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SEMANTIC WEB FOR EFFECTIVE HEALTHCARE SYSTEMS
Título:
SEMANTIC WEB FOR EFFECTIVE HEALTHCARE SYSTEMS
Subtítulo:
Autor:
JAIN, V
Editorial:
JOHN WILEY
Año de edición:
2022
ISBN:
978-1-119-76229-4
Páginas:
2022
199,00 € -10,0% 179,10 €

 

Sinopsis

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 Actionable Content Discovery in Cancer Domain 134

5.6 Conclusion 136

References 136

6 Intelligent Agent System Using Medicine Ontology 139
Tintu Thomas and R. Mervin

6.1 Introduction to Semantic Search 140

6.1.1 What is an Ontology in Terms of Medicine? 140

6.1.2 Needs and Benefits of Ontology in Medical Search 141

6.2 Sematic Search 142

6.2.1 How NLP Works in Sematic Search? 142

6.2.2 Part of Speech Tagging and Chunking 142

6.2.3 Sentence Parsing 143

6.2.4 Discussion About the Various Semantic Search in Medical Databases 144

6.2.5 Discussion About the Retrieval Tools Used in Sematic Search in Medline 145

6.3 Structural Pattern of Semantic Search 146

6.3.1 Architectural Diagram 147

6.3.2 Agent Ontology 148

6.3.3 Rule-Based Approach 149

6.3.4 Reasoners-Based Approach 151

6.4 Implementation of Reasoners 152

6.5 Implementation and Results 153

6.6 Conclusion and Future Prospective 153

References 154

7 Ontology-Based System for Robotic Surgery-A Historical Analysis 159
Ajay Agarwal and Amit Kumar Mishra

7.1 Historical Discourse of Surgical Robots 160

7.2 The Necessity for Surgical Robots 162

7.3 Ontological Evolution of Robotic Surgical Procedures in Various Domains 163

7.4 Inferences Drawn From the Table 164

7.5 Transoral Robotic Surgery 166

7.6 Pancreatoduodenectomy 167

7.7 Robotic Mitral Valve Surgery 168

7.8 Rectal Tumor Surgery 170

7.9 Robotic Lung Cancer Surgery 170

7.10 Robotic Surgery in Gynecology 171

7.11 Robotic Radical Prostatectomy 171

7.12 Conclusion 172

7.13 Future Work 172

References 172

8 IoT-Enabled Effective Healthcare Monitoring System Using Semantic Web 175
Sapna Juneja, Abhinav Juneja, Annu Dhankhar and Vishal Jain

8.1 Introduction 176

8.2 Literature Review 177

8.3 Phases of IoT-Based Healthcare 178

8.4 IoT-Based Healthcare Architecture 179

8.5 IoT-Based Sensors for Health Monitoring 180

8.6 IoT Applications in Healthcare 182

8.7 Semantic Web, Ontology, and Its Usage in Healthcare Sector 183

8.8 Semantic Web-Based IoT Healthcare 183

8.9 Challenges of IoT in Healthcare Industry 185

8.10 Conclusion 186

References 186

9 Precision Medicine in the Context of Ontology 191
Rehab A. Rayan and Imran Zafar

9.1 Introduction 192

9.2 The Rationale Behind Data 195

9.3 Data Standards for Interoperability 197

9.4 The Evolution of Ontology 198

9.5 Ontologies and Classifying Disorders 199

9.6 Phenotypic Ontology of Humans in Rare Disorders 201

9.7 Annotations and Ontology Integration 202

9.8 Precision Annotation and Integration 203

9.9 Ontology in the Contexts of Gene Identification Research 204

9.10 Personalizing Care for Chronic Illness 207

9.11 Roadblocks Toward Precision Medicine 208

9.12 Future Perspectives 209

9.13 Conclusion 209

References 210

10 A Knowledgebase Model Using RDF Knowledge Graph for Clinical Decision Support Systems 215
Ravi Lourdusamy and Xavierlal J. Mattam

