Details

Ontology-Based Information Retrieval for Healthcare Systems


Ontology-Based Information Retrieval for Healthcare Systems


1. Aufl.

von: Vishal Jain, Ritika Wason, Jyotir Moy Chatterjee, Dac-Nhuong Le

197,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 10.07.2020
ISBN/EAN: 9781119641360
Sprache: englisch
Anzahl Seiten: 384

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Beschreibungen

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

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