Semantic Web for Effective Healthcare Systems E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Seitenzahl: 544
Veröffentlichungsjahr: 2021
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Table of Contents
Cover
Title page
Copyright
Preface
Acknowledgment
1 An Ontology-Based Contextual Data Modeling for Process Improvement in Healthcare
1.1 Introduction
1.2 Related Work
1.3 Motivation
1.4 Feature Extraction
1.5 Ontology Development
1.6 Dataset Description
1.7 Results and Discussions
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
2.3 Data and Database
2.4 Big Data and Database Security and Protection
References
3 Ontology-Based System for Patient Monitoring
3.1 Introduction
3.2 Literature Review
3.3 Architectural Design
3.4 Experimental Results
3.5 Conclusion and Future Enhancements
References
4 Semantic Web Solutions for Improvised Search in Healthcare Systems
4.1 Introduction
4.2 Background
4.3 Searching Techniques in Healthcare Systems
4.4 Emerging Technologies/Resources in Health Sector
4.5 Conclusion
References
5 Actionable Content Discovery for Healthcare
5.1 Introduction
5.2 Actionable Content
5.3 Health Analytics
5.4 Ontologies and Actionable Content
5.5 General Architecture for the Discovery of Actionable Content for Healthcare Domain
5.6 Conclusion
References
6 Intelligent Agent System Using Medicine Ontology
6.1 Introduction to Semantic Search
6.2 Sematic Search
6.3 Structural Pattern of Semantic Search
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
10.3 RDF
10.4 Knowledgebase Systems and Knowledge Graphs
10.5 Knowledge Base for CDSS
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
11.3 Supervised Learning
11.4 AQ21 Rule in Machine Learning
11.5 Unified Medical Systems
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
12.5 A Brief Classification
12.6 Rare Disease Databases and Online Resources
12.7 Information Retrieval of Rare Diseases Through a Web Search and Other Methods
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
13.3 Semantic Analytics and Cognitive Computing: Recent Trends
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
14.5 Conclusion
Acknowledgment
References
Index
End User License Agreement
Guide
Cover
Table of Contents
Title page
Copyright
Preface
Begin Reading
Index
End User License Agreement
List of Illustrations
Chapter 1
Figure 1.1 Decision-making process from social media reviews.
Figure 1.2 User-generated content analysis (UCA) model.
Figure 1.3 Term weighing schemes for feature extraction.
Figure 1.4 Synonymy and polysemy issues in English.
Figure 1.5 Approximated TD matrix by SVD.
Figure 1.6 LDA framework.
Figure 1.7 Plate notation of CFS
LDA
model.
Figure 1.8 Ontology-based semantic indexing (OnSI) model.
Figure 1.9 Domain ontology modeling for features.
Figure 1.10 Ontology mapping using OnSI model.
Figure 1.11 Spring view of domain ontology (DS1).
Figure 1.12 Precision vs recall curve for the Dataset DS1.
Figure 1.13 Hierarchy of MCDM problem.
Figure 1.14 Ranking of features by VIKOR method.
Chapter 2
Figure 2.1 Sources of data in healthcare. Wikipedia contributors. (2021, May 11)...
Figure 2.2 Conceptual architecture of big data.
Chapter 3
Figure 3.1 Conceptual diagram for patient’s monitoring system.
Figure 3.2 Ontology for patient’s monitoring system.
Figure 3.3 Parse tree for patient’s monitoring system.
Figure 3.4 SWRL for patient’s monitoring system.
Figure 3.5 SPQRAL query for patient’s monitoring system.
Figure 3.6 Comparison between SQL and SAPRQL.
Chapter 5
Figure 5.1 (a) Sequential composition model; (b) Parallel composition model.
Figure 5.2 Evolution of actionable content.
Figure 5.3 Data to actionable content triangle.
Figure 5.4 Spectrum of healthcare analytics.
Figure 5.5 Model of ontology.
Figure 5.6 Different types of ontologies.
Figure 5.7 Block diagram for actionable content discovery.
Figure 5.8 Overall architecture of ontology-driven actionable content discovery ...
Figure 5.9 Inner mechanism of proposed architecture.
Figure 5.10 Contextual dimension modeling diagram with data coming from various ...
Chapter 6
Figure 6.1 Architecture diagram.
