Semantic Web for Effective Healthcare Systems
1. Auflage Dezember 2021
352 Seiten, Hardcover
Wiley & Sons Ltd
ISBN:
978-1-119-76229-4
John Wiley & Sons
Weitere Versionen
Ziel dieses Buchs ist eine Analyse, wie das Semantic Web nach aktuellem Stand eingesetzt wird, um das Problem der Integration und Interoperabilität von Gesundheitsdaten zu lösen, wobei es fortschrittliche Möglichkeiten zur Datenverknüpfung bietet, um die Suche nach und das Abrufen von medizinischen Daten zu verbessern. In einigen Kapiteln werden die Instrumente und Ansätze zur semantischen Analyse von Gesundheitsdaten und zur Wissenserschließung analysiert. Außerdem wird die Rolle der semantischen Technologien bei der Extraktion und Umwandlung von Gesundheitsdaten vor der Speicherung in Datenpools betrachtet. Auch unterschiedliche Ansätze für die Integration heterogener Gesundheitsdaten werden erörtert. Insgesamt hilft das Buch den Leserinnen und Lesern, die wichtigsten Konzepte von Semantic-Web-Anwendungen im Bereich der Biomedizintechnik und des Gesundheitswesens zu verstehen.
Preface xv
Acknowledgment xix
1 An Ontology-Based Contextual Data Modeling for Process Improvement in Healthcare 1
A. M. Abirami and A. Askarunisa
1.1 Introduction 1
1.1.1 Ontology-Based Information Extraction 3
1.1.2 Ontology-Based Knowledge Representation 4
1.2 Related Work 5
1.3 Motivation 8
1.4 Feature Extraction 9
1.4.1 Vector Space Model 10
1.4.2 Latent Semantic Indexing (LSI) 11
1.4.3 Clustering Techniques 12
1.4.4 Topic Modeling 12
1.5 Ontology Development 17
1.5.1 Ontology-Based Semantic Indexing (OnSI) Model 17
1.5.2 Ontology Development 18
1.5.3 OnSI Model Evaluation 19
1.5.4 Metrics Analysis 23
1.6 Dataset Description 24
1.7 Results and Discussions 25
1.7.1 Discussion 1 29
1.7.2 Discussion 2 29
1.7.3 Discussion 3 30
1.8 Applications 31
1.9 Conclusion 32
1.10 Future Work 33
References 33
2 Semantic Web for Effective Healthcare Systems: Impact and Challenges 39
Hemendra Shankar Sharma and Ashish Sharma
2.1 Introduction 40
2.2 Overview of the Website in Healthcare 45
2.2.1 What is Website? 45
2.2.2 Types of Website 45
2.2.2.1 Static Website 45
2.2.2.2 Dynamic Website 46
2.2.3 What is Semantic Web? 46
2.2.4 Role of Semantic Web 47
2.2.4.1 Pros and Cons of Semantic Web 49
2.2.4.2 Impact on Patient 51
2.2.4.3 Impact on Practitioner 52
2.2.4.4 Impact on Researchers 52
2.3 Data and Database 53
2.3.1 What is Data? 54
2.3.2 What is Database? 54
2.3.3 Source of Data in the Healthcare System 54
2.3.3.1 Electronic Health Record (EHR) 55
2.3.3.2 Biomedical Image Analysis 56
2.3.3.3 Sensor Data Analysis 57
2.3.3.4 Genomic Data Analysis 57
2.3.3.5 Clinical Text Mining 58
2.3.3.6 Social Media 59
2.3.4 Why Are Databases Important? 60
2.3.5 Challenges With the Database in the Healthcare System 61
2.4 Big Data and Database Security and Protection 61
2.4.1 What is Big Data 61
2.4.2 Five V's of Big Data 62
2.4.2.1 Volume 62
2.4.2.2 Variety 63
2.4.2.3 Velocity 63
2.4.2.4 Veracity 64
2.4.2.5 Value 65
2.4.3 Architectural Framework of Big Data 65
2.4.4 Data Protection Versus Data Security in Healthcare 67
2.4.4.1 Phishing Attacks 67
2.4.4.2 Malware and Ransomware 67
2.4.4.3 Cloud Threats 67
