Оглавление
Группа авторов. Biomedical Data Mining for Information Retrieval
Table of Contents
Guide
List of Illustrations
List of Tables
Pages
Biomedical Data Mining for Information Retrieval. Methodologies, Techniques and Applications
Preface. Introduction
Organization of the Book
Concluding Remarks
1. Mortality Prediction of ICU Patients Using Machine Learning Techniques
1.1 Introduction
1.2 Review of Literature
1.3 Materials and Methods. 1.3.1 Dataset
1.3.2 Data Pre-Processing
1.3.3 Normalization
1.3.4 Mortality Prediction
1.3.5 Model Description and Development
1.4 Result and Discussion
1.5 Conclusion
1.6 Future Work
References
2. Artificial Intelligence in Bioinformatics
2.1 Introduction
2.2 Recent Trends in the Field of AI in Bioinformatics
2.2.1 DNA Sequencing and Gene Prediction Using Deep Learning
2.3 Data Management and Information Extraction
2.4 Gene Expression Analysis
2.4.1 Approaches for Analysis of Gene Expression
2.4.2 Applications of Gene Expression Analysis
2.5 Role of Computation in Protein Structure Prediction
2.6 Application in Protein Folding Prediction
2.7 Role of Artificial Intelligence in Computer-Aided Drug Design
2.8 Conclusions
References
3. Predictive Analysis in Healthcare Using Feature Selection
3.1 Introduction
3.1.1 Overview and Statistics About the Disease. 3.1.1.1 Diabetes
3.1.1.2 Hepatitis
3.1.2 Overview of the Experiment Carried Out
3.2 Literature Review. 3.2.1 Summary
3.2.2 Comparison of Papers for Diabetes and Hepatitis Dataset
3.3 Dataset Description. 3.3.1 Diabetes Dataset
3.3.2 Hepatitis Dataset
3.4 Feature Selection
3.4.1 Importance of Feature Selection
3.4.2 Difference Between Feature Selection, Feature Extraction and Dimensionality Reduction
3.4.3 Why Traditional Feature Selection Techniques Still Holds True?
3.4.4 Advantages and Disadvantages of Feature Selection Technique. 3.4.4.1 Advantages
3.4.4.2 Disadvantage
3.5 Feature Selection Methods
3.5.1 Filter Method
3.5.1.1 Basic Filter Methods
3.5.1.2 Correlation Filter Methods
3.5.1.3 Statistical & Ranking Filter Methods
3.5.1.4 Advantages and Disadvantages of Filter Method
3.5.2 Wrapper Method
3.5.2.1 Advantages and Disadvantages of Wrapper Method
3.5.2.2 Difference Between Filter Method and Wrapper Method
3.6 Methodology
3.6.1 Steps Performed
3.6.2 Flowchart
3.7 Experimental Results and Analysis
3.7.1 Task 1—Application of Four Machine Learning Models
3.7.2 Task 2—Applying Ensemble Learning Algorithms
3.7.3 Task 3—Applying Feature Selection Techniques
3.7.4 Task 4—Appling Data Balancing Technique
3.8 Conclusion
References
4. Healthcare 4.0: An Insight of Architecture, Security Requirements, Pillars and Applications
4.1 Introduction
4.2 Basic Architecture and Components of e-Health Architecture
4.2.1 Front End Layer
4.2.2 Communication Layer
4.2.3 Back End Layer
4.3 Security Requirements in Healthcare 4.0
4.3.1 Mutual-Authentications
4.3.2 Anonymity
4.3.3 Un-Traceability
4.3.4 Perfect—Forward—Secrecy
4.3.5 Attack Resistance
4.3.5.1 Replay Attack
4.3.5.2 Spoofing Attack
4.3.5.3 Modification Attack
4.3.5.4 MITM Attack
4.3.5.5 Impersonation Attack
4.4 ICT Pillar’s Associated With HC4.0
4.4.1 IoT in Healthcare 4.0
4.4.2 Cloud Computing (CC) in Healthcare 4.0
4.4.3 Fog Computing (FC) in Healthcare 4.0
4.4.4 BigData (BD) in Healthcare 4.0
4.4.5 Machine Learning (ML) in Healthcare 4.0
4.4.6 Blockchain (BC) in Healthcare 4.0
