Semantic Web for Effective Healthcare Systems: Impact and Challenges

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
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
			p(w|d)
	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
	4.4 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 SVM-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
EULA