Ontology-based information retrieval for healthcare systems
著者
書誌事項
Ontology-based information retrieval for healthcare systems
（Machine learning in biomedical science and healthcare informatics / series editors, Vishal Jain and Jyotir Moy Chatterjee）
Wiley , Scrivener, 2020
- : hardback
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注記
Other editors: Ritika Wason, Jyotir Moy Chatterjee, Dac-Nhuong Le
Includes bibliographical references and index
内容説明・目次
内容説明
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
目次
Preface xix
Acknowledgment xxiii
1 Role of Ontology in Health Care 1
Sonia Singla
1.1 Introduction 2
1.2 Ontology in Diabetes 3
1.2.1 Ontology Process 4
1.2.2 Impediments of the Present Investigation 5
1.3 Role of Ontology in Cardiovascular Diseases 6
1.4 Role of Ontology in Parkinson Diseases 8
1.4.1 The Spread of Disease With Age and Onset of Disease 10
1.4.2 Cost of PD for Health Care, Household 11
1.4.3 Treatment and Medicines 11
1.5 Role of Ontology in Depression 13
1.6 Conclusion 15
1.7 Future Scope 15
References 15
2 A Study on Basal Ganglia Circuit and Its Relation With Movement Disorders 19
Dinesh Bhatia
2.1 Introduction 19
2.2 Anatomy and Functioning of Basal Ganglia 21
2.2.1 The Striatum-Major Entrance to Basal Ganglia Circuitry 22
2.2.2 Direct and Indirect Striatofugal Projections 23
2.2.3 The STN: Another Entrance to Basal Ganglia Circuitry 25
2.3 Movement Disorders 26
2.3.1 Parkinson Disease 26
2.3.2 Dyskinetic Disorder 27
2.3.3 Dystonia 28
2.4 Effect of Basal Ganglia Dysfunctioning on Movement Disorders 29
2.5 Conclusion and Future Scope 31
References 31
3 Extraction of Significant Association Rules Using Pre- and Post-Mining Techniques-An Analysis 37
M. Nandhini and S. N. Sivanandam
3.1 Introduction 38
3.2 Background 39
3.2.1 Interestingness Measures 39
3.2.2 Pre-Mining Techniques 40
3.2.2.1 Candidate Set Reduction Schemes 40
3.2.2.2 Optimal Threshold Computation Schemes 41
3.2.2.3 Weight-Based Mining Schemes 42
3.2.3 Post-Mining Techniques 42
3.2.3.1 Rule Pruning Schemes 43
3.2.3.2 Schemes Using Knowledge Base 43
3.3 Methodology 44
3.3.1 Data Preprocessing 44
3.3.2 Pre-Mining 46
3.3.2.1 Pre-Mining Technique 1: Optimal Support and Confidence Threshold Value Computation Using PSO 46
3.3.2.2 Pre-Mining Technique 2: Attribute Weight Computation Using IG Measure 48
3.3.3 Association Rule Generation 50
3.3.3.1 ARM Preliminaries 50
3.3.3.2 WARM Preliminaries 52
3.3.4 Post-Mining 56
3.3.4.1 Filters 56
3.3.4.2 Operators 58
3.3.4.3 Rule Schemas 58
3.4 Experiments and Results 59
3.4.1 Parameter Settings for PSO-Based Pre-Mining Technique 60
3.4.2 Parameter Settings for PAW-Based Pre-Mining Technique 60
3.5 Conclusions 63
References 65
4 Ontology in Medicine as a Database Management System 69
Shobowale K. O.
