Medical and health informatics Books

Book SynopsisTable of ContentsUnit 1: The Environment of Health Care 1. The Health Care Industry 2. Collecting and Storing Health Care Data Unit 2: Content, Structure, and Processing of Health Information 3. Sources of Data 4. Data Quality and Management 5. Coded Data Unit 3: Use and Analysis of Data 6. Financial Management 7. Statistics and Data Analytics Unit 4: Administration and Operations 8. Confidentiality and Compliance 9. Management and Leadership 10. Performance Improvement and Project Management Appendix A Paper Health Records Appendix B Electronic Documentation Appendix C Using Microsoft Excel to Perform Calculations Glossary Index Abbreviations

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Book SynopsisTable of ContentsUNIT ONE: BUILDING A FOUNDATION Chapter 1: The Origins of Health Insurance Chapter 2: Tools of the Trade: A Career as a Health Insurance (Medical) Professional Chapter 3: The Legal and Ethical Side of Health Insurance Chapter 4: Healthcare Reform: Coverage Types and Sources Chapter 5: The Patient and the Billing Process UNIT TWO: HEALTH INSURANCE BASICS Chapter 6: Reimbursement Models Chapter 7: Understanding Managed Care Chapter 8: Understanding Medicare Chapter 9: Understanding Medicaid Chapter 10: Understanding Military Carriers Chapter 11: Understanding Miscellaneous Carriers: Workers' Compensation and Disability Insurance UNIT THREE: CLAIMS SUBMISSION Chapter 12: Claim Submission Methods Chapter 13: Diagnostic Coding Chapter 14: Procedural, Evaluation and Management, and HCPCS Coding Chapter 15: Claims Management UNIT FOUR: ADVANCED APPLICATION Chapter 16: The Role of Computers in Health Insurance Chapter 17: Reimbursement Procedures: Getting Paid Chapter 18: Hospital Billing and the UB-04 Appendix: Blank Forms

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Book SynopsisA doctor's powerful meditation on what his patients taught him, and what they can teach us about listening, healing, and public health.  For over three decades, Dr. Dean-David Schillinger has served in one of the country’s busiest and most important public hospitals. A public health leader and primary care physician for underserved patients, Schillinger learned that high-tech tests and novel medications are often not enough to save lives. Rather, accurate diagnosis, treatment and true healing come from listening deeply to patients and their stories.  In Telltale Hearts, Schillinger reveals what is lost when patients’ stories are ignored or overlooked, and how much is gained when these stories are actively elicited. The stories themselves, at times shocking and always revelatory, disclose secrets, prompt awe, forge unexpected connections, and even catalyze public health action.Telltale Hear

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Book SynopsisSimulations are an integral part of medical education today. Many universities have simulation centers, so-called skills labs, where students and medical personal can practice diagnostics and procedures on life-like mannequins. Others offer simulation courses in the different sub-disciplines. In the pre-clinical phase, simulations are used to illustrate basic principles in physiology, anatomy, genetics, and biochemistry. For example, simulations can show how the metabolism of enzymes changes in the presence of inhibitors, illustrating drug actions. This book covers all areas of simulations in medicine, starting from the molecular level via tissues and organs to the whole body. At the beginning of each chapter, a biological phenomenon is described, such as cell communication, gene translation, or the action of anti-carcinogenic drugs on tumors. In the following, simulations that illustrate these phenomena are discussed in detail, with the focus on how to use and interpret these simulations. The book is complemented by topics such as serious games and distance medicine. The book is based on a course for medical students organized in the editor's department. Every year, around 300 international undergraduate medical students take the course.

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Book SynopsisLooks inside this landscape of digital scientific work. This title chronicles the emergence of bioinformatics - the mode of working across and between biology, computing, mathematics, and statistics - from the 1960s to the present, seeking to understand how knowledge about life is made in and through virtual spaces.Trade Review"What happens to biology with computerization? Hallam Stevens's compelling ethnographic and historical narrative shows how the nature of the biological experiment has changed with the increasing use of the tools of information technology in life science and biomedicine." (Hannah Landecker, University of California, Los Angeles)"

