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E-raamat: Ontology-Based Information Retrieval for Healthcare Systems

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  • Ilmumisaeg: 10-Jul-2020
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119641360
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Ontology-Based Information Retrieval for Healthcare SystemsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 10-Jul-2020
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119641360
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With the advancements of semantic web, ontology has become the crucial mechanism for representing concepts in various domains. For research and dispersal of customized healthcare services, a major challenge is to efficiently retrieve and analyze individual patient data from a large volume of heterogeneous data over a long time span. This requirement demands effective ontology-based information retrieval approaches for clinical information systems so that the pertinent information can be mined from large amount of distributed data.

This unique and groundbreaking book highlights the key advances in ontology-based information retrieval techniques being applied in the healthcare domain and covers the following areas:

  • Semantic data integration in e-health care systems
  • Keyword-based medical information retrieval
  • Ontology-based query retrieval support for e-health implementation
  • Ontologies as a database management system technology for medical information retrieval
  • Information integration using contextual knowledge and ontology merging
  • Collaborative ontology-based information indexing and retrieval in health informatics
  • An ontology-based text mining framework for vulnerability assessment in health and social care
  • An ontology-based multi-agent system for matchmaking patient healthcare monitoring
  • A multi-agent system for querying heterogeneous data sources with ontologies for reducing cost of customized healthcare systems
  • A methodology for ontology based multi agent systems development
  • Ontology based systems for clinical systems: validity, ethics and regulation
Preface xix
Acknowledgment xxiii
1 Role of Ontology in Health Care
1(18)
Sonia Singla
1.1 Introduction
2(1)
1.2 Ontology in Diabetes
3(3)
1.2.1 Ontology Process
4(1)
1.2.2 Impediments of the Present Investigation
5(1)
1.3 Role of Ontology in Cardiovascular Diseases
6(2)
1.4 Role of Ontology in Parkinson Diseases
8(5)
1.4.1 The Spread of Disease With Age and Onset of Disease
10(1)
1.4.2 Cost of PD for Health Care, Household
11(1)
1.4.3 Treatment and Medicines
11(2)
1.5 Role of Ontology in Depression
13(2)
1.6 Conclusion
15(1)
1.7 Future Scope
15(4)
References
15(4)
2 A Study on Basal Ganglia Circuit and Its Relation With Movement Disorders
19(18)
Dinesh Bhatia
2.1 Introduction
19(2)
2.2 Anatomy and Functioning of Basal Ganglia
21(5)
2.2.1 The Striatum-Major Entrance to Basal Ganglia Circuitry
22(1)
2.2.2 Direct and Indirect Striatofugal Projections
23(2)
2.2.3 The STN: Another Entrance to Basal Ganglia Circuitry
25(1)
2.3 Movement Disorders
26(3)
2.3.1 Parkinson Disease
26(1)
2.3.2 Dyskinetic Disorder
27(1)
2.3.3 Dystonia
28(1)
2.4 Effect of Basal Ganglia Dysfunctioning on Movement Disorders
29(2)
2.5 Conclusion and Future Scope
31(6)
References
31(6)
3 Extraction of Significant Association Rules Using Pre- and Post-Mining Techniques---An Analysis
37(32)
M. Nandhini
S. N. Sivanandam
3.1 Introduction
38(1)
3.2 Background
39(5)
3.2.1 Interestingness Measures
39(1)