4.1 Introduction 70
4.1.1 Ontology Engineering and Development Methodology 72
4.2 Literature Review on Medical Data Processing 72
4.3 Information on Medical Ontology 75
4.3.1 Types of Medical Ontology 75
4.3.2 Knowledge Representation 76
4.3.3 Methodology of Developing Medical Ontology 76
4.3.4 Medical Ontology Standards 77
4.4 Ontologies as a Knowledge-Based System 78
4.4.1 Domain Ontology in Medicine 79
4.4.2 Brief Introduction of Some Medical Standards 81
4.4.2.1 Medical Subject Headings (MeSH) 81
4.4.2.2 Medical Dictionary for Regulatory Activities (MedDRA) 81
4.4.2.3 Medical Entities Dictionary (MED) 81
4.4.3 Reusing Medical Ontology 82
4.4.4 Ontology Evaluation 85
4.5 Conclusion 86
4.6 Future Scope 86
References 87
5 Using IoT and Semantic Web Technologies for Healthcare and Medical Sector 91
Nikita Malik and Sanjay Kumar Malik
5.1 Introduction 92
5.1.1 Significance of Healthcare and Medical Sector and Its Digitization 92
5.1.2 e-Health and m-Health 92
5.1.3 Internet of Things and Its Use 94
5.1.4 Semantic Web and Its Technologies 96
5.2 Use of IoT in Healthcare and Medical Domain 98
5.2.1 Scope of IoT in Healthcare and Medical Sector 98
5.2.2 Benefits of IoT in Healthcare and Medical Systems 100
5.2.3 IoT Healthcare Challenges and Open Issues 100
5.3 Role of SWTs in Healthcare Services 101
5.3.1 Scope and Benefits of Incorporating Semantics in Healthcare 101
5.3.2 Ontologies and Datasets for Healthcare and Medical Domain 103
5.3.3 Challenges in the Use of SWTs in Healthcare Sector 104
5.4 Incorporating IoT and/or SWTs in Healthcare and Medical Sector 106
5.4.1 Proposed Architecture or Framework or Model 106
5.4.2 Access Mechanisms or Approaches 108
5.4.3 Applications or Systems 109
5.5 Healthcare Data Analytics Using Data Mining and Machine Learning 110
5.6 Conclusion 112
5.7 Future Work 113
References 113
6 An Ontological Model, Design, and Implementation of CSPF for Healthcare 117
Pooja Mohan
6.1 Introduction 117
6.2 Related Work 119
6.3 Mathematical Representation of CSPF Model 122
6.3.1 Basic Sets of CSPF Model 123
6.3.2 Conditional Contextual Security and Privacy Constraints 123
6.3.3 CSPF Model States CsetofStates 124
6.3.4 Permission Cpermission 124
6.3.5 Security Evaluation Function (SEFcontexts) 124
6.3.6 Secure State 125
6.3.7 CSPF Model Operations 125
6.3.7.1 Administrative Operations 125
6.3.7.2 Users' Operations 127
6.4 Ontological Model 127
6.4.1 Development of Class Hierarchy 127
6.4.1.1 Object Properties of Sensor Class 129
6.4.1.2 Data Properties 129
6.4.1.3 The Individuals 129
6.5 The Design of Context-Aware Security and Privacy Model for Wireless Sensor Network 129
6.6 Implementation 133
6.7 Analysis and Results 135
6.7.1 Inference Time/Latency/Query Response Time vs. No. of Policies 135
6.7.2 Average Inference Time vs. Contexts 136
6.8 Conclusion and Future Scope 137
References 138
7 Ontology-Based Query Retrieval Support for E-Health Implementation 143
Aatif Ahmad Khan and Sanjay Kumar Malik
7.1 Introduction 143
7.1.1 Health Care Record Management 144
7.1.1.1 Electronic Health Record 144
7.1.1.2 Electronic Medical Record 145
7.1.1.3 Picture Archiving and Communication System 145
7.1.1.4 Pharmacy Systems 145
7.1.2 Information Retrieval 145
7.1.3 Ontology 146
7.2 Ontology-Based Query Retrieval Support 146
7.3 E-Health 150
7.3.1 Objectives and Scope 150
7.3.2 Benefits of E-Health 151
7.3.3 E-Health Implementation 151
7.4 Ontology-Driven Information Retrieval for E-Health 154
7.4.1 Ontology for E-Heath Implementation 155