10.1 Introduction 216

10.2 Relational Database to Graph Database 217

10.2.1 Relational Database for Knowledge Representation 218

10.2.2 NoSQL Databases 220

10.2.3 Graph Database 223

10.3 RDF 225

10.3.1 RDF Model and Technology 226

10.3.2 Metadata and URI 226

10.3.3 RDF Stores 228

10.4 Knowledgebase Systems and Knowledge Graphs 230

10.4.1 Knowledgebase Systems 230

10.4.2 Knowledge Graphs 232

10.4.3 RDF Knowledge Graphs 233

10.4.4 Information Retrieval Using SPARQL 234

10.5 Knowledge Base for CDSS 235

10.5.1 Curation of Knowledge Base for CDSS 236

10.5.2 Proposed Model for Curation 236

10.5.3 Evaluation Methodology 238

10.6 Discussion for Further Research and Development 239

10.7 Conclusion 239

References 240

11 Medical Data Supervised Learning Ontologies for Accurate Data Analysis 249
B. Tarakeswara Rao, R. S. M. Lakshmi Patibandla, V. Lakshman Narayana and Arepalli Peda Gopi

11.1 Introduction 250

11.2 Ontology of Biomedicine 251

11.2.1 Ontology Resource Open Sharing 254

11.3 Supervised Learning 255

11.4 AQ21 Rule in Machine Learning 256

11.5 Unified Medical Systems 259

11.5.1 Note of Relevance to Bioinformatic Experts 259

11.5.2 Terminological Incorporation Principles 260

11.5.3 Cross-References External 261

11.5.4 UMLS Data Access 262

11.6 Performance Analysis 262

11.7 Conclusion 265

References 265

12 Rare Disease Diagnosis as Information Retrieval Task 269
Jaya Lakkakula, Rutuja Phate, Alfiya Korbu and Sagar Barage

12.1 Introduction 270

12.2 Definition 271

12.3 Characteristics of Rare Diseases (RDs) 272

12.4 Types of Rare Diseases 273

12.4.1 Genetic Causes 274

12.4.2 Non-Genetic Causes 275

12.4.3 Pathogenic Causes (Infectious Agents) 275

12.4.4 Toxic Agents 275

12.4.5 Other Causes 276

12.5 A Brief Classification 276

12.6 Rare Disease Databases and Online Resources 277

12.6.1 European Reference Network: ERN 277

12.6.2 Genetic and Rare Diseases Information Center: GARD 278

12.6.3 International Classification of Diseases, 10th Revision: ICD-10 279

12.6.4 Orphanet-INSERM (Institut National de la Santé et de la Recherche Médicale) 280

12.6.5 Medical Dictionary for Regulatory Activities: MedDRA 280

12.6.6 Medical Subject Headings: MeSH 281

12.6.7 Online Mendelian Inheritance in Man: OMIM 282

12.6.8 Orphanet Rare Disease Ontology: ORDO 282

12.6.9 UMLS: Unified Medical Language System 282

12.6.10 SNOMED-CT: Systematized Nomenclature of Human and Veterinary Medicine-Clinical Terms 283

12.7 Information Retrieval of Rare Diseases Through a Web Search and Other Methods 284

12.7.1 What is Information Retrieval (IR)? 284

12.7.2 Listed Below Are Some of the Methods for Information Retrieval 284

12.7.2.1 Web Search for a Diagnosis 284

12.7.2.2 Cause of Diagnostic Errors in Web-Based Tools 285

12.7.2.3 Nonprofessional Use of Web Tool for Diagnosis 285

12.7.2.4 Performance of Web Search Tools 285

12.7.2.5 Design of Watson 286

12.8 Tips and Tricks for Information Retrieval 287

12.9 Research on Rare Disease Throughout the World 288

12.10 Conclusion 290

References 290

13 Atypical Point of View of Semantic Computing in Healthcare 293
L. Mayuri and K. M. Mehata

13.1 Introduction 294

13.2 Mind the Language 295

13.2.1 Why Words Matter 296

13.2.2 What Words Matter 296

13.2.3 How Words Matter 297

13.3 Semantic Analytics and Cognitive Computing: Recent Trends 297

13.3.1 Semantic Data Analysis 298

13.3.2 Semantic Data Integration 299

13.3.3 Semantic Applications 300

13.4 Semantics-Powered Healthcare SOS Engineering 302

13.5 Conclusion 303

References 304

14 Using Artificial Intelligence to Help COVID-19 Patients 309
Ayush Hans

14.1 Introduction 310

14.2 Method 313

14.3 Results 314

14.4 Discussion 315

14.4.1 What is the Use of AI in Healthcare? 315

14.4.2 How to Use AI for Critical Care Units 315

14.4.2.1 Input Stage 315

14.4.2.2 Process Stage 316

14.4.2.3 Output Stage 317

14.5 Conclusion 320

Acknowledgment 321

References 321

Index 325