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

<p><b>Vishal Jain</b> 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 <i>Web of Science and Scopus</i>. He has authored and edited more than 10 books with various international publishers. <p><b>Jyotir Moy Chatterjee</b> is an assistant professor in the Department of Information Technology at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal. <p><B>Ankita Bansal</b> 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). <p><b>Abha Jain </b>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.

<p><b>The book summarizes the trends and current research advances in web semantics, delineating the existing tools, techniques, methodologies, and research solutions</b></p> <p>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. <p>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. <p>This innovative book offers: <ul><li>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;</li> <li>Presents a comprehensive examination of the emerging research in areas of the semantic web; </li> <li>Discusses studies on new research areas including ontological engineering, semantic annotation and semantic sentiment analysis;</li> <li>Helps readers understand key concepts in semantic web applications for the biomedical engineering and healthcare fields;</li> <li>Includes coverage of key application areas of the semantic web.</li></ul> <p><b>Audience:</b> 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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