3.2.2 Pre-Mining Techniques
40(1)
3.2.2.1 Candidate Set Reduction Schemes
40(1)
3.2.2.2 Optimal Threshold Computation Schemes
41(1)
3.2.2.3 Weight-Based Mining Schemes
42(1)
3.2.3 Post-Mining Techniques
42(1)
3.2.3.1 Rule Pruning Schemes
43(1)
3.2.3.2 Schemes Using Knowledge Base
43(1)
3.3 Methodology
44(15)
3.3.1 Data Preprocessing
44(2)
3.3.2 Pre-Mining
46(1)
3.3.2.1 Pre-Mining Technique 1: Optimal Support and Confidence Threshold Value Computation Using PSO
46(2)
3.3.2.2 Pre-Mining Technique 2: Attribute Weight Computation Using IG Measure
48(2)
3.3.3 Association Rule Generation
50(1)
3.3.3.1 ARM Preliminaries
50(2)
3.3.3.2 WARM Preliminaries
52(4)
3.3.4 Post-Mining
56(1)
3.3.4.1 Filters
56(2)
3.3.4.2 Operators
58(1)
3.3.4.3 Rule Schemas
58(1)
3.4 Experiments and Results
59(4)
3.4.1 Parameter Settings for PSO-Based Pre-Mining Technique
60(1)
3.4.2 Parameter Settings for PAW-Based Pre-Mining Technique
60(3)
3.5 Conclusions
63(6)
References
65(4)
4 Ontology in Medicine as a Database Management System
69(22)
K. O. Shobowale
4.1 Introduction
70(2)
4.1.1 Ontology Engineering and Development Methodology
72(1)
4.2 Literature Review on Medical Data Processing
72(3)
4.3 Information on Medical Ontology
75(3)
4.3.1 Types of Medical Ontology
75(1)
4.3.2 Knowledge Representation
76(1)
4.3.3 Methodology of Developing Medical Ontology
76(1)
4.3.4 Medical Ontology Standards
77(1)
4.4 Ontologies as a Knowledge-Based System
78(8)
4.4.1 Domain Ontology in Medicine
79(2)
4.4.2 Brief Introduction of Some Medical Standards
81(1)
4.4.2.1 Medical Subject Headings (MeSH)
81(1)
4.4.2.2 Medical Dictionary for Regulatory Activities (MedDRA)
81(1)
4.4.2.3 Medical Entities Dictionary (MED)
81(1)
4.4.3 Reusing Medical Ontology
82(3)
4.4.4 Ontology Evaluation
85(1)
4.5 Conclusion
86(1)
4.6 Future Scope
86(5)
References
87(4)
5 Using IoT and Semantic Web Technologies for Healthcare and Medical Sector
91(26)
Nikita Malik
Sanjay Kumar Malik
5.1 Introduction
92(6)
5.1.1 Significance of Healthcare and Medical Sector and Its Digitization
92(1)
5.1.2 E-Health and m-Health
92(2)
5.1.3 Internet of Things and Its Use
94(2)
5.1.4 Semantic Web and Its Technologies
96(2)
5.2 Use of IoT in Healthcare and Medical Domain
98(3)
5.2.1 Scope of IoT in Healthcare and Medical Sector
98(2)
5.2.2 Benefits of IoT in Healthcare and Medical Systems
100(1)
5.2.3 IoT Healthcare Challenges and Open Issues
100(1)
5.3 Role of SWTs in Healthcare Services
101(5)
5.3.1 Scope and Benefits of Incorporating Semantics in Healthcare
101(2)
5.3.2 Ontologies and Datasets for Healthcare and Medical Domain
103(1)
5.3.3 Challenges in the Use of SWTs in Healthcare Sector
104(2)
5.4 Incorporating IoT and/or SWTs in Healthcare and Medical Sector
106(4)
5.4.1 Proposed Architecture or Framework or Model
106(2)
5.4.2 Access Mechanisms or Approaches
108(1)
5.4.3 Applications or Systems
109(1)
5.5 Healthcare Data Analytics Using Data Mining and Machine Learning
110(2)
5.6 Conclusion
112(1)
5.7 Future Work
113(4)
References
113(4)
6 An Ontological Model, Design, and Implementation of CSPF for Healthcare
117(26)
Pooja Mohan
6.1 Introduction
117(2)
6.2 Related Work
119(3)
6.3 Mathematical Representation of CSPF Model
122(5)
6.3.1 Basic Sets of CSPF Model
123(1)
6.3.2 Conditional Contextual Security and Privacy Constraints
123(1)
6.3.3 CSPF Model States C set of States
124(1)
6.3.4 Permission C permission
124(1)
6.3.5 Security Evaluation Function (SEFcontexts)
124(1)
6.3.6 Secure State
125(1)
6.3.7 CSPF Model Operations
125(1)
6.3.7.1 Administrative Operations
125(2)
6.3.7.2 Users' Operations
