Semantic web for effective healthcare systems
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 util...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc
[2022]
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| Edición: | 1st edition |
| Colección: | Machine learning in biomedical science and healthcare informatics
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009645712106719 |
Tabla de Contenidos:
- 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.
