Handbook on Intelligent Healthcare Analytics: Knowledge Engineering with Big Data
- Length: 448 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2022-06-08
- ISBN-10: 1119791790
- ISBN-13: 9781119791799
- Sales Rank: #0 (See Top 100 Books)
HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS
The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners.
The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information.
A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare.
In addition, the reader will find in this Handbook:
- Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning;
- An exploration of predictive analytics in healthcare;
- The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics.
Audience
Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.
Cover Half-Title Page Series Page Title Page Copyright Page Contents Preface 1 An Introduction to Knowledge Engineering and Data Analytics 1.1 Introduction 1.1.1 Online Learning and Fragmented Learning Modeling 1.2 Knowledge and Knowledge Engineering 1.2.1 Knowledge 1.2.2 Knowledge Engineering 1.3 Knowledge Engineering as a Modelling Process 1.4 Tools 1.5 What are KBSs? 1.5.1 What is KBE? 1.5.2 When Can KBE Be Used? 1.5.3 CAD or KBE? 1.6 Guided Random Search and Network Techniques 1.6.1 Guide Random Search Techniques 1.7 Genetic Algorithms 1.7.1 Design Point Data Structure 1.7.2 Fitness Function 1.7.3 Constraints 1.7.4 Hybrid Algorithms 1.7.5 Considerations When Using a GA 1.7.6 Alternative to Genetic-Inspired Creation of Children 1.7.7 Alternatives to GA 1.7.8 Closing Remarks for GA 1.8 Artificial Neural Networks 1.9 Conclusion References 2 A Framework for Big Data Knowledge Engineering 2.1 Introduction 2.1.1 Knowledge Engineering in AI and Its Techniques 2.1.1.1 Supervised Model 2.1.1.2 Unsupervised Model 2.1.1.3 Deep Learning 2.1.1.4 Deep Reinforcement Learning 2.1.1.5 Optimization 2.1.2 Disaster Management 2.2 Big Data in Knowledge Engineering 2.2.1 Cognitive Tasks for Time Series Sequential Data 2.2.2 Neural Network for Analyzing the Weather Forecasting 2.2.3 Improved Bayesian Hidden Markov Frameworks 2.3 Proposed System 2.4 Results and Discussion 2.5 Conclusion References 3 Big Data Knowledge System in Healthcare 3.1 Introduction 3.2 Overview of Big Data 3.2.1 Big Data: Definition 3.2.2 Big Data: Characteristics 3.3 Big Data Tools and Techniques 3.3.1 Big Data Value Chain 3.3.2 Big Data Tools and Techniques 3.4 Big Data Knowledge System in Healthcare 3.4.1 Sources of Medical Big Data 3.4.2 Knowledge in Healthcare 3.4.3 Big Data Knowledge Management Systems in Healthcare 3.4.4 Big Data Analytics in Healthcare 3.5 Big Data Applications in the Healthcare Sector 3.5.1 Real Time Healthcare Monitoring and Altering 3.5.2 Early Disease Prediction with Big Data 3.5.3 Patients Predictions for Improved Staffing 3.5.4 Medical Imaging 3.6 Challenges with Healthcare Big Data 3.6.1 Challenges of Big Data 3.6.2 Challenges of Healthcare Big Data 3.7 Conclusion References 4 Big Data for Personalized Healthcare 4.1 Introduction 4.1.1 Objectives 4.1.2 Motivation 4.1.3 Domain Description 4.1.4 Organization of the Chapter 4.2 Related Literature 4.2.1 Healthcare Cyber Physical System Architecture 4.2.2 Healthcare Cloud Architecture 4.2.3 User Authentication Management 4.2.4 Healthcare as a Service (HaaS) 4.2.5 Reporting Services 4.2.6 Chart and Trend Analysis 4.2.7 Medical Data Analysis 4.2.8 Hospital Platform Based On Cloud Computing 4.2.9 Patient’s Data Collection 4.2.10 H-Cloud Challenges 4.2.11 Healthcare Information System and Cost 4.3 System Analysis and Design 4.3.1 Proposed Solution 4.3.2 Software Components 