2.4.5 Technology in Use to Secure the Healthcare Data 68
2.4.5.1 Access Control Policy 69
2.4.6 Monitoring and Auditing 69
2.4.7 Standard for Data Protection 70
2.4.7.1 Healthcare Standard in India 70
2.4.7.2 Security Technical Standards 71
2.4.7.3 Administrative Safeguards Standards 71
2.4.7.4 Physical Safeguard Standards 71
References 71
3 Ontology-Based System for Patient Monitoring 75
R. Mervin, Tintu Thomas and A. Jaya
3.1 Introduction 76
3.1.1 Basics of Ontology 77
3.1.2 Need of Ontology in Patient Monitoring 78
3.2 Literature Review 78
3.2.1 Uses of Ontology in Various Domains 78
3.2.2 Ontology in Patient Monitoring System 80
3.3 Architectural Design 80
3.3.1 Phases of Patient Monitoring System 82
3.3.2 Reasoner in Patient Monitoring 87
3.4 Experimental Results 88
3.4.1 SPARQL Results 89
3.4.2 Comparison Between Other Systems 89
3.5 Conclusion and Future Enhancements 90
References 91
4 Semantic Web Solutions for Improvised Search in Healthcare Systems 95
Nidhi Malik, Aditi Sharan and Sadika Verma
4.1 Introduction 95
4.1.1 Key Benefits and Usage of Technology in Healthcare System 96
4.2 Background 97
4.2.1 Significance of Semantics in Healthcare Systems 97
4.2.2 Scope and Benefits of Semantics in Healthcare Systems 98
4.2.3 Issues in Incorporating Semantics 98
4.2.4 Existing Semantic Web Technologies 99
4.3 Searching Techniques in Healthcare Systems 100
4.3.1 Keyword-Based Search 100
4.3.2 Controlled Vocabularies Based Search 101
4.3.3 Improvising Searches With Semantic Web Solutions 101
4.3.4 Health Domain-Specific Resources for Semantic Search 102
4.3.4.1 Ontologies 103
4.3.4.2 Libraries 103
4.3.4.3 Search Engines 103
4.4 Emerging Technologies/Resources in Health Sector 108
4.4.1 Elasticsearch 109
4.4.2 BioBERT 109
4.4.3 Knowledge Graphs 110
4.5 Conclusion 110
References 111
5 Actionable Content Discovery for Healthcare 115
Ujwala Bharambe and Anuradha Srinivasaraghavan
5.1 Introduction 116
5.2 Actionable Content 117
5.2.1 Actionable Content in Theory 117
5.2.2 Actionable Content in Practice 122
5.3 Health Analytics 124
5.3.1 Artificial Intelligence/Machine Learning-Based Predictive Analytics 125
5.3.2 Semantic Technology for Prescriptive Health Analytics 126
5.4 Ontologies and Actionable Content 127
5.4.1 Ontologies in Healthcare Domain 129
5.5 General Architecture for the Discovery of Actionable Content for Healthcare Domain 130
5.5.1 Ontology-Driven Actionable Content Discovery in Healthcare Domain 131
5.5.2 Case Study for Actionable Content Discovery in Cancer Domain 134
5.6 Conclusion 136
References 136
6 Intelligent Agent System Using Medicine Ontology 139
Tintu Thomas and R. Mervin
6.1 Introduction to Semantic Search 140
6.1.1 What is an Ontology in Terms of Medicine? 140
6.1.2 Needs and Benefits of Ontology in Medical Search 141
6.2 Sematic Search 142
6.2.1 How NLP Works in Sematic Search? 142
6.2.2 Part of Speech Tagging and Chunking 142
6.2.3 Sentence Parsing 143
6.2.4 Discussion About the Various Semantic Search in Medical Databases 144
6.2.5 Discussion About the Retrieval Tools Used in Sematic Search in Medline 145
6.3 Structural Pattern of Semantic Search 146
6.3.1 Architectural Diagram 147
