Оглавление
Группа авторов. Semantic Web for Effective Healthcare Systems
Table of Contents
Guide
List of Illustrations
List of Tables
Pages
Semantic Web for Effective Healthcare
Preface
Acknowledgment
1. An Ontology-Based Contextual Data Modeling for Process Improvement in Healthcare
1.1 Introduction
1.1.1 Ontology-Based Information Extraction
1.1.2 Ontology-Based Knowledge Representation
1.2 Related Work
1.3 Motivation
1.4 Feature Extraction
1.4.1 Vector Space Model
1.4.2 Latent Semantic Indexing (LSI)
1.4.3 Clustering Techniques
1.4.4 Topic Modeling
1.5 Ontology Development
1.5.1 Ontology-Based Semantic Indexing (OnSI) Model
1.5.2 Ontology Development
1.5.3 OnSI Model Evaluation
1.5.4 Metrics Analysis
1.6 Dataset Description
1.7 Results and Discussions
1.7.1 Discussion 1
1.7.2 Discussion 2
1.7.3 Discussion 3
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. 2.2.1 What Is Website?
2.2.2 Types of Website. 2.2.2.1 Static Website
2.2.2.2 Dynamic Website
2.2.3 What Is Semantic Web?
2.2.4 Role of Semantic Web
2.2.4.1 Pros and Cons of Semantic Web
2.2.4.2 Impact on Patient
2.2.4.3 Impact on Practitioner
2.2.4.4 Impact on Researchers
2.3 Data and Database
2.3.1 What Is Data?
2.3.2 What Is Database?
2.3.3 Source of Data in the Healthcare System
2.3.3.1 Electronic Health Record (EHR)
2.3.3.2 Biomedical Image Analysis
2.3.3.3 Sensor Data Analysis
2.3.3.4 Genomic Data Analysis
2.3.3.5 Clinical Text Mining
2.3.3.6 Social Media
2.3.4 Why Are Databases Important?
2.3.5 Challenges With the Database in the Healthcare System
2.4 Big Data and Database Security and Protection. 2.4.1 What Is Big Data
2.4.2 Five V’s of Big Data
2.4.2.1 Volume
2.4.2.2 Variety
2.4.2.3 Velocity
2.4.2.4 Veracity
2.4.2.5 Value
2.4.3 Architectural Framework of Big Data
2.4.4 Data Protection Versus Data Security in Healthcare
2.4.4.1 Phishing Attacks
2.4.4.2 Malware and Ransomware
2.4.4.3 Cloud Threats
2.4.5 Technology in Use to Secure the Healthcare Data
2.4.5.1 Access Control Policy
2.4.6 Monitoring and Auditing
2.4.7 Standard for Data Protection
2.4.7.1 Healthcare Standard in India
2.4.7.2 Security Technical Standards
2.4.7.3 Administrative Safeguards Standards
2.4.7.4 Physical Safeguard Standards
References
3. Ontology-Based System for Patient Monitoring
3.1 Introduction
3.1.1 Basics of Ontology
3.1.2 Need of Ontology in Patient Monitoring
3.2 Literature Review
3.2.1 Uses of Ontology in Various Domains
3.2.2 Ontology in Patient Monitoring System
3.3 Architectural Design
3.3.1 Phases of Patient Monitoring System
3.3.2 Reasoner in Patient Monitoring
3.4 Experimental Results
3.4.1 SPARQL Results
3.4.2 Comparison Between Other Systems
3.5 Conclusion and Future Enhancements
References
4. Semantic Web Solutions for Improvised Search in Healthcare Systems
4.1 Introduction
4.1.1 Key Benefits and Usage of Technology in Healthcare System
4.2 Background
4.2.1 Significance of Semantics in Healthcare Systems
4.2.2 Scope and Benefits of Semantics in Healthcare Systems
4.2.3 Issues in Incorporating Semantics
4.2.4 Existing Semantic Web Technologies
4.3 Searching Techniques in Healthcare Systems
4.3.1 Keyword-Based Search
4.3.2 Controlled Vocabularies Based Search
4.3.3 Improvising Searches With Semantic Web Solutions
4.3.4 Health Domain-Specific Resources for Semantic Search
4.3.4.1 Ontologies
4.3.4.2 Libraries
4.3.4.3 Search Engines
4.3.4.3.1 PUBMED
4.3.4.3.2 Quertle
4.3.4.3.3 FACTA+
4.3.4.3.4 THALIA
4.3.4.3.5 LitVar
4.3.4.3.6 CTGA
4.3.4.3.7 GoWeb
4.3.4.3.8 BioTCM
4.3.4.3.9 BEST
4.4 Emerging Technologies/Resources in Health Sector
4.4.1 Elasticsearch
4.4.2 BioBERT
4.4.3 Knowledge Graphs
4.5 Conclusion
References
5. Actionable Content Discovery for Healthcare
5.1 Introduction
5.2 Actionable Content
5.2.1 Actionable Content in Theory
5.2.2 Actionable Content in Practice
5.3 Health Analytics