4.3.3 System Design 4.3.4 Architecture Diagram 4.3.5 List of Modules 4.3.6 Use Case Diagram 4.3.7 Sequence Diagram 4.3.8 Class Diagram 4.4 System Implementation 4.4.1 User Interface 4.4.2 Storage Module 4.4.3 Notification Module 4.4.4 Middleware 4.4.5 OTP Module 4.5 Results and Discussion 4.6 Conclusion References 5 Knowledge Engineering for AI in Healthcare 5.1 Introduction 5.2 Overview 5.2.1 Knowledge Representation 5.2.2 Types of Knowledge in Artificial Intelligence 5.2.3 Relation Between Knowledge and Intelligence 5.2.4 Approaches to Knowledge Representation 5.2.5 Requirements for Knowledge Representation System 5.2.6 Techniques of Knowledge Representation 5.2.6.1 Logical Representation 5.2.6.2 Semantic Network Representation 5.2.6.3 Frame Representation 5.2.6.4 Production Rules 5.2.7 Process of Knowledge Engineering 5.2.8 Knowledge Discovery Process 5.3 Applications of Knowledge Engineering in AI for Healthcare 5.3.1 AI Supports in Clinical Decisions 5.3.2 AI-Assisted Robotic Surgery 5.3.3 Enhance Primary Care and Triage 5.3.4 Clinical Judgments or Diagnosis 5.3.5 Precision Medicine 5.3.6 Drug Discovery 5.3.7 Deep Learning to Diagnose Diseases 5.3.8 Automating Administrative Tasks 5.3.9 Reducing Operational Costs 5.3.10 Virtual Nursing Assistants 5.4 Conclusion References 6 Business Intelligence and Analytics from Big Data to Healthcare 6.1 Introduction 6.1.1 Impact of Healthcare Industry on Economy 6.1.2 Coronavirus Impact on the Healthcare Industry 6.1.3 Objective of the Study 6.1.4 Limitations of the Study 6.2 Related Works 6.3 Conceptual Healthcare Stock Prediction System 6.3.1 Data Source 6.3.2 Business Intelligence and Analytics Framework 6.3.2.1 Simple Machine Learning Model 6.3.2.2 Time Series Forecasting 6.3.2.3 Complex Deep Neural Network 6.3.3 Predicting the Stock Price 6.4 Implementation and Result Discussion 6.4.1 Apollo Hospitals Enterprise Limited 6.4.2 Cadila Healthcare Ltd 6.4.3 Dr. Reddy’s Laboratories 6.4.4 Fortis Healthcare Limited 6.4.5 Max Healthcare Institute Limited 6.4.6 Opto Circuits Limited 6.4.7 Panacea Biotec 6.4.8 Poly Medicure Ltd 6.4.9 Thyrocare Technologies Limited 6.4.10 Zydus Wellness Ltd 6.5 Comparisons of Healthcare Stock Prediction Framework 6.6 Conclusion and Future Enhancement References Books Web Citation 7 Internet of Things and Big Data Analytics for Smart Healthcare 7.1 Introduction 7.2 Literature Survey 7.3 Smart Healthcare Using Internet of Things and Big Data Analytics 7.3.1 Smart Diabetes Prediction 7.3.2 Smart ADHD Prediction 7.4 Security for Internet of Things 7.4.1 K(Binary) ECC FSM 7.4.2 NAF Method 7.4.3 K-NAF Multiplication Architecture 7.4.4 K(NAF) ECC FSM 7.5 Conclusion References 8 Knowledge-Driven and Intelligent Computing in Healthcare 8.1 Introduction 8.1.1 Basics of Health Recommendation System 8.1.2 Basics of Ontology 8.1.3 Need of Ontology in Health Recommendation System 8.2 Literature Review 8.2.1 Ontology in Various Domain 8.2.2 Ontology in Health Recommendation System 8.3 Framework for Health Recommendation System 8.3.1 Domain Ontology Creation 8.3.2 Query Pre-Processing 8.3.3 Feature Selection 8.3.4 Recommendation System 8.4 Experimental Results 8.5 Conclusion and Future Perspective References 9 Secure Healthcare Systems Based on Big Data Analytics 9.1 Introduction 9.2 Healthcare Data 9.2.1 Structured Data 9.2.2 Unstructured Data 9.2.3 Semi-Structured Data 9.2.4 Genomic Data 9.2.5 Patient Behavior and Sentiment Data 9.2.6 Clinical Data and Clinical Notes 9.2.7 Clinical Reference and Health Publication Data 9.2.8 Administrative and External Data 9.3 Recent Works in Big Data Analytics in Healthcare Data 