Semantic Web for Effective Healthcare Systems: Impact and Challenges
- Length: 352 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2021-12-09
- ISBN-10: 1119762294
- ISBN-13: 9781119762294
- Sales Rank: #0 (See Top 100 Books)
Recently, the Semantic Web has gained huge popularity to address these challenges. Semantic web technologies have the opportunity to transform the way healthcare providers utilize technology to gain insights and knowledge from their data and make 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 Semantic Web is used to solve the health data integration and interoperability problem, how it provides advanced data linking capabilities that can improve search and retrieval of medical data. There are chapters in the book which 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. To summarize, the book will help readers understand key concepts in semantic web applications for biomedical engineering and healthcare.
Cover Table of Contents Title page Copyright Preface Acknowledgment 1 An Ontology-Based Contextual Data Modeling for Process Improvement in Healthcare 1.1 Introduction 1.2 Related Work 1.3 Motivation 1.4 Feature Extraction 1.5 Ontology Development 1.6 Dataset Description 1.7 Results and Discussions 1.8 Applications 1.9 Conclusion 1.10 Future Work References 2 Semantic Web for Effective Healthcare Systems: Impact and Challenges 2.1 Introduction 2.2 Overview of the Website in Healthcare 2.3 Data and Database 2.4 Big Data and Database Security and Protection References 3 Ontology-Based System for Patient Monitoring 3.1 Introduction 3.2 Literature Review 3.3 Architectural Design 3.4 Experimental Results 3.5 Conclusion and Future Enhancements References 4 Semantic Web Solutions for Improvised Search in Healthcare Systems 4.1 Introduction 4.2 Background 4.3 Searching Techniques in Healthcare Systems 4.4 Emerging Technologies/Resources in Health Sector 4.5 Conclusion References 5 Actionable Content Discovery for Healthcare 5.1 Introduction 5.2 Actionable Content 5.3 Health Analytics 5.4 Ontologies and Actionable Content 5.5 General Architecture for the Discovery of Actionable Content for Healthcare Domain 5.6 Conclusion References 6 Intelligent Agent System Using Medicine Ontology 6.1 Introduction to Semantic Search 6.2 Sematic Search 6.3 Structural Pattern of Semantic Search 6.4 Implementation of Reasoners 6.5 Implementation and Results 6.6 Conclusion and Future Prospective References 7 Ontology-Based System for Robotic Surgery—A Historical Analysis 7.1 Historical Discourse of Surgical Robots 7.2 The Necessity for Surgical Robots 7.3 Ontological Evolution of Robotic Surgical Procedures in Various Domains 7.4 Inferences Drawn From the Table 7.5 Transoral Robotic Surgery 7.6 Pancreatoduodenectomy 7.7 Robotic Mitral Valve Surgery 7.8 Rectal Tumor Surgery 7.9 Robotic Lung Cancer Surgery 7.10 Robotic Surgery in Gynecology 7.11 Robotic Radical Prostatectomy 7.12 Conclusion 7.13 Future Work References 8 IoT-Enabled Effective Healthcare Monitoring System Using Semantic Web 8.1 Introduction 8.2 Literature Review 8.3 Phases of IoT-Based Healthcare 8.4 IoT-Based Healthcare Architecture 8.5 IoT-Based Sensors for Health Monitoring 8.6 IoT Applications in Healthcare 8.7 Semantic Web, Ontology, and Its Usage in Healthcare Sector 8.8 Semantic Web-Based IoT Healthcare 8.9 Challenges of IoT in Healthcare Industry 8.10 Conclusion References 9 Precision Medicine in the Context of Ontology 9.1 Introduction 9.2 The Rationale Behind Data 9.3 Data Standards for Interoperability 9.4 The Evolution of Ontology 9.5 Ontologies and Classifying Disorders 9.6 Phenotypic Ontology of Humans in Rare Disorders 9.7 Annotations and Ontology Integration 9.8 Precision Annotation and Integration 9.9 Ontology in the Contexts of Gene Identification Research 9.10 Personalizing Care for Chronic Illness 9.11 Roadblocks Toward Precision Medicine 9.12 Future Perspectives 9.13 Conclusion References 10 A Knowledgebase Model Using RDF Knowledge Graph for Clinical Decision Support Systems 10.1 Introduction 10.2 Relational Database to Graph Database 10.3 RDF 10.4 Knowledgebase Systems and Knowledge Graphs 10.5 Knowledge Base for CDSS 10.6 Discussion for Further Research and Development 10.7 Conclusion References 11 Medical Data Supervised Learning Ontologies for Accurate Data Analysis 11.1 Introduction 11.2 Ontology of Biomedicine 11.3 Supervised Learning 11.4 AQ21 Rule in Machine Learning 11.5 Unified Medical Systems 11.6 Performance Analysis 11.7 Conclusion References 12 Rare Disease Diagnosis as Information Retrieval Task 12.1 Introduction 12.2 Definition 12.3 Characteristics of Rare Diseases (RDs) 12.4 Types of Rare Diseases 12.5 A Brief Classification 12.6 Rare Disease Databases and Online Resources 12.7 Information Retrieval of Rare Diseases Through a Web Search and Other Methods 12.8 Tips and Tricks for Information Retrieval 12.9 Research on Rare Disease Throughout the World 12.10 Conclusion References 13 Atypical Point of View on Semantic Computing in Healthcare 13.1 Introduction 13.2 Mind the Language 13.3 Semantic Analytics and Cognitive Computing: Recent Trends 13.4 Semantics-Powered Healthcare SOS Engineering 13.5 Conclusion References 14 Using Artificial Intelligence to Help COVID-19 Patients 14.1 Introduction 14.2 Method 14.3 Results 14.4 Discussion 14.5 Conclusion Acknowledgment References Index End User License Agreement
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