Healthcare 4.0: Health Informatics and Precision Data Management

Description

The main aim of Healthcare 4.0: Health Informatics and Precision Data Management is to improve the services given by the healthcare industry and to bring meaningful patient outcomes, Informatics involved by applying the data, information and knowledge in the healthcare domain. Features: Improving the quality of health data of a patient A wide range of opportunities and renewed possibilities for healthcare systems Gives a way for carefully and meticulously tracking the provenance of medical records Accelerating the process of disease oriented data and medical data arbitration To bring the meaningful patient health outcomes To eradicate the delayed clinical communications To help the research intellectuals to step down further towards the disease and clinical data storage. Creating more patient-centered services The precise focus of this handbook will be on the potential applications and use of data informatics in area of healthcare, including clinical trials, tailored ailment data, patient and ailment record characterization and health records management. Cover Half Title Title Page Copyright Page Table of Contents Preface Editor Biography List of Contributors Chapter 1: Privacy-preserving Healthcare Informatics using Federated Learning and Blockchain 1.1 Introduction 1.2 Electronic Health Record Management and Informatics 1.2.1 Information Retrieval System for Healthcare Informatics 1.2.2 Privacy and Security in E-health Records 1.2.3 Healthcare Informatics and Bigdata 1.2.4 Enabling Technologies – Fog Computing, Blockchain, Internet of Things 1.3 Federated Learning 1.3.1 Massive Non-identically Independent Distribution 1.3.2 Horizontal Federated Learning 1.3.3 Vertical Federated Learning 1.3.4 Federated Transfer Learning in Healthcare Informatics 1.3.5 Privacy Preservation in Federated Learning 1.3.6 Statistical Challenges of Federated Learning 1.4 Blockchain in Healthcare Informatics 1.4.1 Blockchain Distributed Ledger for Healthcare Informatics 1.4.2 Healthcare Framework with Blockchain 1.4.3 Key Challenges of Blockchain in Healthcare Systems 1.4.4 Future Healthcare with Security 1.5 Differential Privacy Preservation in Healthcare Data 1.5.1 Deep Learning Models for Privacy Preservation 1.5.2 Adaptive Laplace Mechanism 1.5.3 Local Differential Privacy Preservation 1.5.4 Differential Privacy in Blockchain 1.6 Improved Federated Leaning–fusion Learning 1.7 Visualizing Knowledge Structure in Healthcare Informatics 1.8 Conclusion References Chapter 2: Applications, Opportunities, and Current Challenges in the Healthcare Industry 2.1 Healthcare Introduction 2.2 Healthcare Data 2.3 Healthcare Research Issues 2.4 Healthcare Blockchain Systems 2.5 Healthcare Analytics 2.5.1 Types of Analytics 2.6 Healthcare Applications 2.6.1 Applications for Patients 2.6.2 Applications for Healthcare Professionals 2.6.3 Applications for Resource Management 2.7 Security and Privacy 2.8 Conclusion References Chapter 3: Harnessing Big Data and Artificial Intelligence for Data Acquisition, Storage, and Retrieval of Healthcare Informatics in Precision Medicine 3.1 Introduction 3.2 Biomedical Informatics and Precision Medicine: Healthcare 4.0 3.3 Data Acquisition system 3.3.1 Protocols of Data Acquisition System 3.3.1.1 Advanced Message Queuing Protocol 3.3.1.2 Java Message Service 3.3.2 Open Source Data Acquisition Frameworks 3.3.2.1 Storm 3.3.2.2 Simply Scalable Streaming System (S4) 3.3.2.3 Kafka 3.3.2.4 Flume 3.3.2.5 Hadoop 3.3.2.6 Flink 3.4 Storage of Data 3.4.1 Bigdata in Biomedical Informatics 