7.4.2 Frameworks for Information Retrieval Using Ontology for E-Health 157
7.4.3 Applications of Ontology-Driven Information Retrieval in Health Care 158
7.4.4 Benefits and Limitations 160
7.5 Discussion 160
7.6 Conclusion 164
References 164
8 Ontology-Based Case Retrieval in an E-Mental Health Intelligent Information System 167
Georgia Kaoura, Konstantinos Kovas and Basilis Boutsinas
8.1 Introduction 167
8.2 Literature Survey 170
8.3 Problem Identified 173
8.4 Proposed Solution 174
8.4.1 The PAVEFS Ontology 174
8.4.2 Knowledge Base 179
8.4.3 Reasoning 180
8.4.4 User Interaction 182
8.5 Pros and Cons of Solution 183
8.5.1 Evaluation Methodology and Results 183
8.5.2 Evaluation Methodology 185
8.5.2.1 Evaluation Tools 186
8.5.2.2 Results 187
8.6 Conclusions 189
8.7 Future Scope 190
References 190
9 Ontology Engineering Applications in Medical Domain 193
Mariam Gawich and Marco Alfonse
9.1 Introduction 193
9.2 Ontology Activities 195
9.2.1 Ontology Learning 195
9.2.2 Ontology Matching 195
9.2.3 Ontology Merging (Unification) 195
9.2.4 Ontology Validation 196
9.2.5 Ontology Verification 196
9.2.6 Ontology Alignment 196
9.2.7 Ontology Annotation 196
9.2.8 Ontology Evaluation 196
9.2.9 Ontology Evolution 196
9.3 Ontology Development Methodologies 197
9.3.1 TOVE 197
9.3.2 Methontology 198
9.3.3 Brusa et al. Methodology 198
9.3.4 UPON Methodology 199
9.3.5 Uschold and King Methodology 200
9.4 Ontology Languages 203
9.4.1 RDF-RDF Schema 203
9.4.2 OWL 205
9.4.3 OWL 2 205
9.5 Ontology Tools 208
9.5.1 Apollo 208
9.5.2 NeON 209
9.5.3 Protege 210
9.6 Ontology Engineering Applications in Medical Domain 212
9.6.1 Ontology-Based Decision Support System (DSS) 213
9.6.1.1 OntoDiabetic 213
9.6.1.2 Ontology-Based CDSS for Diabetes Diagnosis 214
9.6.1.3 Ontology-Based Medical DSS within E-Care Telemonitoring Platform 215
9.6.2 Medical Ontology in the Dynamic Healthcare Environment 216
9.6.3 Knowledge Management Systems 217
9.6.3.1 Ontology-Based System for Cancer Diseases 217
9.6.3.2 Personalized Care System for Chronic Patients at Home 218
9.7 Ontology Engineering Applications in Other Domains 219
9.7.1 Ontology Engineering Applications in E-Commerce 219
9.7.1.1 Automated Approach to Product Taxonomy Mapping in E-Commerce 219
9.7.1.2 LexOnt Matching Approach 221
9.7.2 Ontology Engineering Applications in Social Media Domain 222
9.7.2.1 Emotive Ontology Approach 222
9.7.2.2 Ontology-Based Approach for Social Media Analysis 224
9.7.2.3 Methodological Framework for Semantic Comparison of Emotional Values 225
References 226
10 Ontologies on Biomedical Informatics 233
Marco Alfonse and Mariam Gawich
10.1 Introduction 233
10.2 Defining Ontology 234
10.3 Biomedical Ontologies and Ontology-Based Systems 235
10.3.1 MetaMap 235
10.3.2 GALEN 236
10.3.3 NIH-CDE 236
10.3.4 LOINC 237
10.3.5 Current Procedural Terminology (CPT) 238
10.3.6 Medline Plus Connect 238
10.3.7 Gene Ontology 239
10.3.8 UMLS 240
10.3.9 SNOMED-CT 240
10.3.10 OBO Foundry 240
10.3.11 Textpresso 240
10.3.12 National Cancer Institute Thesaurus 241
References 241
11 Machine Learning Techniques Best for Large Data Prediction: A Case Study of Breast Cancer Categorical Data: k-Nearest Neighbors 245
Yagyanath Rimal
11.1 Introduction 246
11.2 R Programming 250
11.3 Conclusion 255
References 255
12 Need of Ontology-Based Systems in Healthcare System 257
Tshepiso Larona Mokgetse
12.1 Introduction 258
12.2 What is Ontology? 259
12.3 Need for Ontology in Healthcare Systems 260
12.3.1 Primary Healthcare 262
12.3.1.1 Semantic Web System 262
12.3.2 Emergency Services 263