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Book SynopsisLooks inside this landscape of digital scientific work. This title chronicles the emergence of bioinformatics - the mode of working across and between biology, computing, mathematics, and statistics - from the 1960s to the present, seeking to understand how knowledge about life is made in and through virtual spaces.Trade Review"What happens to biology with computerization? Hallam Stevens's compelling ethnographic and historical narrative shows how the nature of the biological experiment has changed with the increasing use of the tools of information technology in life science and biomedicine." (Hannah Landecker, University of California, Los Angeles)"

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Book SynopsisThe recent expansion in genome data and the parallel increase in cheap computing power has placed the bioinformatics exploration of pathogen genomes centre stage for vaccine researchers. The book shows how bioinformatic techniques can solve key problems from vaccinology and immunology.Trade Review“It pulls a number of different disciplines into a concise review that illustrates the potential we have in science to change our world.” (Doody's, April 2009) "This book may well serve as a first line of reference for all biologists and computer scientists. This textbook would be an excellent addition to the bookshelf of most scientists who encounter vaccinology in the drug discovery and development processes." ( Virology Journal - October -2009) Table of ContentsPreface xiii Acknowledgements xv Exordium xvii 1 Vaccines: Their place in history 1 Smallpox in history 1 Variolation 3 Variolation in history 5 Variolation comes to Britain 6 Lady Mary Wortley Montagu 9 Variolation and the Sublime Porte 11 The royal experiment 13 The boston connection 14 Variolation takes hold 17 The Suttonian method 18 Variolation in Europe 19 The coming of vaccination 21 Edward Jenner 23 Cowpox 26 Vaccination vindicated 28 Louis Pasteur 29 Vaccination becomes a science 30 Meister, Pasteur and rabies 31 A vaccine for every disease 33 In the time of cholera 34 Haffkine and cholera 36 Bubonic plague 37 The changing face of disease 39 Almroth wright and typhoid 40 Tuberculosis, Koch, and Calmette 43 Vaccine BCG 44 Poliomyelitis 46 Salk and Sabin 47 Diphtheria 49 Whooping cough 50 Many diseases, many vaccines 51 Smallpox: Endgame 53 Further reading 54 2 Vaccines: Need and opportunity 55 Eradication and reservoirs 55 The ongoing burden of disease 57 Lifespans 57 The evolving nature of disease 59 Economics, climate and disease 60 Three threats 60 Tuberculosis in the 21st century 61 HIV and AIDS 62 Malaria: Then and now 63 Influenza 64 Bioterrorism 65 Vaccines as medicines 67 Vaccines and the pharmaceutical industry 68 Making vaccines 70 The coming of the vaccine industry 70 3 Vaccines: How they work 73 Challenging the immune system 73 The threat from bacteria: Robust, diverse, and endemic 74 Microbes, diversity and metagenomics 75 The intrinsic complexity of the bacterial threat 76 Microbes and humankind 77 The nature of vaccines 78 Types of vaccine 80 Carbohydrate vaccines 82 Epitopic vaccines 82 Vaccine delivery 83 Emerging immunovaccinology 84 The immune system 85 Innate immunity 86 Adaptive immunity 88 The microbiome and mucosal immunity 90 Cellular components of immunity 90 Cellular immunity 93 The T cell repertoire 93 Epitopes: The immunological quantum 94 The major histocompatibility complex 95 MHC nomenclature 97 Peptide binding by the MHC 98 The structure of the MHC 99 Antigen presentation 101 The proteasome 101 Transporter associated with antigen processing 103 Class II processing 103 Seek simplicity and then distrust it 104 Cross presentation 105 T cell receptor 106 T cell activation 108 Immunological synapse 109 Signal 1, signal 2, immunodominance 109 Humoral immunity 110 Further reading 112 4 Vaccines: Data and databases 113 Making sense of data 113 Knowledge in a box 114 The science of -omes and -omics 115 The proteome 115 Systems biology 116 The immunome 117 Databases and databanks 118 The relational database 119 The XML database 119 The protein universe 120 Much data, many databases 122 What proteins do 122 What proteins are 124 The amino acid world 124 The chiral nature of amino acids 127 Naming the amino acids 130 The amino acid alphabet 132 Defining amino acid properties 134 Size, charge and hydrogen bonding 135 Hydrophobicity, lipophilicity and partitioning 136 Understanding partitioning 139 Charges, ionization, and pka 140 Many kinds of property 143 Mapping the world of sequences 146 Biological sequence databases 147 Nucleic acid sequence databases 148 Protein sequence databases 149 Annotating databases 150 Text mining 151 Ontologies 153 Secondary sequence databases 154 Other databases 155 Databases in immunology 156 Host databases 156 Pathogen databases 159 Functional immunological databases 161 Composite, integrated databases 162 Allergen databases 163 Further reading 165 Reference 165 5 Vaccines: Data driven prediction of binders, epitopes and immunogenicity 167 Towards epitope-based vaccines 167 T cell epitope prediction 168 Predicting MHC binding 169 Binding is biology 172 Quantifying binding 173 Entropy, enthalpy and entropy-enthalpy compensation 174 Experimental measurement of binding 175 Modern measurement methods 177 Isothermal titration calorimetry 178 Long and short of peptide binding 179 The class I peptide repertoire 180 Practicalities of binding prediction 181 Binding becomes