Figure 6.2 Parse tree of medicine.
Figure 6.3 Medicine ontology.
Figure 6.4 Rules of construction.
Figure 6.5 SPARQL query output.
Chapter 7
Figure 7.1 The above figure represents a graph-based representation of meta-anal...
Figure 7.2 The above figure represents a graph-based representation of meta-anal...
Figure 7.3 The intraoperative results of the robotic-assisted mitral valve surge...
Figure 7.4 The encounters of complications faced during the surgery by both grou...
Figure 7.5 The postoperative results depict that the patients in Group II requir...
Chapter 8
Figure 8.1 Phases of IoT process.
Figure 8.2 IoT-based healthcare architecture.
Figure 8.3 Semantic web-based IoT healthcare.
Chapter 9
Figure 9.1 Examples of some ontologies.
Chapter 10
Figure 10.1 CAP. Consistency and tolerance to network partition or availability ...
Figure 10.2 Stages in the proposed model.
Chapter 11
Figure 11.1 Disorders of tyrosinemia type II.
Figure 11.2 UMLS metathesaurus relations.
Chapter 14
Graph 14.1 Increase in life expectancy with the advancement in healthcare sector...
Figure 14.1 Distribution of COVID cases across the world.
Figure 14.2 Projections of the healthcare facilities present VS that we require.
Figure 14.3 Three-stage model for critical care patients.
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly MA, 01915-6106
Machine Learning in Biomedical Science and Healthcare Informatics
Series Editors: Vishal Jain and Jyotir Moy Chatterjee
In this series, the focus centers on the various applications of machine learning in the biomedical engineering and healthcare fields, with a special emphasis on the most representative learning techniques, namely deep learning-based approaches. Machine learning tasks typically classified into two broad categories depending on whether there is a learning “label” or “feedback” available to a learning system: supervised learning and unsupervised learning. This series also introduces various types of machine learning tasks in the biomedical engineering field from classification (supervised learning) to clustering (unsupervised learning). The objective of the series is to compile all aspects of biomedical science and healthcare informatics, from fundamental principles to current advanced concepts.
Submission to the series: Please send book proposals to [email protected] and/or [email protected]
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Semantic Web for Effective Healthcare
Edited by
Vishal Jain
Sharda University, India
Jyotir Moy Chatterjee
Lord Buddha Education Foundation, Nepal
Ankita Bansal
Netaji Subhas University of Technology, India
and
Abha Jain
Shaheed Rajguru College of Applied Sciences for Women, Delhi University, India
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2022 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
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For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-76229-4
Cover image: Pixabay.ComCover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Preface
The tremendous amount of data being generated on a daily basis in hospitals and other medical institutions needs to be properly harnessed and analyzed in order to gain useful insights. In other words, the health-related data needs to be explored in order to uncover valuable information that could lead to improved healthcare practices and the development of better biomedical products. However, there are many challenges, which need to be addressed before this goal is reached. One of the major challenges is interoperability of health and medical data. The data generated not only comes from different sources but also has inconsistencies in naming, structure, and format. An important requirement is to capture relevant data and also make it widely available for others to use. In addition to the data integration problem, user interaction with the data is another challenge. The difficulty lies in search handling, data navigation and data presentation. Finally, another challenge is how to use this huge amount of data to find valuable new patterns and transform such data into valuable knowledge, leading to potential improvement of resource utilization and patient health, and the development of biomedical products. There is a vast potential for data mining and data analytics tools in healthcare that could lead to useful information for decision making. In recent years, the Semantic Web has been gaining ground in addressing these challenges. The aim of this book is to analyze the current status on how the Semantic Web can be used to solve various challenges and enlighten readers with key advances in ontology-based information retrieval techniques in the healthcare domain. The following is a brief summary of the wide range of subjects covered throughout the book.
-
Chapter 1
discusses various information extraction techniques used to model the documents product/service reviews. The advantages of using the Semantic Web to ease communication between businesses and improve processes are also discussed.
-
Chapter 2
explores the impact of Semantic Web technologies and the challenges associated with their use in effective healthcare systems and also proposes solutions, which can be achieved with the present technology. In addition to this, some algorithms, frameworks, and real-time database systems realized with the help of artificial intelligence and web technology-based solutions are also discussed.