6.3.2 Agent Ontology 148
6.3.3 Rule-Based Approach 149
6.3.4 Reasoners-Based Approach 151
6.4 Implementation of Reasoners 152
6.5 Implementation and Results 153
6.6 Conclusion and Future Prospective 153
References 154
7 Ontology-Based System for Robotic Surgery--A Historical Analysis 159
Ajay Agarwal and Amit Kumar Mishra
7.1 Historical Discourse of Surgical Robots 160
7.2 The Necessity for Surgical Robots 162
7.3 Ontological Evolution of Robotic Surgical Procedures in Various Domains 163
7.4 Inferences Drawn From the Table 164
7.5 Transoral Robotic Surgery 166
7.6 Pancreatoduodenectomy 167
7.7 Robotic Mitral Valve Surgery 168
7.8 Rectal Tumor Surgery 170
7.9 Robotic Lung Cancer Surgery 170
7.10 Robotic Surgery in Gynecology 171
7.11 Robotic Radical Prostatectomy 171
7.12 Conclusion 172
7.13 Future Work 172
References 172
8 IoT-Enabled Effective Healthcare Monitoring System Using Semantic Web 175
Sapna Juneja, Abhinav Juneja, Annu Dhankhar and Vishal Jain
8.1 Introduction 176
8.2 Literature Review 177
8.3 Phases of IoT-Based Healthcare 178
8.4 IoT-Based Healthcare Architecture 179
8.5 IoT-Based Sensors for Health Monitoring 180
8.6 IoT Applications in Healthcare 182
8.7 Semantic Web, Ontology, and Its Usage in Healthcare Sector 183
8.8 Semantic Web-Based IoT Healthcare 183
8.9 Challenges of IoT in Healthcare Industry 185
8.10 Conclusion 186
References 186
9 Precision Medicine in the Context of Ontology 191
Rehab A. Rayan and Imran Zafar
9.1 Introduction 192
9.2 The Rationale Behind Data 195
9.3 Data Standards for Interoperability 197
9.4 The Evolution of Ontology 198
9.5 Ontologies and Classifying Disorders 199
9.6 Phenotypic Ontology of Humans in Rare Disorders 201
9.7 Annotations and Ontology Integration 202
9.8 Precision Annotation and Integration 203
9.9 Ontology in the Contexts of Gene Identification Research 204
9.10 Personalizing Care for Chronic Illness 207
9.11 Roadblocks Toward Precision Medicine 208
9.12 Future Perspectives 209
9.13 Conclusion 209
References 210
10 A Knowledgebase Model Using RDF Knowledge Graph for Clinical Decision Support Systems 215
Ravi Lourdusamy and Xavierlal J. Mattam
10.1 Introduction 216
10.2 Relational Database to Graph Database 217
10.2.1 Relational Database for Knowledge Representation 218
10.2.2 NoSQL Databases 220
10.2.3 Graph Database 223
10.3 RDF 225
10.3.1 RDF Model and Technology 226
10.3.2 Metadata and URI 226
10.3.3 RDF Stores 228
10.4 Knowledgebase Systems and Knowledge Graphs 230
10.4.1 Knowledgebase Systems 230
10.4.2 Knowledge Graphs 232
10.4.3 RDF Knowledge Graphs 233
10.4.4 Information Retrieval Using SPARQL 234
10.5 Knowledge Base for CDSS 235
10.5.1 Curation of Knowledge Base for CDSS 236
10.5.2 Proposed Model for Curation 236
10.5.3 Evaluation Methodology 238
10.6 Discussion for Further Research and Development 239
10.7 Conclusion 239
References 240
11 Medical Data Supervised Learning Ontologies for Accurate Data Analysis 249
B. Tarakeswara Rao, R. S. M. Lakshmi Patibandla, V. Lakshman Narayana and Arepalli Peda Gopi
11.1 Introduction 250
11.2 Ontology of Biomedicine 251
11.2.1 Ontology Resource Open Sharing 254
11.3 Supervised Learning 255