recognition 182 Immunoinformatics lends a hand 183 Motif based prediction 184 The imperfect motif 185 Other approaches to binding prediction 186 Representing sequences 187 Computer science lends a hand 188 Artificial neural networks 188 Hidden Markov models 190 Support vector machines 190 Robust multivariate statistics 191 Partial least squares 191 Quantitative structure activity relationships 192 Other techniques and sequence representations 193 Amino acid properties 194 Direct epitope prediction 195 Predicting antigen presentation 196 Predicting class II MHC binding 197 Assessing prediction accuracy 199 ROC plots 202 Quantitative accuracy 203 Prediction assessment protocols 204 Comparing predictions 206 Prediction versus experiment 207 Predicting B cell epitopes 208 Peak profiles and smoothing 209 Early methods 210 Imperfect B cell prediction 211 References 212 6 Vaccines: Structural approaches 217 Structure and function 217 Types of protein structure 219 Protein folding 220 Ramachandran plots 221 Local structures 222 Protein families, protein folds 223 Comparing structures 223 Experimental structure determination 224 Structural genomics 226 Protein structure databases 227 Other databases 228 Immunological structural databases 229 Small molecule databases 230 Protein homology modelling 231 Using homology modelling 232 Predicting MHC supertypes 233 Application to alloreactivity 235 3D-QSAR 236 Protein docking 238 Predicting B cell epitopes with docking 238 Virtual screening 240 Limitations to virtual screening 241 Predicting epitopes with virtual screening 243 Virtual screening and adjuvant discovery 244 Adjuvants and innate immunity 245 Small molecule adjuvants 246 Molecular dynamics and immunology 248 Molecular dynamics methodology 249 Molecular dynamics and binding 249 Immunological applications 250 Limitations of molecular dynamics 251 Molecular dynamics and high performance computing 252 References 253 7 Vaccines: Computational solutions 257 Vaccines and the world 257 Bioinformatics and the challenge for vaccinology 259 Predicting immunogenicity 260 Computational vaccinology 261 The threat remains 262 Beyond empirical vaccinology 262 Designing new vaccines 263 The perfect vaccine 264 Conventional approaches 265 Genome sequences 266 Size of a genome 267 Reverse vaccinology 268 Finding antigens 269 The success of reverse vaccinology 271 Tumour vaccines 273 Prediction and personalised medicine 275 Imperfect data 276 Forecasting and the future of computational vaccinology 277 Index 283

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Book SynopsisVolume VI in theBen cao gang museries offers a complete translation of chapters 26 through 33, devoted to vegetables and fruits. TheBen cao gang muis a sixteenth-century Chinese encyclopedia of medical matter and natural history by Li Shizhen (15181593). The culmination of a sixteen-hundred-year history of Chinese medical and pharmaceutical literature, it is considered the most important and comprehensive book ever written in the history of Chinese medicine and remains an invaluable resource for researchers and practitioners. This nine-volume series reveals an almost two-millennia-long panorama of wide-ranging observations and sophisticated interpretations, ingenious manipulations, and practical applications of natural substances for the benefit of human health. Paul U. Unschuld's annotated translation of theBen cao gang mu, presented here with the original Chinese text, opens a rare window into viewing the people and culture of China's past. Table of ContentsContents 1. Prolegomena 1.1 History of Chinese materia medica literature 1.2 Structure and contents of the Ben cao gang mu 1.3 Biographical sketch of Li Shizhen (1518 – 1593) 2. Notes on the Translation 3. Wang Shizhen’s preface of 1590 4. Translation of the Ben Cao Gang Mu , ch. 26 through 33 Vegetables I, Fragrant-Acrid [Items], Chapter 26 Vegetables II, Soft[ing] and Smooth[ing items], Chapter 27 Vegetables III, Melons, Chapter 28 Vegetables IV, Water Vegetables Vegetables V, Mushrooms-Fungi Fruits I, Five Fruits, Chapter 29 Fruits II, Mountain Fruits, Chapter 30 Fruits III, Non-Chinese Fruits, Chapter 31 Fruits IV, Spices, Chapter 32 Fruits V, Melons and Berries Chapter 33 Fruits VI, Water Fruits Appendix

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Book SynopsisThis volume details state-of-the-art computational methods designed to manage, analyze, and generally leverage epigenomic and epitranscriptomic data. Chapters guide readers through fine-mapping and quantification of modifications, visual analytics, imputation methods, supervised analysis, and integrative approaches for single-cell data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Computational Epigenomics and Epitranscriptomics aims to provide an overview of epiomic protocols, making it easier for researchers to extract impactful biological insight from their data.Table of Contents1. DNA methylation data analysis using Msuite Xiaojian Liu, Pengxiang Yuan, and Kun Sun 2. Interactive DNA methylation arrays analysis with ShinyÉPICo Octavio Morante-Palacios 3. Predicting Chromatin Interactions from DNA Sequence using DeepC Ron