9.4 Healthcare Big Data 9.5 Privacy of Healthcare Big Data 9.6 Privacy Right by Country and Organization 9.7 How Blockchain is Big Data Usable for Healthcare 9.7.1 Digital Trust 9.7.2 Smart Data Tracking 9.7.3 Ecosystem Sensible 9.7.4 Switch Digital 9.7.5 Cybersecurity 9.7.6 Sharing Interoperability and Data 9.7.7 Improving Research and Development (R&D) 9.7.8 Drugs Fighting Counterfeit 9.7.9 Patient Mutual Participation 9.7.10 Internet Access by Patient to Longitudinal Data 9.7.11 Data Storage into Off Related to Confidentiality and Data Scale 9.8 Blockchain Threats and Medical Strategies Big Data Technology 9.9 Conclusion and Future Research References 10 Predictive and Descriptive Analysis for Healthcare Data 10.1 Introduction 10.2 Motivation 10.2.1 Healthcare Analysis 10.2.2 Predictive Analytics 10.2.3 Predictive Analytics Current Trends 10.2.3.1 Importance of PA 10.2.4 Descriptive Analysis 10.2.4.1 Descriptive Statistics 10.2.4.2 Categories of Descriptive Analysis 10.2.5 Method of Modeling 10.2.6 Measures of Data Analytics 10.2.7 Healthcare Data Analytics Platforms and Tools 10.2.8 Challenges 10.2.9 Issues in Predictive Healthcare Analysis 10.2.9.1 Integrating Separate Data Sources 10.2.9.2 Advanced Cloud Technologies 10.2.9.3 Privacy and Security 10.2.9.4 The Fast Pace of Technology Changes 10.2.10 Applications of Predictive Analysis 10.2.10.1 Improving Operational Efficiency 10.2.10.2 Personal Medicine 10.2.10.3 Population Health and Risk Scoring 10.2.10.4 Outbreak Prediction 10.2.10.5 Controlling Patient Deterioration 10.2.10.6 Supply Chain Management 10.2.10.7 Potential in Precision Medicine 10.2.10.8 Cost Savings From Reducing Waste and Fraud 10.3 Conclusion References 11 Machine and Deep Learning Algorithms for Healthcare Applications 11.1 Introduction 11.2 Artificial Intelligence, Machine Learning, and Deep Learning 11.3 Machine Learning 11.3.1 Supervised Learning 11.3.2 Unsupervised Learning 11.3.3 Semi-Supervised 11.3.4 Reinforcement Learning 11.4 Advantages of Using Deep Learning on Top of Machine Learning 11.5 Deep Learning Architecture 11.6 Medical Image Analysis using Deep Learning 11.7 Deep Learning in Chest X-Ray Images 11.8 Machine Learning and Deep Learning in Content-Based Medical Image Retrieval 11.9 Image Retrieval Performance Metrics 11.10 Conclusion References 12 Artificial Intelligence in Healthcare Data Science with Knowledge Engineering 12.1 Introduction 12.2 Literature Review 12.3 AI in Healthcare 12.4 Data Science and Knowledge Engineering for COVID-19 12.5 Proposed Architecture and Its Implementation 12.5.1 Implementation 12.5.1.1 Data Collection 12.5.1.2 Understanding Class and Dependencies 12.5.1.3 Pre-Processing 12.5.1.4 Sampling 12.5.1.5 Model Fixing 12.5.1.6 Analysis of Real-Time Datasets 12.5.1.7 Machine Learning Algorithms 12.6 Conclusions and Future Work References 13 Knowledge Engineering Challenges in Smart Healthcare Data Analysis System 13.1 Introduction 13.1.1 Motivation 13.2 Ongoing Research on Intelligent Decision Support System 13.3 Methodology and Architecture of the Intelligent Rule-Based System 13.3.1 Proposed System Design 13.3.2 Algorithms Used 13.3.2.1 Forward Chaining 13.3.2.2 Backward Chaining 13.4 Creating a Rule-Based System using Prolog 13.5 Results and Discussions 13.6 Conclusion 13.7 Acknowledgments References 14 Big Data in Healthcare: Management, Analysis, and Future Prospects 14.1 Introduction 14.2 Breast Cancer: Overview 14.3 State-of-the-Art Technology in Treatment of Cancer 14.3.1 Chemotherapy 14.3.2 Radiotherapy 14.4 Early Diagnosis of Breast Cancer: Overview 14.4.1 Advantages and Risks Associated with