Biomedical Data Mining for Information Retrieval: Methodologies, Techniques, and Applications
- Length: 448 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2021-08-24
- ISBN-10: 111971124X
- ISBN-13: 9781119711247
- Sales Rank: #0 (See Top 100 Books)
This book comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical Image Mining, a novel research area, due to its large amount of biomedical images increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients.
Cover Half-Title Page Series Page Title Page Copyright Page Contents Preface 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 3.1.2 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 3.5 Feature Selection Methods 3.5.1 Filter Method 3.5.2 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 4.4 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 7.4 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 9.1.4 Applications 9.2 Deep Learning in Health Informatics 9.2.1 Medical Imaging 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 11.2.2 Unsupervised Learning 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 11.5 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 12.4.2 Modern Approaches 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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