Schwessinger 4. Integrating single-cell methylome and transcriptome data with MAPLE Yasin Uzun, Hao Wu, and Kai Tan 5. Quantitative comparison of multiple chromatin immunoprecipitation-sequencing (ChIP-seq) experiments with spikChIP Enrique Blanco, Cecilia Ballaré, Luciano Di Croce, and Sergi Aranda 6. A Guide To MethylationToActivity: A Deep-Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes In Individual Tumors Karissa Dieseldorff Jones, Daniel Putnam, Justin Williams, and Xiang Chen 7. DNA modification patterns filtering and analysis using DNAModAnnot Alexis Hardy, Sandra Duharcourt, and Matthieu Defrance 8. Methylome imputation by methylation patterns Ya-Ting Chang, Ming-Ren Yen, and Pao-Yang Chen 9. Sequoia: a framework for visual analysis of RNA modifications from direct RNA sequencing data Ratanond Koonchanok, Swapna Vidhur Daulatabad, Khairi Reda, and Sarath Chandra Janga 10. Predicting pseudouridine sites with Porpoise Xudong Guo, Fuyi Li, and Jiangning Song 11. Pseudouridine Identification and Functional Annotation with PIANO Jiahui Yao, Cuiyueyue Hao, Kunqi Chen, Jia Meng, and Bowen Song 12. Analyzing mRNA epigenetic sequencing data with TRESS Zhenxing Guo, Andrew M. Shafik, Peng Jin, Zhijin Wu, and Hao Wu 13. Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores Luca Cozzuto, Anna Delgado-Tejedor, Toni Hermoso Pulido, Eva Maria Novoa, and Julia Ponomarenko 14. Data Analysis Pipeline for Detection and Quantification of Pseudouridine (ψ) in RNA by HydraPsiSeq Florian Pichot, Virginie Marchand, Mark Helm, and Yuri Motorin 15. Analysis of RNA sequences and modifications using NASE Samuel Wein 16. Mapping of RNA modifications by direct Nanopore sequencing and JACUSA2 Amina Lemsara, Christoph Dieterich, and Isabel Naarmann-de Vries

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Book SynopsisThe Updated and Extensively Revised Guide to Developing Efficient Health Information Management Systems Health Information Management is the most comprehensive introduction to the study and development of health information management (HIM). Students in all areas of health care gain an unmatched understanding of the entire HIM profession and how it currently relates to the complex and continuously evolving field of health care in the United States. This brand-new Sixth Edition represents the most thorough revision to date of this cornerstone resource. Inside, a group of hand-picked HIM educators and practitioners representing the vanguard of the field provide fundamental guidelines on content and structure, analysis, assessment, and enhanced information. Fully modernized to reflect recent changes in the theory and practice of HIM, this latest edition features all-new illustrative examples and in-depth case studies, along with: Fresh anTable of ContentsAbout the Editor viiAbout the Contributors ixPreface xvAcknowledgments xvii 1 Health Information Management and the Healthcare Institution 1Felecia Williams 2 Health Record Content and Structure of the Health Record 25Linda Galocy 3 The Health Record: Electronic and Paper 55Linda Galocy 4 Healthcare Topics in Data Governance and Data Management 81Dilhari R. DeAlmeida and Suzanne Paone 5 Health Law, Data Privacy and Security, Fraud, and Abuse 105Dorinda M. Sattler 6 Informatics, Analytics, Data Use, and System Support 143Dorinda M. Sattler 7 Coding, Compliance, and Classification Systems 171Sandra K. Rains, Margaret A. Skurka, and Margie White 8 Clinical Documentation Improvement 205Sandra K. Rains 9 Revenue Cycle and Reimbursement 227Karen Wright 10 Strategic, Financial, and Organizational Management 253Janelle Wapola and Katie Kerr Index 283

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Book SynopsisApplied Smart Health Care Informatics Explores how intelligent systems offer new opportunities for optimizing the acquisition, storage, retrieval, and use of information in healthcare Applied Smart Health Care Informatics explores how health information technology and intelligent systems can be integrated and deployed to enhance healthcare management. Edited and authored by leading experts in the field, this timely volume introduces modern approaches for managing existing data in the healthcare sector by utilizing artificial intelligence (AI), meta-heuristic algorithms, deep learning, the Internet of Things (IoT), and other smart technologies. Detailed chapters review advances in areas including machine learning, computer vision, and soft computing techniques, and discuss various applications of healthcare management systems such as medical imaging, electronic medical records (EMR), and drug development assistance. Throughout the text, the authors propose new reTable of ContentsPreface xiii About the Editors xix List of Contributors xxv 1 An Overview of Applied Smart Health Care Informatics in the Context of Computational Intelligence 1Sourav De and Rik Das 1.1 Introduction 1 1.2 Big Data Analytics in Healthcare 2 1.3 AI in Healthcare 3 1.4 Cloud Computing in Healthcare 4 1.5 IoT in Healthcare 4 1.6 Conclusion 5 References 5 2 A Review on Deep Learning Method for Lung Cancer Stage Classification Using PET-CT 9Kaushik Pratim Das, Chandra J, and