11.4 AQ21 Rule in Machine Learning 256
11.5 Unified Medical Systems 259
11.5.1 Note of Relevance to Bioinformatic Experts 259
11.5.2 Terminological Incorporation Principles 260
11.5.3 Cross-References External 261
11.5.4 UMLS Data Access 262
11.6 Performance Analysis 262
11.7 Conclusion 265
References 265
12 Rare Disease Diagnosis as Information Retrieval Task 269
Jaya Lakkakula, Rutuja Phate, Alfiya Korbu and Sagar Barage
12.1 Introduction 270
12.2 Definition 271
12.3 Characteristics of Rare Diseases (RDs) 272
12.4 Types of Rare Diseases 273
12.4.1 Genetic Causes 274
12.4.2 Non-Genetic Causes 275
12.4.3 Pathogenic Causes (Infectious Agents) 275
12.4.4 Toxic Agents 275
12.4.5 Other Causes 276
12.5 A Brief Classification 276
12.6 Rare Disease Databases and Online Resources 277
12.6.1 European Reference Network: ERN 277
12.6.2 Genetic and Rare Diseases Information Center: GARD 278
12.6.3 International Classification of Diseases, 10th Revision: ICD-10 279
12.6.4 Orphanet-INSERM (Institut National de la Santé et de la Recherche Médicale) 280
12.6.5 Medical Dictionary for Regulatory Activities: MedDRA 280
12.6.6 Medical Subject Headings: MeSH 281
12.6.7 Online Mendelian Inheritance in Man: OMIM 282
12.6.8 Orphanet Rare Disease Ontology: ORDO 282
12.6.9 UMLS: Unified Medical Language System 282
12.6.10 SNOMED-CT: Systematized Nomenclature of Human and Veterinary Medicine--Clinical Terms 283
12.7 Information Retrieval of Rare Diseases Through a Web Search and Other Methods 284
12.7.1 What is Information Retrieval (IR)? 284
12.7.2 Listed Below Are Some of the Methods for Information Retrieval 284
12.7.2.1 Web Search for a Diagnosis 284
12.7.2.2 Cause of Diagnostic Errors in Web-Based Tools 285
12.7.2.3 Nonprofessional Use of Web Tool for Diagnosis 285
12.7.2.4 Performance of Web Search Tools 285
12.7.2.5 Design of Watson 286
12.8 Tips and Tricks for Information Retrieval 287
12.9 Research on Rare Disease Throughout the World 288
12.10 Conclusion 290
References 290
13 Atypical Point of View of Semantic Computing in Healthcare 293
L. Mayuri and K. M. Mehata
13.1 Introduction 294
13.2 Mind the Language 295
13.2.1 Why Words Matter 296
13.2.2 What Words Matter 296
13.2.3 How Words Matter 297
13.3 Semantic Analytics and Cognitive Computing: Recent Trends 297
13.3.1 Semantic Data Analysis 298
13.3.2 Semantic Data Integration 299
13.3.3 Semantic Applications 300
13.4 Semantics-Powered Healthcare SOS Engineering 302
13.5 Conclusion 303
References 304
14 Using Artificial Intelligence to Help COVID-19 Patients 309
Ayush Hans
14.1 Introduction 310
14.2 Method 313
14.3 Results 314
14.4 Discussion 315
14.4.1 What is the Use of AI in Healthcare? 315
14.4.2 How to Use AI for Critical Care Units 315
14.4.2.1 Input Stage 315
14.4.2.2 Process Stage 316
14.4.2.3 Output Stage 317
14.5 Conclusion 320
Acknowledgment 321
References 321
Index 325
Acknowledgment xix
1 An Ontology-Based Contextual Data Modeling for Process Improvement in Healthcare 1
A. M. Abirami and A. Askarunisa
1.1 Introduction 1
1.1.1 Ontology-Based Information Extraction 3
1.1.2 Ontology-Based Knowledge Representation 4
1.2 Related Work 5
1.3 Motivation 8
1.4 Feature Extraction 9