3.4.2 Intelligent Medical Big Data System with Hadoop and Blockchain 3.4.2.1 Hadoop Architecture 3.4.2.2 Hadoop Distributed File System 3.4.2.3 Apache HBase 3.4.3 Cloud Computing in Healthcare 3.4.4 IoT Applications for Healthcare 3.4.5 Integration of Cloud and IoT 3.4.6 Enabling Security for Cloud data 3.4.6.1 Blockchain 3.4.6.2 Integration of Cloud and Blockchain 3.4.6.3 Integration of Cloud and Blockchain in Healthcare 3.4.6.4 Fog Computing in Health Sector 3.5 Information Retrieval 3.5.1 Query Expansion 3.5.2 Content-Based Medical Visual Information Retrieval 3.5.3 Fusion Technique in Biomedical Information Retrieval 3.5.4 Tag-Based Information Retrieval 3.5.5 Biomedical Word Embedding 3.6 Discussion and Future Directions 3.7 Conclusion References Chapter 4: Analogous Healthcare Product Identification in Online Shopping 4.1 Introduction 4.2 Content-Based Image Recovery 4.3 Text-Based Image Recovery 4.4 Retrieval by Colour 4.5 Existing Method 4.5.1 Drawback 4.6 The Proposed Method 4.7 Conclusion References Chapter 5: Segmentation-based Comparative Analysis for Detection of Bone Tumour Using Healthcare Data 5.1 Introduction 5.2 Literature Survey 5.2.1 Image Capture and Display 5.2.2 The Biological Significance of Bone 5.2.3 Conversion of a Picture into a Greyscale Level 5.2.4 Preparation of the Material 5.3 Main Contributions 5.3.1 Outline of the Paper 5.4 Segmentation 5.5 Classification 5.5.1 Min/Max Algorithm For Graph Cuts 5.6 Existing System 5.7 Proposed System 5.8 Result and Observations 5.9 Conclusion References Chapter 6: Challenges, Progress and Opportunities of Blockchain in Healthcare Data 6.1 Different Ways of Managing Healthcare Data 6.1.1 Patient Maintains Their Records 6.1.2 Hospitals Maintain Patient Records on Local Servers 6.1.3 Hospitals Maintain Patient Records on Centralized Servers 6.1.4 Hospitals Maintain Patient Records in the Cloud 6.1.5 Drawbacks in Current Technology 6.2 Introduction of Blockchain 6.2.1 Secure Hash Algorithm in Blockchain 6.3 Application of Blockchain 6.4 Need for Blockchain in Healthcare 6.4.1 Advantages of Distributed Storage in Blockchain 6.5 Managing Patient Health Data in Blockchain 6.6 Working on Blockchain in Health Data 6.7 Literature Review 6.8 Challenges of Blockchain in Health Data 6.8.1 Technical Challenges 6.8.2 Organizational Challenges 6.8.3 Government Policy Challenges 6.9 Opportunities for Blockchain in Health Data References Chapter 7: SepSense: A Novel Sepsis Detection System Using Machine Learning Techniques 7.1 Introduction 7.2 Related Works 7.3 Proposed System 7.3.1 Data Preprocessing 7.3.2 Classification 7.3.3 Model Selection 7.4 Implementation and Results 7.5 Conclusion References Chapter 8: Oral Cancer Detection at Early Stage Using Convolutional Neural Network in Healthcare Informatics 8.1 Introduction 8.2 Literature Survey 8.3 Existing System 8.4 Proposed Method 8.4.1 Image Processing System 8.4.1.1 Digitizer 8.4.1.2 Image Processor 8.4.1.3 Image Processing Fundamental 8.5 Methodology 8.5.1 Input Image and Pre-processing 8.5.2 Resizing Images and Changing the Colour Space 8.5.3 Gaussian Filtering and Gamma Correction 8.5.4 Segmentation 8.5.5 Colour Space Conversions 8.6 CNN 8.6.1 Operation 8.7 Image Feature Detector 8.8 Trainee 8.9 Results and Discussion 8.9.1 Performance Measures 8.9.2 Image Classification Result 8.9.3 Object Detection Result 8.10 Conclusion References Chapter 9: Lung Diseases Identification 9.1 Introduction 9.1.1 Squamous Cell Lung Cancer in the Early Stages 9.1.2 Advanced Small Cell Lung Cancer in the Extensive Stage 9.1.3 Difficulties in Determining the Nature