Dr Nachamai M 2.1 Introduction 9 2.1.1 Scope of the Research 10 2.1.2 TNM Staging 11 2.1.2.1 TNM Descriptors for Staging per IASLC Guidelines 11 2.1.2.2 PET-CT Scan in Lung Cancer Imaging 12 2.2 Related Works 12 2.2.1 Artificial Intelligence in Medical Imaging 14 2.2.2 Classification for Medical Imaging 14 2.2.2.1 Deep Learning 15 2.2.2.2 Image Classification Using Deep-learning Techniques 15 2.3 Methods 15 2.3.1 Transfer Learning 15 2.3.2 AlexNet 16 2.3.3 AlexNet Architecture 16 2.3.4 Experimental Setup 17 2.3.4.1 Image Processing 18 2.3.4.2 Data Augmentation 19 2.3.4.3 Training and Validation 19 2.4 Results and Discussion 19 2.4.1 Primary Tumor (T) 19 2.4.2 Metastasis (M) 21 2.4.3 Lymph Node (N) 21 2.4.4 Classification Accuracy of AlexNet 24 2.4.5 Comparative Analysis 25 2.4.6 Limitations 26 2.5 Conclusion 26 References 27 3 Formal Methods for the Security of Medical Devices 31Srinivas Pinisetty, Nathan Allen, Hammond Pearce, Mark Trew, Manoj Singh Gaur, and Partha Roop 3.1 Introduction 31 3.1.1 Pacemaker Security 33 3.1.2 Overview 34 3.2 Background: Cardiac Pacemakers 34 3.2.1 Pacemakers 35 3.2.1.1 Operation of a DDD Mode Pacemaker 36 3.2.2 The Cardiac System 37 3.2.2.1 Electrograms and Electrocardiograms 38 3.3 State of the Art, Formal Verification Techniques 39 3.3.1 Formal Verification Techniques 40 3.3.1.1 Static Verification Techniques 41 3.3.1.2 Dynamic Verification Techniques 42 3.3.2 Runtime Verification 43 3.3.2.1 A Brief Overview of Some Runtime Verification Frameworks 44 3.3.3 Correcting Execution of a System at Runtime (Runtime Enforcement) 45 3.3.3.1 Runtime Enforcement of Untimed Properties 46 3.3.3.2 Runtime Enforcement Approaches for Timed Properties 46 3.4 Formal Runtime-Based Approaches for Medical Device Security 47 3.4.1 Overview of the Approach 47 3.4.2 Mapping EGM Properties to ECG Properties 48 3.4.3 Security of Pacemakers Using Runtime Verification 49 3.4.3.1 Timed Words, Timed Languages, and Defining Timed Properties 50 3.4.3.2 Runtime Verification Monitor 51 3.4.3.3 Architecture of the Monitoring System 53 3.4.3.4 Implementation of the ECG Processing and RV Monitor Modules 53 3.4.3.5 Summary of Experiments and Results 54 3.4.4 Securing Pacemakers with Runtime Enforcement Hardware 54 3.4.4.1 Preliminaries: Words, Languages, and Defining Properties as DTA 55 3.4.4.2 Runtime Enforcement Monitor 56 3.4.4.3 Verification of the Enforcer Hardware 58 3.4.4.4 How Does the Enforcer Prevent Security Attacks? 58 3.4.4.5 Summary of Experiments and Results 59 3.5 Summary 59 References 60 4 Integrating Two Deep Learning Models to Identify Gene Signatures in Head and Neck Cancer from Multi-Omics Data 67Suparna Saha, Sumanta Ray, and Sanghamitra Bandyopadhyay 4.1 Introduction 67 4.2 Related Work 68 4.3 Materials and Methods 70 4.3.1 A Brief Introduction of the Capsule Network 70 4.3.2 An Introduction to Autoencoders 71 4.4 Results 72 4.4.1 Data Set Details 72 4.4.1.1 Gene Expression Data (Illumina Hiseq) 72 4.4.1.2 Human Methylation 450K 73 4.4.2 Architecture of Autoencoder Model 73 4.4.3 Architecture of the Proposed Capsule Network Model 74 4.4.4 Validation of Two Deep Learning Models 75 4.4.5 Gene Signatures from Primary Capsules 76 4.5 Discussion 77 Acknowledgments 78 References 79 5 A Review of Computational Learning and IoT Applications to High-Throughput Array-Based Sequencing and Medical Imaging Data in Drug Discovery and Other Health Care Systems 83Soham Choudhuri, Saurav Mallik, Bhaswar Ghosh, Tapas Si, Tapas Bhadra, Ujjwal Maulik, and Aimin Li 5.1 Introduction 83 5.2 Biological Terms 84 5.3 Single-Cell Sequencing (scRNA-seq) Data 86 5.3.1 Computational Methods for Interpreting scRNA-seq Data 86 5.3.1.1 Visualizing and Clustering Cells 86 5.3.1.2 Inference and Branching Analysis of Cellular Trajectory 86 5.3.1.3 Identifying Highly Variable Genes 86 5.3.1.4 Identifying Marker and Differentially Expressed Genes 90 5.4 Methods of Multi-Omic Data Integration 90 5.4.1 Unsupervised Data Integration Methods 91 5.4.1.1 Matrix Factorization Methods 91 5.4.1.2 Bayesian Methods 91 5.4.1.3 Network-Based Methods 94 5.4.1.4 Multi-Step Analysis and Multiple Kernel Learning 94 5.4.2 Supervised Data Integration 95 5.4.2.1 Network-Based Methods 95 5.4.2.2 Multiple Kernel Learning 95 5.4.2.3 Multi-Step Analysis 95 5.4.3 Semi-Supervised Data Integration 95 5.4.3.1 GeneticInterPred 97 5.5 AI Drug Discovery 97 5.5.1 AI Primary Drug Screening 97 5.5.1.1 Cell Sorting and Classification with Image Analysis 97 5.5.2 AI Secondary Drug Screening 99 5.5.2.1 Physical Properties Predictions 99 5.5.2.2 Predictions of Bio-Activity 99 5.5.2.3 Prediction of Toxicity 99 5.5.3 AI in Drug Design 99 5.5.3.1 Prediction of Target Protein 3D Structures 99 5.5.3.2 Predicting Drug-Protein Interactions 100 5.5.4 Planning Chemical Synthesis with AI 100 5.5.4.1 Retro-Synthesis Pathway Prediction 100 5.5.4.2 Reaction Yield Predictions and Reaction Mechanism Insights 100 5.6 Medical Imaging Data Analysis 100 5.6.1 Analysis: Radio-Mic Quantification 101 5.6.2 Analysis: Bio-Marker Identification 101 5.7 Applying IoT (Internet of Things) to Biomedical Research 102 5.7.1 IoT and IoMT Applications for Healthcare and Well-Being 102 5.7.1.1 