1.4.1 Vector Space Model 10
1.4.2 Latent Semantic Indexing (LSI) 11
1.4.3 Clustering Techniques 12
1.4.4 Topic Modeling 12
1.5 Ontology Development 17
1.5.1 Ontology-Based Semantic Indexing (OnSI) Model 17
1.5.2 Ontology Development 18
1.5.3 OnSI Model Evaluation 19
1.5.4 Metrics Analysis 23
1.6 Dataset Description 24
1.7 Results and Discussions 25
1.7.1 Discussion 1 29
1.7.2 Discussion 2 29
1.7.3 Discussion 3 30
1.8 Applications 31
1.9 Conclusion 32
1.10 Future Work 33
References 33
2 Semantic Web for Effective Healthcare Systems: Impact and Challenges 39
Hemendra Shankar Sharma and Ashish Sharma
2.1 Introduction 40
2.2 Overview of the Website in Healthcare 45
2.2.1 What is Website? 45
2.2.2 Types of Website 45
2.2.2.1 Static Website 45
2.2.2.2 Dynamic Website 46
2.2.3 What is Semantic Web? 46
2.2.4 Role of Semantic Web 47
2.2.4.1 Pros and Cons of Semantic Web 49
2.2.4.2 Impact on Patient 51
2.2.4.3 Impact on Practitioner 52
2.2.4.4 Impact on Researchers 52
2.3 Data and Database 53
2.3.1 What is Data? 54
2.3.2 What is Database? 54
2.3.3 Source of Data in the Healthcare System 54
2.3.3.1 Electronic Health Record (EHR) 55
2.3.3.2 Biomedical Image Analysis 56
2.3.3.3 Sensor Data Analysis 57
2.3.3.4 Genomic Data Analysis 57
2.3.3.5 Clinical Text Mining 58
2.3.3.6 Social Media 59
2.3.4 Why Are Databases Important? 60
2.3.5 Challenges With the Database in the Healthcare System 61
2.4 Big Data and Database Security and Protection 61
2.4.1 What is Big Data 61
2.4.2 Five V's of Big Data 62
2.4.2.1 Volume 62
2.4.2.2 Variety 63
2.4.2.3 Velocity 63
2.4.2.4 Veracity 64
2.4.2.5 Value 65
2.4.3 Architectural Framework of Big Data 65
2.4.4 Data Protection Versus Data Security in Healthcare 67
2.4.4.1 Phishing Attacks 67
2.4.4.2 Malware and Ransomware 67
2.4.4.3 Cloud Threats 67
2.4.5 Technology in Use to Secure the Healthcare Data 68
2.4.5.1 Access Control Policy 69
2.4.6 Monitoring and Auditing 69
2.4.7 Standard for Data Protection 70
2.4.7.1 Healthcare Standard in India 70
2.4.7.2 Security Technical Standards 71
2.4.7.3 Administrative Safeguards Standards 71
2.4.7.4 Physical Safeguard Standards 71
References 71
3 Ontology-Based System for Patient Monitoring 75
R. Mervin, Tintu Thomas and A. Jaya
3.1 Introduction 76
3.1.1 Basics of Ontology 77
3.1.2 Need of Ontology in Patient Monitoring 78
3.2 Literature Review 78
3.2.1 Uses of Ontology in Various Domains 78
3.2.2 Ontology in Patient Monitoring System 80
3.3 Architectural Design 80
3.3.1 Phases of Patient Monitoring System 82
3.3.2 Reasoner in Patient Monitoring 87
3.4 Experimental Results 88
3.4.1 SPARQL Results 89
3.4.2 Comparison Between Other Systems 89
3.5 Conclusion and Future Enhancements 90
References 91
4 Semantic Web Solutions for Improvised Search in Healthcare Systems 95
Nidhi Malik, Aditi Sharan and Sadika Verma
4.1 Introduction 95
4.1.1 Key Benefits and Usage of Technology in Healthcare System 96
4.2 Background 97
4.2.1 Significance of Semantics in Healthcare Systems 97
4.2.2 Scope and Benefits of Semantics in Healthcare Systems 98
4.2.3 Issues in Incorporating Semantics 98
4.2.4 Existing Semantic Web Technologies 99
4.3 Searching Techniques in Healthcare Systems 100
