Ontology-Based Information Retrieval for Healthcare Systems

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.

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 Protégé 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 Naïve 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