of Lung Cancer Cells 9.2 Literature Survey 9.3 Proposed Method 9.3.1 Input Image 9.3.2 Preprocessing 9.3.3 Segmentation 9.3.4 Feature Extraction 9.3.5 Classification 9.4 Result and Output 9.5 Conclusion References Chapter 10: Brain–Computer Interface-based Real-Time Movement of Upper Limb Prostheses 10.1 Introduction 10.1.1 Motor Imagery Signal Decoding 10.2 Literature Survey 10.3 Methodology of the Proposed Work 10.3.1 Proposed Control Scheme 10.3.2 One Versus All Adaptive Neural Type-2 Fuzzy Inference System (OVA-ANT2FIS) 10.3.3 Position Control of Robot Arm Using Hybrid BCI for Rehabilitation Purpose 10.3.4 Jaco Robot Arm 10.3.5 Scheme 1: Random Order Positional Control 10.4 Experiments and Data Processing 10.4.1 Feature Extraction 10.4.2 Performance Analysis of the Detectors 10.4.2.1 Scheme 1 10.4.3 Performance of the Real-Time Robot Arm Controllers 10.5 Discussion 10.6 Future Research Directions References Chapter 11: A Robust Image-Driven CNN Algorithm to Detect Skin Disease in Healthcare Systems 11.1 Introduction 11.2 Related Work 11.3 Materials and Methods 11.3.1 Local Ternary Pattern 11.3.2 Splitting LTP into Two LBP Channels 11.3.3 Gray-Level Co-Occurrence Matrix 11.3.4 Morphological Process 11.3.4.1 Back Propagation Networks 11.3.4.2 Back Propagation Algorithm 11.3.4.3 Steps of the Algorithm 11.3.4.4 Removing Unnecessary Neurons 11.3.4.5 Contour Detection 11.3.5 Threshold Segmentation 11.4 Results and Discussions 11.5 Conclusion References Chapter 12: Patient Identity Ailments and Maintenance Using Blockchain and Health Informatics 12.1 Introduction: Background and Research Motivation 12.1.1 Background and Research Motivation 12.2 Hyperledger Fabric 12.3 Proposed Patient Identity Hyperledger Architecture 12.4 Proposed Architecture Implementation 12.4.1 Registering the Users 12.4.2 Adding EHR Record to the Blockchain 12.4.3 Self-Sovereign Identity for the Patients 12.4.4 Retrieving the Record 12.5 Security Analysis Using Use Case Scenarios 12.5.1 Use Case 1: Enhanced Security 12.5.2 Use Case 2: Efficient Health Records Storage 12.5.3 Use Case 3: Improved Data Privacy 12.5.4 Use Case 4: Better Data Scalability 12.6 Performance Analysis and Discussion 12.7 Conclusion References Chapter 13: An Innovative Outcome of Internet of Things and Artificial Intelligence in Remote Centered Healthcare Application Schemes 13.1 Introduction 13.2 Machine Learning Applications 13.3 Related Works 13.4 Background 13.5 Collection and Transmission of Information 13.6 Record Establishment to Do Disease Diagnosis 13.7 System Architecture 13.8 Cloudlet Processing 13.8.1 Cloud Computing in Healthcare 13.8.1.1 Cost Reduction 13.8.1.2 Facilitating the Interaction 13.8.1.3 Access to Performance Analysis 13.9 Proposed Methodology 13.9.1 System Modules 13.9.1.1 Healthcare Monitoring Section 13.9.1.2 Emergency Alert and Notification Section 13.9.1.3 Health Status Predictor 13.10 Implementation 13.10.1 Raspberry Pi 13.10.2 Temperature Sensor 13.10.3 Heartbeat Sensor 13.10.4 Vibration Sensor 13.10.5 BP Sensor 13.10.6 Analog-to-Digital Converter 13.10.7 Global System for Mobile Communications Module 13.10.8 Camera Specifications 13.11 ML for Health Care: The Challenges 13.11.1 The Safety Challenge 13.11.2 Personal Obstacles to Overcome 13.11.3 Ethical Issues to Consider 13.12 Conclusion 13.13 Future Work References Chapter 14: Electronic Health Records Storing and Sharing System Using Blockchain 14.1 Introduction 14.1.1 Blockchain 14.1.2 Types of Blockchain 14.1.3 Smart Contract 14.1.4 Ganache 14.2 Proposed System 14.3 Methodology 14.4 Results 14.5 Conclusion References Index