4.3.1 Keyword-Based Search 100
4.3.2 Controlled Vocabularies Based Search 101
4.3.3 Improvising Searches With Semantic Web Solutions 101
4.3.4 Health Domain-Specific Resources for Semantic Search 102
4.3.4.1 Ontologies 103
4.3.4.2 Libraries 103
4.3.4.3 Search Engines 103
4.4 Emerging Technologies/Resources in Health Sector 108
4.4.1 Elasticsearch 109
4.4.2 BioBERT 109
4.4.3 Knowledge Graphs 110
4.5 Conclusion 110
References 111
5 Actionable Content Discovery for Healthcare 115
Ujwala Bharambe and Anuradha Srinivasaraghavan
5.1 Introduction 116
5.2 Actionable Content 117
5.2.1 Actionable Content in Theory 117
5.2.2 Actionable Content in Practice 122
5.3 Health Analytics 124
5.3.1 Artificial Intelligence/Machine Learning-Based Predictive Analytics 125
5.3.2 Semantic Technology for Prescriptive Health Analytics 126
5.4 Ontologies and Actionable Content 127
5.4.1 Ontologies in Healthcare Domain 129
5.5 General Architecture for the Discovery of Actionable Content for Healthcare Domain 130
5.5.1 Ontology-Driven Actionable Content Discovery in Healthcare Domain 131
5.5.2 Case Study for Actionable Content Discovery in Cancer Domain 134
5.6 Conclusion 136
References 136
6 Intelligent Agent System Using Medicine Ontology 139
Tintu Thomas and R. Mervin
6.1 Introduction to Semantic Search 140
6.1.1 What is an Ontology in Terms of Medicine? 140
6.1.2 Needs and Benefits of Ontology in Medical Search 141
6.2 Sematic Search 142
6.2.1 How NLP Works in Sematic Search? 142
6.2.2 Part of Speech Tagging and Chunking 142
6.2.3 Sentence Parsing 143
6.2.4 Discussion About the Various Semantic Search in Medical Databases 144
6.2.5 Discussion About the Retrieval Tools Used in Sematic Search in Medline 145
6.3 Structural Pattern of Semantic Search 146
6.3.1 Architectural Diagram 147
6.3.2 Agent Ontology 148
6.3.3 Rule-Based Approach 149
6.3.4 Reasoners-Based Approach 151
6.4 Implementation of Reasoners 152
6.5 Implementation and Results 153
6.6 Conclusion and Future Prospective 153
References 154
7 Ontology-Based System for Robotic Surgery--A Historical Analysis 159
Ajay Agarwal and Amit Kumar Mishra
7.1 Historical Discourse of Surgical Robots 160
7.2 The Necessity for Surgical Robots 162
7.3 Ontological Evolution of Robotic Surgical Procedures in Various Domains 163
7.4 Inferences Drawn From the Table 164
7.5 Transoral Robotic Surgery 166
7.6 Pancreatoduodenectomy 167
7.7 Robotic Mitral Valve Surgery 168
7.8 Rectal Tumor Surgery 170
7.9 Robotic Lung Cancer Surgery 170
7.10 Robotic Surgery in Gynecology 171
7.11 Robotic Radical Prostatectomy 171
7.12 Conclusion 172
7.13 Future Work 172
References 172
8 IoT-Enabled Effective Healthcare Monitoring System Using Semantic Web 175
Sapna Juneja, Abhinav Juneja, Annu Dhankhar and Vishal Jain
8.1 Introduction 176
8.2 Literature Review 177
8.3 Phases of IoT-Based Healthcare 178
8.4 IoT-Based Healthcare Architecture 179
8.5 IoT-Based Sensors for Health Monitoring 180
8.6 IoT Applications in Healthcare 182
8.7 Semantic Web, Ontology, and Its Usage in Healthcare Sector 183
8.8 Semantic Web-Based IoT Healthcare 183
8.9 Challenges of IoT in Healthcare Industry 185
8.10 Conclusion 186
References 186
