Semantic web for effective healthcare systems / edited by Vishal Jain, [and three others].

Format:

Book

Contributor:

Jain, Vishal, 1983- editor.

Series:

Machine learning in biomedical science and healthcare informatics

Language:

English

Subjects (All):

Semantic Web.

Medical informatics.

Medicine--Data processing.

Medicine.

Physical Description:

1 online resource (368 pages)

Edition:

1st edition.

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Inc., [2022]

Summary:

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 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. Audience: Researchers and graduate students in computer science, biomedical engineering, electronic and software engineering, as well as industry scientific researchers, clinicians, and systems managers in biomedical fields.

Contents:

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

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

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

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

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

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

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

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

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

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

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.

Notes:

Description based on print version record.

Other Format:

Print version: Jain, Vishal Semantic Web for Effective Healthcare Systems

ISBN:

9781119764151

1119764157

9781119764175

1119764173

9781119764168

1119764165

OCLC:

1287025144