-
Chapter 3
focuses on the importance of an ontology-based system for a patient monitoring system. A domain ontology has been constructed to preserve the details of patient health issues. With the support of ontology, a patient monitoring system is constructed wherein data concerning every detail about the patient and their health is stored.
-
Chapter 4
highlights the role of Semantic Web technologies in improving services provided by healthcare systems. It elaborates on the search techniques used by researchers in the field to find the desired information. The role of semantics, how they are beneficial in the search process, and domain-specific resources are presented in detail. The latest technological advancements and resources from the bio-medical field are also discussed.
-
Chapter 5
discusses what actionable content should look like in practice and how it can become more efficient by aiding in clinical decision-making and administrative capability. The chapter renders various definitions of actionable content, and also focuses on the stages of health analytics and how ontology can be used for prescriptive health analytics.
-
Chapter 6
depicts the retrieval of ontology-based information from the medical literature database MEDLINE. The main focus of the chapter is to enhance the retrieval of information from the medical literature database and conduct the search with more clarity. The approach discussed to achieve this is the preliminary design and execution of an ontology-based intelligent agent system that applies Semantic Web language, which benefits efficient systematic retrieval of medical information.
-
Chapter 7
presents a historical analysis of an ontology-based system for robotic surgery and documents the most significant interventions of robots in medical surgery. The chapter discusses how the academic field has embraced this new discipline and how inclusive research on a worldwide scale has honed the design and method of robotic procedures, all while maintaining an impeccable metric.
-
Chapter 8
presents the applications of IoT in healthcare and how these applications can be used with the help of various sensors. It discusses the established strategies used by IoT-based devices to deal with patients, doctors, and hospitals in order to provide smarter and faster services. The authors propose an IoT-based architecture for monitoring the health of patients remotely.
-
Chapter 9
discusses the use of precision medicine in the context of ontology. It explains ontologies and their application in computational reasoning to promote an accurate classification of patients’ diagnoses and managing care, and for translational research.
-
Chapter 10
discusses the use of knowledge graphs for knowledge representation. A model for such a knowledgebase is proposed that makes use of open information extraction systems to capture relevant knowledge from medical literature and curate it in the knowledgebase of the clinical decision support system.
-
Chapter 11
covers all aspects related to the successful customization of data semantics, ontologies, clinical jobs, and free learning, and depicts the Unified Medical Language System (UMLS) framework used inside AQ21 rule learning programming. Ontologies are the quality systems for expressive genuine variables in clinical and flourishing fields.
-
Chapter 12
provides information on rare diseases and explores the relationship between rare diseases, diagnoses, and information retrieval. In particular, it illustrates the history, characteristics, types, and classification along with databases of rare disease information. It also explores the challenges faced by researchers in rare disease information retrieval and how they can be resolved by search query optimization.
-
Chapter 13
reviews the recent advances in medical terminology tools and application strategies currently in use for semantic reasoning and interoperability in healthcare. Common terminology standards used in health information and technology, such as SNOMED CT, RxNorm, LOINC, ICD-x-CM, and UCUM, are discussed. Also discussed are the current reference terminology mapping solutions that enable semantic interoperability of data between health systems.
-
Chapter 14
builds upon the existing AI-based model in order to discover a new model to improve healthcare facilities for the faster recovery of COVID-19 patients. The chapter discusses different AI-related solutions for the healthcare industry.
In conclusion, we are grateful to all those who directly and indirectly contributed to this book. We are also grateful to the publisher for giving us the opportunity to publish it.
Vishal JainJyotir Moy ChatterjeeAnkita BansalAbha JainSeptember 2021
Acknowledgment
I would like to acknowledge the most important people in my life—my late grandfather Shri Gopal Chatterjee, my late grandmother Smt. Subhankori Chatterjee, my late mother Nomita Chatterjee, my uncle Shri. Moni Moy Chatterjee, and my father Shri. Aloke Moy Chatterjee. The book has been my long-cherished dream, which would not have become a reality without the support and love of these amazing people. They continued to encourage me despite my failing to give them the proper time and attention. I am also grateful to my friends, who have encouraged and blessed this work with their unconditional love and patience.
Jyotir Moy ChatterjeeDepartment of ITLord Buddha Education Foundation(Asia Pacific University of Technology & Innovation)Kathmandu, Nepal