127(1)
6.4 Ontological Model
127(2)
6.4.1 Development of Class Hierarchy
127(2)
6.4.1.1 Object Properties of Sensor Class
129(1)
6.4.1.2 Data Properties
129(1)
6.4.1.3 The Individuals
129(1)
6.5 The Design of Context-Aware Security and Privacy Model for Wireless Sensor Network
129(4)
6.6 Implementation
133(2)
6.7 Analysis and Results
135(2)
6.7.1 Inference Time/Latency/Query Response Time vs. No. of Policies
135(1)
6.7.2 Average Inference Time vs. Contexts
136(1)
6.8 Conclusion and Future Scope
137(6)
References
138(5)
7 Ontology-Based Query Retrieval Support for E-Health Implementation
143(24)
Aatif Ahmad Khan
Sanjay Kumar Malik
7.1 Introduction
143(3)
7.1.1 Health Care Record Management
144(1)
7.1.1.1 Electronic Health Record
144(1)
7.1.1.2 Electronic Medical Record
145(1)
7.1.1.3 Picture Archiving and Communication System
145(1)
7.1.1.4 Pharmacy Systems
145(1)
7.1.2 Information Retrieval
145(1)
7.1.3 Ontology
146(1)
7.2 Ontology-Based Query Retrieval Support
146(4)
7.3 E-Health
150(4)
7.3.1 Objectives and Scope
150(1)
7.3.2 Benefits of E-Health
151(1)
7.3.3 E-Health Implementation
151(3)
7.4 Ontology-Driven Information Retrieval for E-Health
154(6)
7.4.1 Ontology for E-Heath Implementation
155(2)
7.4.2 Frameworks for Information Retrieval Using Ontology for E-Health
157(1)
7.4.3 Applications of Ontology-Driven Information Retrieval in Health Care
158(2)
7.4.4 Benefits and Limitations
160(1)
7.5 Discussion
160(4)
7.6 Conclusion
164(3)
References
164(3)
8 Ontology-Based Case Retrieval in an E-Mental Health Intelligent Information System
167(26)
Georgia Kaoura
Konstantinos Kovas
Basilis Boutsinas
8.1 Introduction
167(3)
8.2 Literature Survey
170(3)
8.3 Problem Identified
173(1)
8.4 Proposed Solution
174(9)
8.4.1 The PAVEFS Ontology
174(5)
8.4.2 Knowledge Base
179(1)
8.4.3 Reasoning
180(2)
8.4.4 User Interaction
182(1)
8.5 Pros and Cons of Solution
183(6)
8.5.1 Evaluation Methodology and Results
183(2)
8.5.2 Evaluation Methodology
185(1)
8.5.2.1 Evaluation Tools
186(1)
8.5.2.2 Results
187(2)
8.6 Conclusions
189(1)
8.7 Future Scope
190(3)
References
190(3)
9 Ontology Engineering Applications in Medical Domain
193(40)
Mariam Gawich
Marco Alfonse
9.1 Introduction
193(2)
9.2 Ontology Activities
195(2)
9.2.1 Ontology Learning
195(1)
9.2.2 Ontology Matching
195(1)
9.2.3 Ontology Merging (Unification)
195(1)
9.2.4 Ontology Validation
196(1)
9.2.5 Ontology Verification
196(1)
9.2.6 Ontology Alignment
196(1)
9.2.7 Ontology Annotation
196(1)
9.2.8 Ontology Evaluation
196(1)
9.2.9 Ontology Evolution
196(1)
9.3 Ontology Development Methodologies
197(6)
9.3.1 TOVE
197(1)
9.3.2 Methontology
198(1)
9.3.3 Brusa et al. Methodology
198(1)
9.3.4 UPON Methodology
199(1)
9.3.5 Uschold and King Methodology
200(3)
9.4 Ontology Languages
203(5)
9.4.1 RDF-RDF Schema
203(2)
9.4.2 OWL
205(1)
9.4.3 OWL 2
205(3)
9.5 Ontology Tools
208(4)
9.5.1 Apollo
208(1)
9.5.2 NeON
209(1)
9.5.3 Protege
210(2)
9.6 Ontology Engineering Applications in Medical Domain
212(7)
9.6.1 Ontology-Based Decision Support System (DSS)
213(1)
9.6.1.1 OntoDiabetic
213(1)
9.6.1.2 Ontology-Based CDSS for Diabetes Diagnosis
214(1)
9.6.1.3 Ontology-Based Medical DSS within E-Care Telemonitoring Platform
215(1)
9.6.2 Medical Ontology in the Dynamic Healthcare Environment
216(1)
9.6.3 Knowledge Management Systems
217(1)
9.6.3.1 Ontology-Based System for Cancer Diseases
217(1)
9.6.3.2 Personalized Care System for Chronic Patients at Home
218(1)
9.7 Ontology Engineering Applications in Other Domains
219(14)
9.7.1 Ontology Engineering Applications in E-Commerce
219(1)
