Librería Portfolio

SEMANTIC WEB FOR EFFECTIVE HEALTHCARE SYSTEMS

The book summarizes the trends and current research advances in web semantics, delineating the existing tools, techniques, methodologies, and research solutions

Semantic Web technologies have the opportunity to transform the way healthcare providers utilize technology to gain insights and knowledge from their data and make treatment decisions. Both Big Data and Semantic Web technologies can complement each other to address the challenges and add intelligence to healthcare management systems.

The aim of this book is to analyze the current status on how the semantic web is used to solve health data integration and interoperability problems, and how it provides advanced data linking capabilities that can improve search and retrieval of medical data. Chapters analyze the tools and approaches to semantic health data analysis and knowledge discovery. The book discusses the role of semantic technologies in extracting and transforming healthcare data before storing it in repositories. It also discusses different approaches for integrating heterogeneous healthcare data.

This innovative book offers:

The first of its kind and highlights only the ontology driven information retrieval mechanisms and techniques being applied to healthcare as well as clinical information systems;
Presents a comprehensive examination of the emerging research in areas of the semantic web;
Discusses studies on new research areas including ontological engineering, semantic annotation and semantic sentiment analysis;
Helps readers understand key concepts in semantic web applications for the biomedical engineering and healthcare fields;
Includes coverage of key application areas of the semantic web.

Audience: Researchers and graduate students in computer science, biomedical engineering, electronic and software engineering, as well as industry scientific researchers, clinicians, and systems managers in biomedical fields.




Table of contents

Preface xv

Acknowledgment xix

1 An Ontology-Based Contextual Data Modeling for Process Improvement in Healthcare 1
A. M. Abirami and A. Askarunisa

1.1 Introduction 1

1.1.1 Ontology-Based Information Extraction 3

1.1.2 Ontology-Based Knowledge Representation 4

1.2 Related Work 5

1.3 Motivation 8

1.4 Feature Extraction 9

1.4.1 Vector Space Model 10

1.4.2 Latent Semantic Indexing (LSI) 11

1.4.3 Clustering Techniques 12

1.4.4 Topic Modeling 12

1.5 Ontology Development 17

1.5.1 Ontology-Based Semantic Indexing (OnSI) Model 17

1.5.2 Ontology Development 18

1.5.3 OnSI Model Evaluation 19

1.5.4 Metrics Analysis 23

1.6 Dataset Description 24

1.7 Results and Discussions 25

1.7.1 Discussion 1 29

1.7.2 Discussion 2 29

1.7.3 Discussion 3 30

1.8 Applications 31

1.9 Conclusion 32

1.10 Future Work 33

References 33

2 Semantic Web for Effective Healthcare Systems: Impact and Challenges 39
Hemendra Shankar Sharma and Ashish Sharma

2.1 Introduction 40

2.2 Overview of the Website in Healthcare 45

2.2.1 What is Website? 45

2.2.2 Types of Website 45

2.2.2.1 Static Website 45

2.2.2.2 Dynamic Website 46

2.2.3 What is Semantic Web? 46

2.2.4 Role of Semantic Web 47

2.2.4.1 Pros and Cons of Semantic Web 49

2.2.4.2 Impact on Patient 51

2.2.4.3 Impact on Practitioner 52

2.2.4.4 Impact on Researchers 52

2.3 Data and Database 53

2.3.1 What is Data? 54

2.3.2 What is Database? 54

2.3.3 Source of Data in the Healthcare System 54

2.3.3.1 Electronic Health Record (EHR) 55

2.3.3.2 Biomedical Image Analysis 56

2.3.3.3 Sensor Data Analysis 57

2.3.3.4 Genomic Data Analysis 57

2.3.3.5 Clinical Text Mining 58

2.3.3.6 Social Media 59

2.3.4 Why Are Databases Important? 60

2.3.5 Challenges With the Database in the Healthcare System 61

2.4 Big Data and Database Security and Protection 61

2.4.1 What is Big Data 61

2.4.2 Five V's of Big Data 62

2.4.2.1 Volume 62

2.4.2.2 Variety 63

2.4.2.3 Velocity 63

2.4.2.4 Veracity 64

2.4.2.5 Value 65

2.4.3 Architectural Framework of Big Data 65

2.4.4 Data Protection Versus Data Security in Healthcare 67

2.4.4.1 Phishing Attacks 67

2.4.4.2 Malware and Ransomware 67

2.4.4.3 Cloud Threats 67

2.4.5 Technology in Use to Secure the Healthcare Data 68

2.4.5.1 Access Control Policy 69

2.4.6 Monitoring and Auditing 69

2.4.7 Standard for Data Protection 70

2.4.7.1 Healthcare Standard in India 70

2.4.7.2 Security Technical Standards 71

2.4.7.3 Administrative Safeguards Standards 71

2.4.7.4 Physical Safeguard Standards 71

References 71

3 Ontology-Based System for Patient Monitoring 75
R. Mervin, Tintu Thomas and A. Jaya

3.1 Introduction 76

3.1.1 Basics of Ontology 77

3.1.2 Need of Ontology in Patient Monitoring 78

3.2 Literature Review 78

3.2.1 Uses of Ontology in Various Domains 78

3.2.2 Ontology in Patient Monitoring System 80

3.3 Architectural Design 80

3.3.1 Phases of Patient Monitoring System 82

3.3.2 Reasoner in Patient Monitoring 87

3.4 Experimental Results 88

3.4.1 SPARQL Results 89

3.4.2 Comparison Between Other Systems 89

3.5 Conclusion and Future Enhancements 90

References 91

4 Semantic Web Solutions for Improvised Search in Healthcare Systems 95
Nidhi Malik, Aditi Sharan and Sadika Verma

4.1 Introduction 95

4.1.1 Key Benefits and Usage of Technology in Healthcare System 96

4.2 Background 97

4.2.1 Significance of Semantics in Healthcare Systems 97

4.2.2 Scope and Benefits of Semantics in Healthcare Systems 98

4.2.3 Issues in Incorporating Semantics 98

4.2.4 Existing Semantic Web Technologies 99

4.3 Searching Techniques in Healthcare Systems 100

4.3.1 Keyword-Based Search 100

4.3.2 Controlled Vocabularies Based Search 101

4.3.3 Improvising Searches With Semantic Web Solutions 101

4.3.4 Health Domain-Specific Resources for Semantic Search 102

4.3.4.1 Ontologies 103

4.3.4.2 Libraries 103

4.3.4.3 Search Engines 103

4.4 Emerging Technologies/Resources in Health Sector 108

4.4.1 Elasticsearch 109

4.4.2 BioBERT 109

4.4.3 Knowledge Graphs 110

4.5 Conclusion 110

References 111

5 Actionable Content Discovery for Healthcare 115
Ujwala Bharambe and Anuradha Srinivasaraghavan

5.1 Introduction 116

5.2 Actionable Content 117

5.2.1 Actionable Content in Theory 117

5.2.2 Actionable Content in Practice 122

5.3 Health Analytics 124

5.3.1 Artificial Intelligence/Machine Learning-Based Predictive Analytics 125

5.3.2 Semantic Technology for Prescriptive Health Analytics 126

5.4 Ontologies and Actionable Content 127

5.4.1 Ontologies in Healthcare Domain 129

5.5 General Architecture for the Discovery of Actionable Content for Healthcare Domain 130

5.5.1 Ontology-Driven Actionable Content Discovery in Healthcare Domain 131

5.5.2 Case Study for