12.3.2.1 Service-Oriented Architecture 263
12.3.2.2 IOT Ontology 264
12.3.3 Public Healthcare 265
12.3.3.1 IOT Data Model 265
12.3.4 Chronic Disease Healthcare 266
12.3.4.1 Clinical Reminder System 266
12.3.4.2 Chronic Care Model 267
12.3.5 Specialized Healthcare 268
12.3.5.1 E-Health Record System 268
12.3.5.2 Maternal and Child Health 269
12.3.6 Cardiovascular System 270
12.3.6.1 Distributed Healthcare System 270
12.3.6.2 Records Management System 270
12.3.7 Stroke Rehabilitation 271
12.3.7.1 Patient Information System 271
12.3.7.2 Toronto Virtual System 271
12.4 Conclusion 272
References 272
13 Exploration of Information Retrieval Approaches With Focus on Medical Information Retrieval 275
Mamata Rath and Jyotir Moy Chatterjee
13.1 Introduction 276
13.1.1 Machine Learning-Based Medical Information System 278
13.1.2 Cognitive Information Retrieval 278
13.2 Review of Literature 279
13.3 Cognitive Methods of IR 281
13.4 Cognitive and Interactive IR Systems 286
13.5 Conclusion 288
References 289
14 Ontology as a Tool to Enable Health Internet of Things Viable 5G Communication Networks 293
Nidhi Sharma and R. K. Aggarwal
14.1 Introduction 293
14.2 From Concept Representations to Medical Ontologies 295
14.2.1 Current Medical Research Trends 296
14.2.2 Ontology as a Paradigm Shift in Health Informatics 296
14.3 Primer Literature Review 297
14.3.1 Remote Health Monitoring 298
14.3.2 Collecting and Understanding Medical Data 298
14.3.3 Patient Monitoring 298
14.3.4 Tele-Health 299
14.3.5 Advanced Human Services Records Frameworks 299
14.3.6 Applied Autonomy and Healthcare Mechanization 300
14.3.7 IoT Powers the Preventive Healthcare 301
14.3.8 Hospital Statistics Control System (HSCS) 301
14.3.9 End-to-End Accessibility and Moderateness 301
14.3.10 Information Mixing and Assessment 302
14.3.11 Following and Alerts 302
14.3.12 Remote Remedial Assistance 302
14.4 Establishments of Health IoT 303
14.4.1 Technological Challenges 304
14.4.2 Probable Solutions 306
14.4.3 Bit-by-Bit Action Statements 307
14.5 Incubation of IoT in Health Industry 307
14.5.1 Hearables 308
14.5.2 Ingestible Sensors 308
14.5.3 Moodables 308
14.5.4 PC Vision Innovation 308
14.5.5 Social Insurance Outlining 308
14.6 Concluding Remarks 309
References 309
15 Tools and Techniques for Streaming Data: An Overview 313
K. Saranya, S. Chellammal and Pethuru Raj Chelliah
15.1 Introduction 314
15.2 Traditional Techniques 315
15.2.1 Random Sampling 315
15.2.2 Histograms 316
15.2.3 Sliding Window 316
15.2.4 Sketches 317
15.2.4.1 Bloom Filters 317
15.2.4.2 Count-Min Sketch 317
15.3 Data Mining Techniques 317
15.3.1 Clustering 318
15.3.1.1 STREAM 318
15.3.1.2 BRICH 318
15.3.1.3 CLUSTREAM 319
15.3.2 Classification 319
15.3.2.1 Naive Bayesian 319
15.3.2.2 Hoeffding 320
15.3.2.3 Very Fast Decision Tree 320
15.3.2.4 Concept Adaptive Very Fast Decision Tree 320
15.4 Big Data Platforms 320
15.4.1 Apache Storm 321
15.4.2 Apache Spark 321
15.4.2.1 Apache Spark Core 321
15.4.2.2 Spark SQL 322
15.4.2.3 Machine Learning Library 322
15.4.2.4 Streaming Data API 322
15.4.2.5 GraphX 323
15.4.3 Apache Flume 323
15.4.4 Apache Kafka 323
15.4.5 Apache Flink 326
15.5 Conclusion 327
References 328
16 An Ontology-Based IR for Health Care 331
J. P. Patra, Gurudatta Verma and Sumitra Samal
16.1 Introduction 331
16.2 General Definition of Information Retrieval Model 333
16.3 Information Retrieval Model Based on Ontology 334
16.4 Literature Survey 336
16.5 Methodolgy for IR 339
References 344
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