9.7.1.1 Automated Approach to Product Taxonomy Mapping in E-Commerce
219(2)
9.7.1.2 LexOnt Matching Approach
221(1)
9.7.2 Ontology Engineering Applications in Social Media Domain
222(1)
9.7.2.1 Emotive Ontology Approach
222(2)
9.7.2.2 Ontology-Based Approach for Social Media Analysis
224(1)
9.7.2.3 Methodological Framework for Semantic Comparison of Emotional Values
225(1)
References
226(7)
10 Ontologies on Biomedical Informatics
233(12)
Marco Alfonse
Mariam Gawich
10.1 Introduction
233(1)
10.2 Defining Ontology
234(1)
10.3 Biomedical Ontologies and Ontology-Based Systems
235(10)
10.3.1 MetaMap
235(1)
10.3.2 GALEN
236(1)
10.3.3 NIH-CDE
236(1)
10.3.4 LOINC
237(1)
10.3.5 Current Procedural Terminology (CPT)
238(1)
10.3.6 Medline Plus Connect
238(1)
10.3.7 Gene Ontology
239(1)
10.3.8 UMLS
240(1)
10.3.9 SNOMED-CT
240(1)
10.3.10 OBO Foundry
240(1)
10.3.11 Textpresso
240(1)
10.3.12 National Cancer Institute Thesaurus
241(1)
References
241(4)
11 Machine Learning Techniques Best for Large Data Prediction: A Case Study of Breast Cancer Categorical Data: k-Nearest Neighbors
245(12)
Yagyanath Rimal
11.1 Introduction
246(4)
11.2 R Programming
250(5)
11.3 Conclusion
255(2)
References
255(2)
12 Need of Ontology-Based Systems in Healthcare System
257(18)
Tshepiso Larona Mokgetse
12.1 Introduction
258(1)
12.2 What is Ontology?
259(1)
12.3 Need for Ontology in Healthcare Systems
260(12)
12.3.1 Primary Healthcare
262(1)
12.3.1.1 Semantic Web System
262(1)
12.3.2 Emergency Services
263(1)
12.3.2.1 Service-Oriented Architecture
263(1)
12.3.2.2 IOT Ontology
264(1)
12.3.3 Public Healthcare
265(1)
12.3.3.1 IOT Data Model
265(1)
12.3.4 Chronic Disease Healthcare
266(1)
12.3.4.1 Clinical Reminder System
266(1)
12.3.4.2 Chronic Care Model
267(1)
12.3.5 Specialized Healthcare
268(1)
12.3.5.1 E-Health Record System
268(1)
12.3.5.2 Maternal and Child Health
269(1)
12.3.6 Cardiovascular System
270(1)
12.3.6.1 Distributed Healthcare System
270(1)
12.3.6.2 Records Management System
270(1)
12.3.7 Stroke Rehabilitation
271(1)
12.3.7.1 Patient Information System
271(1)
12.3.7.2 Toronto Virtual System
271(1)
12.4 Conclusion
272(3)
References
272(3)
13 Exploration of Information Retrieval Approaches With Focus on Medical Information Retrieval
275(18)
Mamata Rath
Jyotir Moy Chatterjee
13.1 Introduction
276(3)
13.1.1 Machine Learning-Based Medical Information System
278(1)
13.1.2 Cognitive Information Retrieval
278(1)
13.2 Review of Literature
279(2)
13.3 Cognitive Methods of IR
281(5)
13.4 Cognitive and Interactive IR Systems
286(2)
13.5 Conclusion
288(5)
References
289(4)
14 Ontology as a Tool to Enable Health Internet of Things Viable 5G Communication Networks
293(20)
Nidhi Sharma
R. K. Aggarwal
14.1 Introduction
293(2)
14.2 From Concept Representations to Medical Ontologies
295(2)
14.2.1 Current Medical Research Trends
296(1)
14.2.2 Ontology as a Paradigm Shift in Health Informatics
296(1)
14.3 Primer Literature Review
297(6)
14.3.1 Remote Health Monitoring
298(1)
14.3.2 Collecting and Understanding Medical Data
298(1)
14.3.3 Patient Monitoring
298(1)
14.3.4 Tele-Health
299(1)
14.3.5 Advanced Human Services Records Frameworks
299(1)
14.3.6 Applied Autonomy and Healthcare Mechanization
300(1)
14.3.7 IoT Powers the Preventive Healthcare
301(1)
14.3.8 Hospital Statistics Control System (HSCS)
301(1)
14.3.9 End-to-End Accessibility and Moderateness
301(1)
14.3.10 Information Mixing and Assessment
302(1)
14.3.11 Following and Alerts
302(1)
14.3.12 Remote Remedial Assistance
302(1)
14.4 Establishments of Health IoT
303(4)
14.4.1 Technological Challenges