4.5 Healthcare 4.0’s Applications-Scenarios
4.5.1 Monitor-Physical and Pathological Related Signals
4.5.2 Self-Management, and Wellbeing Monitor, and its Precaution
4.5.3 Medication Consumption Monitoring and Smart-Pharmaceutics
4.5.4 Personalized (or Customized) Healthcare
4.5.5 Cloud-Related Medical Information’s Systems
4.5.6 Rehabilitation
4.6 Conclusion
References
5. Improved Social Media Data Mining for Analyzing Medical Trends
5.1 Introduction. 5.1.1 Data Mining
5.1.2 Major Components of Data Mining
5.1.3 Social Media Mining
5.1.4 Clustering in Data Mining
5.2 Literature Survey
5.3 Basic Data Mining Clustering Technique
5.3.1 Classifier and Their Algorithms in Data Mining
5.4 Research Methodology
5.5 Results and Discussion. 5.5.1 Tool Description
5.5.2 Implementation Results
5.5.3 Comparison Graphs Performance Comparison
5.6 Conclusion & Future Scope
References
6. Bioinformatics: An Important Tool in Oncology
6.1 Introduction
6.2 Cancer—A Brief Introduction
6.2.1 Types of Cancer
6.2.2 Development of Cancer
6.2.3 Properties of Cancer Cells
6.2.4 Causes of Cancer
6.3 Bioinformatics—A Brief Introduction
6.4 Bioinformatics—A Boon for Cancer Research
6.5 Applications of Bioinformatics Approaches in Cancer
6.5.1 Biomarkers: A Paramount Tool for Cancer Research
6.5.2 Comparative Genomic Hybridization for Cancer Research
6.5.3 Next-Generation Sequencing
6.5.4 miRNA
6.5.5 Microarray Technology
6.5.6 Proteomics-Based Bioinformatics Techniques
6.5.7 Expressed Sequence Tags (EST) and Serial Analysis of Gene Expression (SAGE)
6.6 Bioinformatics: A New Hope for Cancer Therapeutics
6.7 Conclusion
References
7. Biomedical Big Data Analytics Using IoT in Health Informatics
7.1 Introduction
7.2 Biomedical Big Data
7.2.1 Big EHR Data
7.2.2 Medical Imaging Data
7.2.3 Clinical Text Mining Data
7.2.4 Big OMICs Data
7.3 Healthcare Internet of Things (IoT)
7.3.1 IoT Architecture
7.3.2 IoT Data Source
7.3.2.1 IoT Hardware
7.3.2.2 IoT Middleware
7.3.2.3 IoT Presentation
7.3.2.4 IoT Software
7.3.2.5 IoT Protocols
7.4 Studies Related to Big Data Analytics in Healthcare IoT
7.5 Challenges for Medical IoT & Big Data in Healthcare
7.6 Conclusion
References
8. Statistical Image Analysis of Drying Bovine Serum Albumin Droplets in Phosphate Buffered Saline
8.1 Introduction
8.2 Experimental Methods
8.3 Results. 8.3.1 Temporal Study of the Drying Droplets
8.3.2 FOS Characterization of the Drying Evolution
8.3.3 GLCM Characterization of the Drying Evolution
8.4 Discussions. 8.4.1 Qualitative Analysis of the Drying Droplets and the Dried Films
8.4.2 Quantitative Analysis of the Drying Droplets and the Dried Films
8.5 Conclusions
Acknowledgments
References
9. Introduction to Deep Learning in Health Informatics
9.1 Introduction
9.1.1 Machine Learning v/s Deep Learning
9.1.2 Neural Networks and Deep Learning
9.1.3 Deep Learning Architecture
9.1.3.1 Deep Neural Networks
9.1.3.2 Convolutional Neural Networks
9.1.3.3 Deep Belief Networks
9.1.3.4 Recurrent Neural Networks
9.1.3.5 Deep Auto-Encoder
9.1.4 Applications
9.2 Deep Learning in Health Informatics. 9.2.1 Medical Imaging
9.2.1.1 CNN v/s Medical Imaging
9.2.1.2 Tissue Classification
9.2.1.3 Cell Clustering
9.2.1.4 Tumor Detection
9.2.1.5 Brain Tissue Classification
9.2.1.6 Organ Segmentation
9.2.1.7 Alzheimer’s and Other NDD Diagnosis
9.3 Medical Informatics
9.3.1 Data Mining
9.3.2 Prediction of Disease
9.3.3 Human Behavior Monitoring