5.3.1 Artificial Intelligence/Machine Learning-Based Predictive Analytics
5.3.2 Semantic Technology for Prescriptive Health Analytics
5.4 Ontologies and Actionable Content
5.4.1 Ontologies in Healthcare Domain
5.5 General Architecture for the Discovery of Actionable Content for Healthcare Domain
5.5.1 Ontology-Driven Actionable Content Discovery in Healthcare Domain
5.5.2 Case Study for Actionable Content Discovery in Cancer Domain
5.6 Conclusion
References
6. Intelligent Agent System Using Medicine Ontology
6.1 Introduction to Semantic Search
6.1.1 What Is an Ontology in Terms of Medicine?
6.1.2 Needs and Benefits of Ontology in Medical Search
6.2 Sematic Search
6.2.1 How NLP Works in Sematic Search?
6.2.2 Part of Speech Tagging and Chunking
6.2.3 Sentence Parsing
6.2.4 Discussion About the Various Semantic Search in Medical Databases
6.2.5 Discussion About the Retrieval Tools Used in Sematic Search in Medline
6.3 Structural Pattern of Semantic Search
6.3.1 Architectural Diagram
6.3.2 Agent Ontology
6.3.3 Rule-Based Approach
6.3.4 Reasoners-Based Approach
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
10.2.1 Relational Database for Knowledge Representation
10.2.2 NoSQL Databases
10.2.3 Graph Database
10.3 RDF
10.3.1 RDF Model and Technology
10.3.2 Metadata and URI
10.3.3 RDF Stores
10.4 Knowledgebase Systems and Knowledge Graphs
10.4.1 Knowledgebase Systems
10.4.2 Knowledge Graphs
10.4.3 RDF Knowledge Graphs
10.4.4 Information Retrieval Using SPARQL
10.5 Knowledge Base for CDSS
10.5.1 Curation of Knowledge Base for CDSS
10.5.2 Proposed Model for Curation
10.5.3 Evaluation Methodology
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
11.2.1 Ontology Resource Open Sharing
11.3 Supervised Learning
11.4 AQ21 Rule in Machine Learning
11.5 Unified Medical Systems
11.5.1 Note of Relevance to Bioinformatic Experts
11.5.2 Terminological Incorporation Principles
11.5.3 Cross-References External
11.5.4 UMLS Data Access
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
12.4.1 Genetic Causes
12.4.2 Non-Genetic Causes
12.4.3 Pathogenic Causes (Infectious Agents)
12.4.4 Toxic Agents
12.4.5 Other Causes
12.5 A Brief Classification
12.6 Rare Disease Databases and Online Resources
12.6.1 European Reference Network: ERN
12.6.2 Genetic and Rare Diseases Information Center: GARD
12.6.3 International Classification of Diseases, 10th Revision: ICD-10
12.6.4 Orphanet-INSERM (Institut National de la Santé et de la Recherche Médicale)
12.6.5 Medical Dictionary for Regulatory Activities: MedDRA
12.6.6 Medical Subject Headings: MeSH
12.6.7 Online Mendelian Inheritance in Man: OMIM
12.6.8 Orphanet Rare Disease Ontology: ORDO
12.6.9 UMLS: Unified Medical Language System
12.6.10 SNOMED-CT: Systematized Nomenclature of Human and Veterinary Medicine—Clinical Terms
12.7 Information Retrieval of Rare Diseases Through a Web Search and Other Methods. 12.7.1 What Is Information Retrieval (IR)?
12.7.2 Listed Below Are Some of the Methods for Information Retrieval. 12.7.2.1 Web Search for a Diagnosis
12.7.2.2 Cause of Diagnostic Errors in Web-Based Tools
12.7.2.3 Nonprofessional Use of Web Tool for Diagnosis
12.7.2.4 Performance of Web Search Tools
12.7.2.5 Design of Watson
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
13.2.1 Why Words Matter
13.2.2 What Words Matter
13.2.3 How Words Matter
13.3 Semantic Analytics and Cognitive Computing: Recent Trends
13.3.1 Semantic Data Analysis
13.3.2 Semantic Data Integration
13.3.3 Semantic Applications
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. 14.4.1 What is the Use of AI in Healthcare?
14.4.2 How to Use AI for Critical Care Units
14.4.2.1 Input Stage
14.4.2.2 Process Stage
14.4.2.3 Output Stage
14.5 Conclusion
Acknowledgment
References
Index
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