the Early Detection of Breast Cancer 14.4.2 Diagnosis the Breast Cancer 14.5 Literature Review 14.6 Machine Learning Algorithms 14.6.1 Principal Component Analysis Algorithms 14.6.2 K-Means Algorithm 14.6.3 K-Nearest Neighbor Algorithm 14.6.4 Logistic Regression Algorithm 14.6.5 Support Vector Machine Algorithm 14.6.6 AdaBoost Algorithm 14.6.7 Neural Networks Algorithm 14.6.8 Random Forest Algorithm 14.7 Result and Discussion 14.7.1 Performance Metrics 14.7.1.1 ROC Curve 14.7.1.2 Accuracy 14.7.1.3 Precision and Recall 14.7.1.4 F1-Score 14.8 Experimental Result and Discussion 14.9 Conclusion References 15 Machine Learning for Information Extraction, Data Analysis and Predictions in the Healthcare System 15.1 Introduction 15.2 Machine Learning in Healthcare 15.3 Types of Learnings in Machine Learning 15.3.1 Supervised Learning 15.3.2 Unsupervised Algorithms 15.3.3 Semi-Supervised Learning 15.3.4 Reinforcement Learning 15.4 Types of Machine Learning Algorithms 15.4.1 Classification 15.4.2 Bayes Classification 15.4.3 Association Analysis 15.4.4 Correlation Analysis 15.4.5 Cluster Analysis 15.4.6 Outlier Analysis 15.4.7 Regression Analysis 15.4.8 K-Means 15.4.9 Apriori Algorithm 15.4.10 K Nearest Neighbor 15.4.11 Naive Bayes 15.4.12 AdaBoost 15.4.13 Support Vector Machine 15.4.14 Classification and Regression Trees 15.4.15 Linear Discriminant Analysis 15.4.16 Logistic Regression 15.4.17 Linear Regression 15.4.18 Principal Component Analysis 15.5 Machine Learning for Information Extraction 15.5.1 Natural Language Processing 15.6 Predictive Analysis in Healthcare 15.7 Conclusion References 16 Knowledge Fusion Patterns in Healthcare 16.1 Introduction 16.2 Related Work 16.3 Materials and Methods 16.3.1 Classification of Data Fusion 16.3.2 Levels and Its Working in Healthcare Ecosystems 16.3.2.1 Initial Level Data Access (ILA) 16.3.2.2 Middle Level Access (MLA) 16.3.2.3 High Level Access (HLA) 16.4 Proposed System 16.4.1 Objective 16.4.2 Sample Dataset 16.5 Results and Discussion 16.6 Conclusion and Future Work References 17 Commercial Platforms for Healthcare Analytics: Health Issues for Patients with Sickle Cells 17.1 Introduction 17.2 Materials and Methods 17.2.1 Data Acquisition and Pre-Processing 17.2.2 Sickle Cells Normalization Image 17.2.3 Gradient Calculation 17.2.4 Gradient Descent Step 17.2.5 Insight to Previous Methods Adopted in Convolutional Neural Networks 17.2.6 Segments of Convolutional Neural Networks 17.2.6.1 Convolutional Layer 17.2.6.2 Pooling Layer 17.2.6.3 Fully Connected Layer 17.2.6.4 Softmax Layer 17.2.7 Basic Transformations of Convolutional Neural Networks in Healthcare 17.2.8 Algorithm Review and Comparison 17.2.9 Feedforward 17.3 Results and Discussion 17.3.1 Results on Suitability for Applications in Healthcare 17.3.2 Class Prediction 17.3.3 The Model Sanity Checking 17.3.4 Analysis of the Epoch and Training Losses 17.3.5 Discussion and Healthcare Interpretations 17.3.6 Load Data 17.3.7 Image Pre-Processing 17.3.8 Building and Training the Classifier 17.3.9 Saving the Checkpoint Suitable for Healthcare 17.3.10 Loading the Checkpoint 17.4 Conclusion References 18 New Trends and Applications of Big Data Analytics for Medical Science and Healthcare 18.1 Introduction 18.2 Related Work 18.3 Convolutional Layer 18.4 Pooling Layer 18.5 Fully Connected Layer 18.6 Recurrent Neural Network 18.7 LSTM and GRU 18.8 Materials and Methods 18.8.1 Pre-Processing Strategy Selection 18.8.2 Feature Extraction and Classification 18.9 Results and Discussions 18.10 Conclusion 18.11 Acknowledgement References Index EULA
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