9.4 Bioinformatics
9.4.1 Cancer Diagnosis
9.4.2 Gene Variants
9.4.3 Gene Classification or Gene Selection
9.4.4 Compound–Protein Interaction
9.4.5 DNA–RNA Sequences
9.4.6 Drug Designing
9.5 Pervasive Sensing
9.5.1 Human Activity Monitoring
9.5.2 Anomaly Detection
9.5.3 Biological Parameter Monitoring
9.5.4 Hand Gesture Recognition
9.5.5 Sign Language Recognition
9.5.6 Food Intake
9.5.7 Energy Expenditure
9.5.8 Obstacle Detection
9.6 Public Health
9.6.1 Lifestyle Diseases
9.6.2 Predicting Demographic Information
9.6.3 Air Pollutant Prediction
9.6.4 Infectious Disease Epidemics
9.7 Deep Learning Limitations and Challenges in Health Informatics
References
10. Data Mining Techniques and Algorithms in Psychiatric Health: A Systematic Review
10.1 Introduction
10.2 Techniques and Algorithms Applied
10.3 Analysis of Major Health Disorders Through Different Techniques. 10.3.1 Alzheimer
10.3.2 Dementia
10.3.3 Depression
10.3.4 Schizophrenia and Bipolar Disorders
10.4 Conclusion
References
11. Deep Learning Applications in Medical Image Analysis
11.1 Introduction
11.1.1 Medical Imaging
11.1.2 Artificial Intelligence and Deep Learning
11.1.3 Processing in Medical Images
11.2 Deep Learning Models and its Classification
11.2.1 Supervised Learning
11.2.1.1 RNN (Recurrent Neural Network)
11.2.2 Unsupervised Learning
11.2.2.1 Stacked Auto Encoder (SAE)
11.2.2.2 Deep Belief Network (DBN)
11.2.2.3 Deep Boltzmann Machine (DBM)
11.2.2.4 Generative Adversarial Network (GAN)
11.3 Convolutional Neural Networks (CNN)—A Popular Supervised Deep Model
11.3.1 Architecture of CNN
11.3.2 Learning of CNNs
11.3.3 Medical Image Denoising using CNNs
11.3.4 Medical Image Classification Using CNN
11.4 Deep Learning Advancements—A Biological Overview
11.4.1 Sub-Cellular Level
11.4.2 Cellular Level
11.4.3 Tissue Level
11.4.4 Organ Level
11.4.4.1 The Brain and Neural System
11.4.4.2 Sensory Organs—The Eye and Ear
11.4.4.3 Thoracic Cavity
11.4.4.4 Abdomen and Gastrointestinal (GI) Track
11.4.4.5 Other Miscellaneous Applications
11.5 Conclusion and Discussion
References
12. Role of Medical Image Analysis in Oncology
12.1 Introduction
12.2 Cancer
12.2.1 Types of Cancer
12.2.2 Causes of Cancer
12.2.3 Stages of Cancer
12.2.4 Prognosis
12.3 Medical Imaging
12.3.1 Anatomical Imaging
12.3.2 Functional Imaging
12.3.3 Molecular Imaging
12.4 Diagnostic Approaches for Cancer
12.4.1 Conventional Approaches
12.4.1.1 Laboratory Diagnostic Techniques
12.4.1.2 Tumor Biopsies
12.4.1.3 Endoscopic Exams
12.4.2 Modern Approaches
12.4.2.1 Image Processing
12.4.2.2 Implications of Advanced Techniques
12.4.2.3 Imaging Techniques
12.5 Conclusion
References
13. A Comparative Analysis of Classifiers Using Particle Swarm Optimization-Based Feature Selection
13.1 Introduction
13.2 Feature Selection for Classification. 13.2.1 An Overview: Data Mining
13.2.2 Classification Prediction
13.2.3 Dimensionality Reduction
13.2.4 Techniques of Feature Selection
13.2.5 Feature Selection: A Survey
13.2.6 Summary
13.3 Use of WEKA Tool. 13.3.1 WEKA Tool
13.3.2 Classifier Selection
13.3.3 Feature Selection Algorithms in WEKA
13.3.4 Performance Measure
13.3.5 Dataset Description
13.3.6 Experiment Design
13.3.7 Results Analysis
13.3.8 Summary
13.4 Conclusion and Future Work. 13.4.1 Summary of the Work
13.4.2 Research Challenges
13.4.3 Future Work
References
Index
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