1)
2.3 Methods
15(4)
2.3.1 Transfer Learning
15(1)
2.3.2 AlexNet
16(1)
2.3.3 AlexNet Architecture
16(1)
2.3.4 Experimental Setup
17(1)
2.3.4.1 Image Processing
18(1)
2.3.4.2 Data Augmentation
19(1)
2.3.4.3 Training and Validation
19(1)
2.4 Results and Discussion
19(7)
2.4.1 Primary Tumor (T)
19(2)
2.4.2 Metastasis (M)
21(1)
2.4.3 Lymph Node (N)
21(3)
2.4.4 Classification Accuracy of AlexNet
24(1)
2.4.5 Comparative Analysis
25(1)
2.4.6 Limitations
26(1)
2.5 Conclusion
26(1)
References
27(4)
3 Formal Methods for the Security of Medical Devices
31(80)
Srinivas Pinisetty
Nathan Allen
Hammond Pearce
Mark Trew
Manoj Singh Gaur
Partha Roop
3.1 Introduction
31(3)
3.1.1 Pacemaker Security
33(1)
3.1.2 Overview
34(1)
3.2 Background: Cardiac Pacemakers
34(5)
3.2.1 Pacemakers
35(1)
3.2.1.1 Operation of a DDD Mode Pacemaker
36(1)
3.2.2 The Cardiac System
37(1)
3.2.2.1 Electrograms and Electrocardiograms
38(1)
3.3 State of the Art, Formal Verification Techniques
39(8)
3.3.1 Formal Verification Techniques
40(1)
3.3.1.1 Static Verification Techniques
41(1)
3.3.1.2 Dynamic Verification Techniques
42(1)
3.3.2 Runtime Verification
43(1)
3.3.2.1 A Brief Overview of Some Runtime Verification Frameworks
44(1)
3.3.3 Correcting Execution of a System at Runtime (Runtime Enforcement)
45(1)
3.3.3.1 Runtime Enforcement of Untimed Properties
46(1)
3.3.3.2 Runtime Enforcement Approaches for Timed Properties
46(1)
3.4 Formal Runtime-Based Approaches for Medical Device Security
47(50)
3.4.1 Overview of the Approach
47(1)
3.4.2 Mapping EGM Properties to ECG Properties
48(1)
3.4.3 Security of Pacemakers Using Runtime Verification
49(1)
3.4.3.1 Timed Words, Timed Languages, and Defining Timed Properties
50(1)
3.4.3.2 Runtime Verification Monitor
51(2)
3.4.3.3 Architecture of the Monitoring System
53(1)
3.4.3.4 Implementation of the ECG Processing and RV Monitor Modules
53(1)
3.4.3.5 Summary of Experiments and Results
54(1)
3.4.4 Securing Pacemakers with Runtime Enforcement Hardware
54(1)
3.4.4.1 Preliminaries: Words, Languages, and Defining Properties as DTA
55(36)
5.4.1.2 Bayesian Methods
91(3)
5.4.1.3 Network-Based Methods
94(1)
5.4.1.4 Multi-Step Analysis and Multiple Kernel Learning
94(1)
5.4.2 Supervised Data Integration
95(1)
5.4.2.1 Network-Based Methods
95(1)
5.4.2.2 Multiple Kernel Learning
95(1)
5.4.2.3 Multi-Step Analysis
95(1)
5.4.3 Semi-Supervised Data Integration
95(2)
5.4.3.1 GeneticInterPred
97(1)
5.5 AI Drug Discovery
97(3)
5.5.1 AI Primary Drug Screening
97(1)
5.5.1.1 Cell Sorting and Classification with Image Analysis
97(2)
5.5.2 AI Secondary Drug Screening
99(1)
5.5.2.1 Physical Properties Predictions
99(1)
5.5.2.2 Predictions of Bio-Activity
99(1)
5.5.2.3 Prediction of Toxicity
99(1)
5.5.3 AI in Drug Design
99(1)
5.5.3.1 Prediction of Target Protein 3D Structures
99(1)
5.5.3.2 Predicting Drug-Protein Interactions
100(1)
5.5.4 Planning Chemical Synthesis with AI
100(1)
5.5.4.1 Retro-Synthesis Pathway Prediction
100(1)
5.5.4.2 Reaction Yield Predictions and Reaction Mechanism Insights
100(1)
5.6 Medical Imaging Data Analysis
100(2)
5.6.1 Analysis: Radio-Mic Quantification
101(1)
5.6.2 Analysis: Bio-Marker Identification
101(1)
5.7 Applying IoT (Internet of Things) to Biomedical Research
102(1)
5.7.1 IoT and IoMT Applications for Healthcare and Weil-Being
102(1)
5.7.1.1 Wireless Medical Devices
102(1)
5.8 Conclusions
102(1)
Acknowledgments
102(1)
References
102(9)
6 Association Rule Mining Based on Ethnic Groups and Classification using Super Learning
111(20)
Md Faisal Kabir
Simone A. Ludwig
6.1 Introduction
111(1)
6.2 Background
112(2)
6.3 Motivation and Contribution
114(1)
6.4 Data Analysis