9 Precision Medicine in the Context of Ontology 191
Rehab A. Rayan and Imran Zafar
9.1 Introduction 192
9.2 The Rationale Behind Data 195
9.3 Data Standards for Interoperability 197
9.4 The Evolution of Ontology 198
9.5 Ontologies and Classifying Disorders 199
9.6 Phenotypic Ontology of Humans in Rare Disorders 201
9.7 Annotations and Ontology Integration 202
9.8 Precision Annotation and Integration 203
9.9 Ontology in the Contexts of Gene Identification Research 204
9.10 Personalizing Care for Chronic Illness 207
9.11 Roadblocks Toward Precision Medicine 208
9.12 Future Perspectives 209
9.13 Conclusion 209
References 210
10 A Knowledgebase Model Using RDF Knowledge Graph for Clinical Decision Support Systems 215
Ravi Lourdusamy and Xavierlal J. Mattam
10.1 Introduction 216
10.2 Relational Database to Graph Database 217
10.2.1 Relational Database for Knowledge Representation 218
10.2.2 NoSQL Databases 220
10.2.3 Graph Database 223
10.3 RDF 225
10.3.1 RDF Model and Technology 226
10.3.2 Metadata and URI 226
10.3.3 RDF Stores 228
10.4 Knowledgebase Systems and Knowledge Graphs 230
10.4.1 Knowledgebase Systems 230
10.4.2 Knowledge Graphs 232
10.4.3 RDF Knowledge Graphs 233
10.4.4 Information Retrieval Using SPARQL 234
10.5 Knowledge Base for CDSS 235
10.5.1 Curation of Knowledge Base for CDSS 236
10.5.2 Proposed Model for Curation 236
10.5.3 Evaluation Methodology 238
10.6 Discussion for Further Research and Development 239
10.7 Conclusion 239
References 240
11 Medical Data Supervised Learning Ontologies for Accurate Data Analysis 249
B. Tarakeswara Rao, R. S. M. Lakshmi Patibandla, V. Lakshman Narayana and Arepalli Peda Gopi
11.1 Introduction 250
11.2 Ontology of Biomedicine 251
11.2.1 Ontology Resource Open Sharing 254
11.3 Supervised Learning 255
11.4 AQ21 Rule in Machine Learning 256
11.5 Unified Medical Systems 259
11.5.1 Note of Relevance to Bioinformatic Experts 259
11.5.2 Terminological Incorporation Principles 260
11.5.3 Cross-References External 261
11.5.4 UMLS Data Access 262
11.6 Performance Analysis 262
11.7 Conclusion 265
References 265
12 Rare Disease Diagnosis as Information Retrieval Task 269
Jaya Lakkakula, Rutuja Phate, Alfiya Korbu and Sagar Barage
12.1 Introduction 270
12.2 Definition 271
12.3 Characteristics of Rare Diseases (RDs) 272
12.4 Types of Rare Diseases 273
12.4.1 Genetic Causes 274
12.4.2 Non-Genetic Causes 275
12.4.3 Pathogenic Causes (Infectious Agents) 275
12.4.4 Toxic Agents 275
12.4.5 Other Causes 276
12.5 A Brief Classification 276
12.6 Rare Disease Databases and Online Resources 277
12.6.1 European Reference Network: ERN 277
12.6.2 Genetic and Rare Diseases Information Center: GARD 278
12.6.3 International Classification of Diseases, 10th Revision: ICD-10 279
12.6.4 Orphanet-INSERM (Institut National de la Santé et de la Recherche Médicale) 280
12.6.5 Medical Dictionary for Regulatory Activities: MedDRA 280
12.6.6 Medical Subject Headings: MeSH 281
12.6.7 Online Mendelian Inheritance in Man: OMIM 282
12.6.8 Orphanet Rare Disease Ontology: ORDO 282
12.6.9 UMLS: Unified Medical Language System 282
12.6.10 SNOMED-CT: Systematized Nomenclature of Human and Veterinary Medicine--Clinical Terms 283