Wireless Medical Devices 102 5.8 Conclusions 102 Acknowledgments 102 References 102 6 Association Rule Mining Based on Ethnic Groups and Classification using Super Learning 111Md Faisal Kabir and Simone A. Ludwig 6.1 Introduction 111 6.2 Background 112 6.3 Motivation and Contribution 114 6.4 Data Analysis 115 6.4.1 Data Description 115 6.4.2 Data Preprocessing 115 6.4.3 Further Preprocessing for Ethnic Group Rule Discovery with Multiple Consequences 115 6.4.3.1 Transaction-Like Database for Association Rule 115 6.4.4 Classification Data Set 116 6.5 Methodology 117 6.5.1 Association Rule Mining 117 6.5.2 Super Learning 118 6.5.2.1 Ensemble or Super Learner Set-Up 118 6.6 Experiments and Results 119 6.6.1 Rules Discovery 120 6.6.1.1 Rules of Breast Cancer Patients Based on Ethnic Groups 120 6.6.1.2 Interpreting Rules 120 6.6.2 Evaluation Criteria of Classification Model 121 6.6.2.1 Super Learner Results 124 6.6.3 Discussion 125 6.7 Conclusion and Future Work 126 References 127 7 Neuro-Rough Hybridization for Recognition of Virus Particles from TEM Images 131Debamita Kumar and Pradipta Maji 7.1 Introduction 131 7.2 Existing Approaches for Virus Particle Classification 132 7.3 Proposed Algorithm 134 7.3.1 Extraction of Local Textural Features 135 7.3.2 Selection of Class-Pair Relevant Features 135 7.3.3 Extraction of Discriminating Features 138 7.3.4 Classification 139 7.4 Experimental Results and Discussion 140 7.4.1 Experimental Setup 140 7.4.2 Methods Compared 140 7.4.3 Database Considered 141 7.4.4 Effectiveness of Proposed Approach 141 7.4.5 Comparative Performance Analysis 143 7.4.5.1 Comparison with Deep Architectures 144 7.4.5.2 Comparison with Existing Approaches 145 7.5 Conclusion 146 References 147 8 Neural Network Optimizers for Brain Tumor Image Detection 151T. Kalaiselvi and S.T. Padmapriya 8.1 Introduction 151 8.2 Related Works 152 8.3 Background 153 8.3.1 Types of Neural Networks 153 8.3.2 Tunable Elements of Neural Networks 154 8.3.2.1 Basic Parameters 154 8.3.2.2 Hyperparameters 154 8.3.2.3 Regularization Techniques 155 8.3.2.4 Neural Network Optimizers 156 8.4 Case Study - Brain Tumor Detection 157 8.4.1 Methodology 157 8.4.2 Data Sets and Metrics 157 8.4.3 Results and Discussion 159 8.5 Conclusion 162 References 162 9 Abnormal Slice Classification from MRI Volumes using the Bilateral Symmetry of Human Head Scans 165N. Kalaichelvi, T. Kalaiselvi, and K. Somasundaram 9.1 Introduction 165 9.1.1 MRIs of the Human Brain 165 9.1.2 Normal and Abnormal Slices 166 9.1.3 Background 167 9.1.3.1 Decision Tree Classifiers 167 9.1.3.2 K-Nearest Neighbours (KNN) Classifiers 168 9.1.3.3 Support Vector Machine (SVM) 168 9.1.3.4 Naive Bayes 169 9.1.3.5 Artificial Neural Network (ANN) 169 9.1.3.6 Back-Propagation Neural Network (BPN) 170 9.1.3.7 Random Forest Classifiers 170 9.2 Literature Review 171 9.3 Methodology 172 9.3.1 Preprocessing 173 9.3.2 Feature Extraction 174 9.3.3 Feature Selection 175 9.3.4 Classification 177 9.3.5 Cross-Validation 177 9.3.6 Training Validation and Testing 178 9.4 Materials and Metrics 179 9.4.1 Confusion Matrix 179 9.5 Results and Discussion 180 9.6 Conclusion 182 References 183 10 Conclusion 187Siddhartha Bhattacharyya References 188 Index 191

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Book SynopsisCOGNITIVE INTELLIGENCE AND BIG DATA IN HEALTHCARE Applications of cognitive intelligence, advanced communication, and computational methods can drive healthcare research and enhance existing traditional methods in disease detection and management and prevention. As health is the foremost factor affecting the quality of human life, it is necessary to understand how the human body is functioning by processing health data obtained from various sources more quickly. Since an enormous amount of data is generated during data processing, a cognitive computing system could be applied to respond to queries, thereby assisting in customizing intelligent recommendations. This decision-making process could be improved by the deployment of cognitive computing techniques in healthcare, allowing for cutting-edge techniques to be integrated into healthcare to provide intelligent services in various healthcare applications. This book tackles all these issues and provides insight into these diversifieTable of ContentsPreface xv 1 Era of Computational Cognitive Techniques in Healthcare Systems 1Deependra Rastogi, Varun Tiwari, Shobhit Kumar and Prabhat Chandra Gupta 1.1 Introduction 2 1.2 Cognitive Science 3 1.3 Gap Between Classical Theory of Cognition 4 1.4 Cognitive Computing’s Evolution 6 1.5 The Coming Era of Cognitive Computing 7 1.6 Cognitive Computing Architecture 9 1.6.1 The Internet-of-Things and Cognitive Computing 10 1.6.2 Big Data and Cognitive Computing 11 1.6.3 Cognitive Computing and Cloud Computing 13 1.7 Enabling Technologies in Cognitive Computing 13 1.7.1 Reinforcement Learning and Cognitive Computing 13 1.7.2 Cognitive Computing with Deep Learning 15 1.7.2.1 Relational Technique and Perceptual Technique 15 1.7.2.2 Cognitive Computing and Image Understanding 16 1.8 Intelligent Systems in Healthcare 17 1.8.1 Intelligent Cognitive System in Healthcare (Why and How) 20 