-
Browse Book Series
- Adhesion and Adhesives: Fundamental and Applied Aspects
- Advances in Additive Manufacturing Technologies
- Advances in Antenna, Microwave and Communication Engineering
- Advances in Biofeedstocks and Biofuels
- Advances in Cyber Security
- Advances in Data Engineering and Machine Learning
- Advances in E-Mobility
- Advances in Hydrogen Production and Storage
- Advances in Intelligent and Scientific Computing (AISC)
- Advances in Learning Analytics for Intelligent Cloud-IoT Systems
- Advances in Membrane Processes
- Advances in Nanotechnology and Applications
- Advances in Natural Gas Engineering
- Advances in Pipes and Pipelines
- Advances in Production Engineering
- Advances in Solar Cell Materials and Storage
- Applied Functional Materials
- Artificial Intelligence and Soft Computing for Industrial Transformation
- Astrobiology Perspectives on Life in the Universe
- Bioprocessing in Food Science
- Computational Intelligence and Biomedical Engineering
- Concise Introductions to AI and Data Science
- Cyber Systems and Quantum Engineering
- Decentralized Systems and Next-Generation Internet
- Diatoms: Biology and Applications
- Digital Convergence in Engineering Systems
- Emerging Trends in Medicinal and Pharmaceutical Chemistry
- Engineering Systems Design for Sustainable Development
- Fintech in a Sustainable Digital Society
- Industry 5.0 Transformation Applications
- Infectious and Rare Diseases
- Innovations in Materials and Manufacturing
- Machine Learning in Biomedical Science and Healthcare Informatics
- Materials Degradation and Failure
- Mathematics and Computer Science
- Next-Generation Computing and Communication Engineering
- Performability Engineering Series
- Rotating Machinery Fundamentals and Advances
- Studies in Computational Intelligence and Smart Technologies
- Sustainable Computing and Optimization
Browse Subject Areas
For Authors
Submit a ProposalSemantic Web for Effective Healthcare Systems
 | Edited by Vishal Jain, Jyotir Moy Chatterjee, Ankita Bansal and Abha Jain
Series: Machine Learning in Biomedical Science and Healthcare Informatics Copyright: 2022 | Status: Published ISBN: 9781119762294 | Hardcover | 352 pages | 55 illustrations Price: $195 USD  |
One Line Description
The book summarizes the trends and current research advances in web semantics, delineating the existing tools, techniques, methodologies, and research solutions.
Audience
Researchers and graduate students in computer science, biomedical engineering, and electronic and software engineering, as well as industry scientific researchers, clinicians, and systems managers in biomedical fields.
Researchers and graduate students in computer science, biomedical engineering, and electronic and software engineering, as well as industry scientific researchers, clinicians, and systems managers in biomedical fields.
Description
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.
Back to Top
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.
Back to Top
Author / Editor Details
Vishal Jain is an associate professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U. P. India. He obtained Ph.D (CSE), M.Tech (CSE), MBA (HR), MCA, MCP and CCNA. He has authored more than 80 research papers in reputed conferences and journals, including Web of Science and Scopus. He has authored and edited more than 10 books with various international publishers.
Vishal Jain is an associate professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U. P. India. He obtained Ph.D (CSE), M.Tech (CSE), MBA (HR), MCA, MCP and CCNA. He has authored more than 80 research papers in reputed conferences and journals, including Web of Science and Scopus. He has authored and edited more than 10 books with various international publishers.
Jyotir Moy Chatterjee is an assistant professor in the Department of Information Technology at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal.
Ankita Bansal is an assistant professor in the Division of Information Technology at Netaji Subhas University of Technology. She received her master’s and doctoral degree in computer science from Delhi Technological University (DTU).
Abha Jain is an assistant professor in the Department of Computer Science Engineering, Shaheed Rajguru College of Applied Sciences for Women, Delhi University, India. She received her master’s and doctorate degree in software engineering from Delhi Technological University.
Back to Top
Table of Contents
Preface
Acknowledgment
1. An Ontology-Based Contextual Data Modeling for Process Improvement in Healthcare
A. M. Abirami and A. Askarunisa
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 Hemendra Shankar Sharma and Ashish Sharma
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
R. Mervin, Tintu Thomas and A. Jaya
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
Nidhi Malik, Aditi Sharan and Sadika Verma
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
Ujwala Bharambe and Anuradha Srinivasaraghavan
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
Tintu Thomas and R. Mervin
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
Ajay Agarwal and Amit Kumar Mishra
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
Sapna Juneja, Abhinav Juneja, Annu Dhankhar and Vishal Jain
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
Rehab A. Rayan and Imran Zafar
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
Ravi Lourdusamy and Xavierlal J. Mattam
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
B. Tarakeswara Rao, R. S. M. Lakshmi Patibandla, V. Lakshman Narayana and Arepalli Peda Gopi
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
Jaya Lakkakula, Rutuja Phate, Alfiya Korbu and Sagar Barage
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 278
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 of Semantic Computing in Healthcare
L. Mayuri and K. M. Mehata
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
Ayush Hans
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
Back to Top
Preface
Acknowledgment
1. An Ontology-Based Contextual Data Modeling for Process Improvement in Healthcare
A. M. Abirami and A. Askarunisa
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 Hemendra Shankar Sharma and Ashish Sharma
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
R. Mervin, Tintu Thomas and A. Jaya
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
Nidhi Malik, Aditi Sharan and Sadika Verma
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
Ujwala Bharambe and Anuradha Srinivasaraghavan
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
Tintu Thomas and R. Mervin
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
Ajay Agarwal and Amit Kumar Mishra
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
Sapna Juneja, Abhinav Juneja, Annu Dhankhar and Vishal Jain
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
Rehab A. Rayan and Imran Zafar
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
Ravi Lourdusamy and Xavierlal J. Mattam
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
B. Tarakeswara Rao, R. S. M. Lakshmi Patibandla, V. Lakshman Narayana and Arepalli Peda Gopi
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
Jaya Lakkakula, Rutuja Phate, Alfiya Korbu and Sagar Barage
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 278
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 of Semantic Computing in Healthcare
L. Mayuri and K. M. Mehata
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
Ayush Hans
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
Back to Top