304(2)
14.4.2 Probable Solutions
306(1)
14.4.3 Bit-by-Bit Action Statements
307(1)
14.5 Incubation of IoT in Health Industry
307(2)
14.5.1 Hearables
308(1)
14.5.2 Ingestible Sensors
308(1)
14.5.3 Moodables
308(1)
14.5.4 PC Vision Innovation
308(1)
14.5.5 Social Insurance Outlining
308(1)
14.6 Concluding Remarks
309(4)
References
309(4)
15 Tools and Techniques for Streaming Data: An Overview
313(18)
K. Saranya
S. Chellammal
Pethuru Raj Chelliah
15.1 Introduction
314(1)
15.2 Traditional Techniques
315(2)
15.2.1 Random Sampling
315(1)
15.2.2 Histograms
316(1)
15.2.3 Sliding Window
316(1)
15.2.4 Sketches
317(1)
15.2.4.1 Bloom Filters
317(1)
15.2.4.2 Count-Min Sketch
317(1)
15.3 Data Mining Techniques
317(3)
15.3.1 Clustering
318(1)
15.3.1.1 STREAM
318(1)
15.3.1.2 BRICH
318(1)
15.3.1.3 CLUSTREAM
319(1)
15.3.2 Classification
319(1)
15.3.2.1 Naive Bayesian
319(1)
15.3.2.2 Hoeffding
320(1)
15.3.2.3 Very Fast Decision Tree
320(1)
15.3.2.4 Concept Adaptive Very Fast Decision Tree
320(1)
15.4 Big Data Platforms
320(7)
15.4.1 Apache Storm
321(1)
15.4.2 Apache Spark
321(1)
15.4.2.1 Apache Spark Core
321(1)
15.4.2.2 Spark SQL
322(1)
15.4.2.3 Machine Learning Library
322(1)
15.4.2.4 Streaming Data API
322(1)
15.4.2.5 GraphX
323(1)
15.4.3 Apache Flume
323(1)
15.4.4 Apache Kafka
323(3)
15.4.5 Apache Flink
326(1)
15.5 Conclusion
327(4)
References
328(3)
16 An Ontology-Based IR for Health Care
331(13)
J. P. Patra
Gurudatta Verma
Sumitra Samal
16.1 Introduction
331(2)
16.2 General Definition of Information Retrieval Model
333(1)
16.3 Information Retrieval Model Based on Ontology
334(2)
16.4 Literature Survey
336(3)
16.5 Methodolgy for IR
339(5)
References 344
Vishal Jain is an associate professor at Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi, India. He has more than 350 research citation indices with Google Scholar (h-index score 9 and i-10 index 9). He has authored more than 70 research papers in reputed conferences and journals indexed by Web of Science and Scopus, as well as authored and edited more than 10 books with various international publishers. His research areas include information retrieval, semantic web, ontology engineering, data mining, adhoc networks, and sensor networks.

Ritika Wason is currently working as an associate professor at Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi. She completed her PhD degree in Computer Science from Sharda University. She has more than 10 years of teaching experience and has authored as well as edited several books in computer science and has been a recipient of many awards and honors.

Jyotir Moy Chatterjee is currently an assistant professor in the IT department at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal. He has completed M. Tech from Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha and B. Tech in Computer Science & Engineering from Dr. MGR Educational & Research Institute, Chennai. His research interests include the cloud computing, big data, privacy preservation, data mining, Internet of Things, machine learning.

Dac-Nhuong Le, PhD is the Head-Deputy of Faculty of Information Technology, Haiphong University, Vietnam. He has a total academic teaching experience of 10 years with many publications in reputed international conferences, journals and online book chapter contributions. He researches interests span the optimization and algorithmic mathematics underpinnings of network communication, security and vulnerability, network performance analysis, and cloud computing.
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