1.9 The Cognitive Challenge 32 1.9.1 Case Study: Patient Evacuation 32 1.9.2 Case Study: Anesthesiology 32 1.10 Conclusion 34 References 35 2 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics 41Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur and Yuzo Iano 2.1 Introduction 42 2.2 Literature Concept 44 2.2.1 Cognitive Computing Concept 44 2.2.2 Neural Networks Concepts 47 2.2.3 Convolutional Neural Network 49 2.2.4 Deep Learning 52 2.3 Materials and Methods (Metaheuristic Algorithm Proposal) 55 2.4 Case Study and Discussion 57 2.5 Conclusions with Future Research Scopes 60 References 61 3 Convergence of Big Data and Cognitive Computing in Healthcare 67R. Sathiyaraj, U. Rahamathunnisa, M.V. Jagannatha Reddy and T. Parameswaran 3.1 Introduction 68 3.2 Literature Review 70 3.2.1 Role of Cognitive Computing in Healthcare Applications 70 3.2.2 Research Problem Study by IBM 73 3.2.3 Purpose of Big Data in Healthcare 74 3.2.4 Convergence of Big Data with Cognitive Computing 74 3.2.4.1 Smart Healthcare 74 3.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare 75 3.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification 76 3.3.1 EEG Pathology Diagnoses 76 3.3.2 Cognitive–Big Data-Based Smart Healthcare 77 3.3.3 System Architecture 79 3.3.4 Detection and Classification of Pathology 80 3.3.4.1 EEG Preprocessing and Illustration 80 3.3.4.2 CNN Model 80 3.3.5 Case Study 81 3.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud 83 3.4.1 Cloud Computing with Big Data in Healthcare 86 3.4.2 Heart Diseases 87 3.4.3 Healthcare Big Data Techniques 88 3.4.3.1 Rule Set Classifiers 88 3.4.3.2 Neuro Fuzzy Classifiers 89 3.4.3.3 Experimental Results 91 3.5 Conclusion 92 References 93 4 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging 97R. Indrakumari, Nilanjana Pradhan, Shrddha Sagar and Kiran Singh 4.1 Introduction 98 4.2 The Role of Technology in an Aging Society 99 4.3 Literature Survey 100 4.4 Health Monitoring 101 4.5 Nutrition Monitoring 105 4.6 Stress-Log: An IoT-Based Smart Monitoring System 106 4.7 Active Aging 108 4.8 Localization 108 4.9 Navigation Care 111 4.10 Fall Monitoring 113 4.10.1 Fall Detection System Architecture 114 4.10.2 Wearable Device 114 4.10.3 Wireless Communication Network 114 4.10.4 Smart IoT Gateway 115 4.10.5 Interoperability 115 4.10.6 Transformation of Data 115 4.10.7 Analyzer for Big Data 115 4.11 Conclusion 115 References 116 5 Influence of Cognitive Computing in Healthcare Applications 121Lucia Agnes Beena T. and Vinolyn Vijaykumar 5.1 Introduction 122 5.2 Bond Between Big Data and Cognitive Computing 124 5.3 Need for Cognitive Computing in Healthcare 126 5.4 Conceptual Model Linking Big Data and Cognitive Computing 128 5.4.1 Significance of Big Data 128 5.4.2 The Need for Cognitive Computing 129 5.4.3 The Association Between the Big Data and Cognitive Computing 130 5.4.4 The Advent of Cognition in Healthcare 132 5.5 IBM’s Watson and Cognitive Computing 133 5.5.1 Industrial Revolution with Watson 134 5.5.2 The IBM’s Cognitive Computing Endeavour in Healthcare 135 5.6 Future Directions 137 5.6.1 Retail 138 5.6.2 Research 139 5.6.3 Travel 139 5.6.4 Security and Threat Detection 139 5.6.5 Cognitive Training Tools 140 5.7 Conclusion 141 References 141 6 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems 145Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur and Yuzo Iano 6.1 Introduction 146 6.2 Literature Concept 148 6.2.1 Cognitive Computing Concept 148 6.2.1.1 Application Potential 151 6.2.2 Cognitive Computing in Healthcare 153 6.2.3 Deep Learning in Healthcare 157 6.2.4 Natural Language Processing in Healthcare 160 6.3 Discussion 162 6.4 Trends 163 6.5 Conclusions 164 References 165 7 Protecting Patient Data with 2F- Authentication 169G. S. Pradeep Ghantasala, Anu Radha Reddy and R. Mohan Krishna Ayyappa 7.1 Introduction 170 7.2 Literature Survey 175 7.3 Two-Factor Authentication 177 7.3.1 Novel Features of Two-Factor Authentication 178 7.3.2 Two-Factor Authentication Sorgen 178 7.3.3 Two-Factor Security Libraries 179 7.3.4 Challenges for Fitness Concern 180 7.4 Proposed Methodology 181 7.5 Medical Treatment and the Preservation of Records 186 7.5.1 Remote Method of Control 187 7.5.2 Enabling Healthcare System Technology 187 7.6 Conclusion 189 References 190 8 Data Analytics for Healthcare Monitoring and Inferencing 197Gend Lal Prajapati, Rachana Raghuwanshi and Rambabu Raghuwanshi 8.1 An Overview of Healthcare Systems 198 8.2 Need of Healthcare Systems 198 8.3 Basic Principle of Healthcare Systems 199 8.4 Design and Recommended Structure of Healthcare Systems 199 8.4.1 Healthcare System Designs on the Basis of these Parameters 200 8.4.2 Details of Healthcare Organizational Structure 201 8.5 Various Challenges in Conventional Existing Healthcare System 202 8.6 Health Informatics 202 8.7 Information Technology Use in Healthcare Systems 203 8.8 Details of Various Information Technology Application Use in Healthcare Systems 203 8.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below 204 8.