115(2)
6.4.1 Data Description
115(1)
6.4.2 Data Preprocessing
115(1)
6.4.3 Further Preprocessing for Ethnic Group Rule Discovery with Multiple Consequences
115(1)
6.4.3.1 Transaction-Like Database for Association Rule
115(1)
6.4.4 Classification Data Set
116(1)
6.5 Methodology
117(2)
6.5.1 Association Rule Mining
117(1)
6.5.2 Super Learning
118(1)
6.5.2.1 Ensemble or Super Learner Set-Up
118(1)
6.6 Experiments and Results
119(7)
6.6.1 Rules Discovery
120(1)
6.6.1.1 Rules of Breast Cancer Patients Based on Ethnic Groups
120(1)
6.6.1.2 Interpreting Rules
120(1)
6.6.2 Evaluation Criteria of Classification Model
121(3)
6.6.2.1 Super Learner Results
124(1)
6.6.3 Discussion
125(1)
6.7 Conclusion and Future Work
126(1)
References
127(4)
7 Neuro-Rough Hybridization for Recognition of Virus Particles from TEM Images
131(20)
Debamita Kumar
Pradipta Maji
7.1 Introduction
131(1)
7.2 Existing Approaches for Virus Particle Classification
132(2)
7.3 Proposed Algorithm
134(6)
7.3.1 Extraction of Local Textural Features
135(1)
7.3.2 Selection of Class-Pair Relevant Features
135(3)
7.3.3 Extraction of Discriminating Features
138(1)
7.3.4 Classification
139(1)
7.4 Experimental Results and Discussion
140(6)
7.4.1 Experimental Setup
140(1)
7.4.2 Methods Compared
140(1)
7.4.3 Database Considered
141(1)
7.4.4 Effectiveness of Proposed Approach
141(2)
7.4.5 Comparative Performance Analysis
143(1)
7.4.5.1 Comparison with Deep Architectures
144(1)
7.4.5.2 Comparison with Existing Approaches
145(1)
7.5 Conclusion
146(1)
References
147(4)
8 Neural Network Optimizers for Brain Tumor Image Detection
151(14)
T. Kalaisetvi
S.T. Padmapriya
8.1 Introduction
151(1)
8.2 Related Works
152(1)
8.3 Background
153(4)
8.3.1 Types of Neural fretworks
153(1)
8.3.2 Tunable Elements of Neural Networks
154(1)
8.3.2.1 Basic Parameters
154(1)
8.3.2.2 Hyperparameters
154(1)
8.3.2.3 Regularization Techniques
155(1)
8.3.2.4 Neural Network Optimizers
156(1)
8.4 Case Study - Brain Tumor Detection
157(5)
8.4.1 Methodology
157(1)
8.4.2 Data Sets and Metrics
157(2)
8.4.3 Results and Discussion
159(3)
8.5 Conclusion
162(1)
References
162(3)
9 Abnormal Slice Classification from MRI Volumes using the Bilateral Symmetry of Human Head Scans
165(22)
N. Kalaichelvi
T. Kalaiselvi
K. Somasundaram
9.1 Introduction
165(6)
9.1.1 MRIs of the Human Brain
165(1)
9.1.2 Normal and Abnormal Slices
166(1)
9.1.3 Background
167(1)
9.1.3.1 Decision Tree Classifiers
167(1)
9.1.3.2 K-Nearest Neighbours (KNN) Classifiers
168(1)
9.1.3.3 Support Vector Machine (SVM)
168(1)
9.1.3.4 Naive Bayes
169(1)
9.1.3.5 Artificial Neural Network (ANN)
169(1)
9.1.3.6 Back-Propagation Neural Network (BPN)
170(1)
9.1.3.7 Random Forest Classifiers
170(1)
9.2 Literature Review
171(1)
9.3 Methodology
172(7)
9.3.1 Preprocessing
173(1)
9.3.2 Feature Extraction
174(1)
9.3.3 Feature Selection
175(2)
9.3.4 Classification
177(1)
9.3.5 Cross-Validation
177(1)
9.3.6 Training Validation and Testing
178(1)
9.4 Materials and Metrics
179(1)
9.4.1 Confusion Matrix
179(1)
9.5 Results and Discussion
180(2)
9.6 Conclusion
182(1)
References
183(4)
10 Conclusion
187(4)
Siddhartha Bhattacharyya
References
188(3)
Index 191
Dr. Sourav De, Associate Professor, Department of Computer Science and Engineering, Cooch Behar Government Engineering College, India.

Dr. Rik Das, Assistant Professor, Department of Information Technology, Xavier Institute of Social Service, India.

Dr. Siddhartha Bhattacharyya, Principal, Rajnagar Mahavidyalaya, India.

Dr. Ujjwal Maulik, Professor, Department of Computer Science and Engineering, Jadavpur University, India.
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