12.7 Information Retrieval of Rare Diseases Through a Web Search and Other Methods 284
12.7.1 What is Information Retrieval (IR)? 284
12.7.2 Listed Below Are Some of the Methods for Information Retrieval 284
12.7.2.1 Web Search for a Diagnosis 284
12.7.2.2 Cause of Diagnostic Errors in Web-Based Tools 285
12.7.2.3 Nonprofessional Use of Web Tool for Diagnosis 285
12.7.2.4 Performance of Web Search Tools 285
12.7.2.5 Design of Watson 286
12.8 Tips and Tricks for Information Retrieval 287
12.9 Research on Rare Disease Throughout the World 288
12.10 Conclusion 290
References 290
13 Atypical Point of View of Semantic Computing in Healthcare 293
L. Mayuri and K. M. Mehata
13.1 Introduction 294
13.2 Mind the Language 295
13.2.1 Why Words Matter 296
13.2.2 What Words Matter 296
13.2.3 How Words Matter 297
13.3 Semantic Analytics and Cognitive Computing: Recent Trends 297
13.3.1 Semantic Data Analysis 298
13.3.2 Semantic Data Integration 299
13.3.3 Semantic Applications 300
13.4 Semantics-Powered Healthcare SOS Engineering 302
13.5 Conclusion 303
References 304
14 Using Artificial Intelligence to Help COVID-19 Patients 309
Ayush Hans
14.1 Introduction 310
14.2 Method 313
14.3 Results 314
14.4 Discussion 315
14.4.1 What is the Use of AI in Healthcare? 315
14.4.2 How to Use AI for Critical Care Units 315
14.4.2.1 Input Stage 315
14.4.2.2 Process Stage 316
14.4.2.3 Output Stage 317
14.5 Conclusion 320
Acknowledgment 321
References 321
Index 325
Vishal Jain is an associate professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U. P. India. He obtained Ph.D (CSE), M.Tech (CSE), MBA (HR), MCA, MCP and CCNA. He has authored more than 80 research papers in reputed conferences and journals, including Web of Science and Scopus. He has authored and edited more than 10 books with various international publishers.
Jyotir Moy Chatterjee is an assistant professor in the Department of Information Technology at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal.
Ankita Bansal is an assistant professor in the Division of Information Technology at Netaji Subhas University of Technology. She received her master's and doctoral degree in computer science from Delhi Technological University (DTU).
Abha Jain is an assistant professor in the Department of Computer Science Engineering, Shaheed Rajguru College of Applied Sciences for Women, Delhi University, India. She received her master's and doctorate degree in software engineering from Delhi Technological University.
Jyotir Moy Chatterjee is an assistant professor in the Department of Information Technology at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal.
Ankita Bansal is an assistant professor in the Division of Information Technology at Netaji Subhas University of Technology. She received her master's and doctoral degree in computer science from Delhi Technological University (DTU).
Abha Jain is an assistant professor in the Department of Computer Science Engineering, Shaheed Rajguru College of Applied Sciences for Women, Delhi University, India. She received her master's and doctorate degree in software engineering from Delhi Technological University.