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems 205 8.11 Healthcare Data Analytics 206 8.12 Healthcare as a Concept 206 8.13 Healthcare’s Key Technologies 207 8.14 The Present State of Smart Healthcare Application 207 8.15 Data Analytics with Machine Learning Use in Healthcare Systems 208 8.16 Benefit of Data Analytics in Healthcare System 210 8.17 Data Analysis and Visualization: COVID-19 Case Study in India 210 8.18 Bioinformatics Data Analytics 222 8.18.1 Notion of Bioinformatics 222 8.18.2 Bioinformatics Data Challenges 222 8.18.3 Sequence Analysis 222 8.18.4 Applications 223 8.18.5 COVID-19: A Bioinformatics Approach 224 8.19 Conclusion 224 References 225 9 Features Optimistic Approach for the Detection of Parkinson’s Disease 229R. Shantha Selva Kumari, L. Vaishalee and P. Malavikha 9.1 Introduction 230 9.1.1 Parkinson’s Disease 230 9.1.2 Spect Scan 231 9.2 Literature Survey 232 9.3 Methods and Materials 233 9.3.1 Database Details 233 9.3.2 Procedure 234 9.3.3 Pre-Processing Done by PPMI 235 9.3.4 Image Analysis and Features Extraction 235 9.3.4.1 Image Slicing 235 9.3.4.2 Intensity Normalization 237 9.3.4.3 Image Segmentation 239 9.3.4.4 Shape Features Extraction 240 9.3.4.5 SBR Features 241 9.3.4.6 Feature Set Analysis 242 9.3.4.7 Surface Fitting 242 9.3.5 Classification Modeling 243 9.3.6 Feature Importance Estimation 246 9.3.6.1 Need for Analysis of Important Features 246 9.3.6.2 Random Forest 247 9.4 Results and Discussion 248 9.4.1 Segmentation 248 9.4.2 Shape Analysis 249 9.4.3 Classification 249 9.5 Conclusion 252 References 253 10 Big Data Analytics in Healthcare 257Akanksha Sharma, Rishabha Malviya and Ramji Gupta 10.1 Introduction 258 10.2 Need for Big Data Analytics 260 10.3 Characteristics of Big Data 264 10.3.1 Volume 264 10.3.2 Velocity 265 10.3.3 Variety 265 10.3.4 Veracity 265 10.3.5 Value 265 10.3.6 Validity 265 10.3.7 Variability 266 10.3.8 Viscosity 266 10.3.9 Virality 266 10.3.10 Visualization 266 10.4 Big Data Analysis in Disease Treatment and Management 267 10.4.1 For Diabetes 267 10.4.2 For Heart Disease 268 10.4.3 For Chronic Disease 270 10.4.4 For Neurological Disease 271 10.4.5 For Personalized Medicine 271 10.5 Big Data: Databases and Platforms in Healthcare 279 10.6 Importance of Big Data in Healthcare 285 10.6.1 Evidence-Based Care 285 10.6.2 Reduced Cost of Healthcare 285 10.6.3 Increases the Participation of Patients in the Care Process 285 10.6.4 The Implication in Health Surveillance 285 10.6.5 Reduces Mortality Rate 285 10.6.6 Increase of Communication Between Patients and Healthcare Providers 286 10.6.7 Early Detection of Fraud and Security Threats in Health Management 286 10.6.8 Improvement in the Care Quality 286 10.7 Application of Big Data Analytics 286 10.7.1 Image Processing 286 10.7.2 Signal Processing 287 10.7.3 Genomics 288 10.7.4 Bioinformatics Applications 289 10.7.5 Clinical Informatics Application 291 10.8 Conclusion 293 References 294 11 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery 303V. Sathananthavathi and G. Indumathi 11.1 Introduction 304 11.1.1 Glaucoma 304 11.2 Literature Survey 306 11.3 Methodology 309 11.3.1 Sclera Segmentation 310 11.3.1.1 Fully Convolutional Network 311 11.3.2 Pupil/Iris Ratio 313 11.3.2.1 Canny Edge Detection 314 11.3.2.2 Mean Redness Level (MRL) 315 11.3.2.3 Red Area Percentage (RAP) 316 11.4 Results and Discussion 317 11.4.1 Feature Extraction from Frontal Eye Images 318 11.4.1.1 Level of Mean Redness (MRL) 318 11.4.1.2 Percentage of Red Area (RAP) 318 11.4.2 Images of the Frontal Eye Pupil/Iris Ratio 318 11.4.2.1 Histogram Equalization 319 11.4.2.2 Morphological Reconstruction 319 11.4.2.3 Canny Edge Detection 319 11.4.2.4 Adaptive Thresholding 320 11.4.2.5 Circular Hough Transform 321 11.4.2.6 Classification 322 11.5 Conclusion and Future Work 324 References 325 12 State of Mental Health and Social Media: Analysis, Challenges, Advancements 327Atul Pankaj Patil, Kusum Lata Jain, Smaranika Mohapatra and Suyesha Singh 12.1 Introduction 328 12.2 Introduction to Big Data and Data Mining 328 12.3 Role of Sentimental Analysis in the Healthcare Sector 330 12.4 Case Study: Analyzing Mental Health 332 12.4.1 Problem Statement 332 12.4.2 Research Objectives 333 12.4.3 Methodology and Framework 333 12.4.3.1 Big 5 Personality Model 333 12.4.3.2 Openness to Explore 334 12.4.3.3 Methodology 335 12.4.3.4 Detailed Design Methodologies 340 12.4.3.5 Work Done Details as Required 341 12.5 Results and Discussion 343 12.6 Conclusion and Future 345 References 346 13 Applications of Artificial Intelligence, Blockchain, and Internet-of-Things in Management of Chronic Disease 349Geetanjali, Rishabha Malviya, Rajendra Awasthi, Pramod Kumar Sharma, Nidhi Kala, Vinod Kumar and Sanjay Kumar Yadav 13.1 Introduction 350 13.2 Artificial Intelligence and Management of Chronic Diseases 351 13.3 Blockchain and Healthcare 354 13.3.1 Blockchain and Healthcare Management of Chronic Disease 355 13.4 Internet-of-Things and Healthcare Management of Chronic Disease 358 13.5 Conclusions 360 References 360 14 Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain 367BKSP Kumar Raju Alluri 14.1 Introduction 367 14.2 Cognitive Computing Framework in Healthcare 371 14.3 Benefits of Using Cognitive Computing for Healthcare 372 14.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management 374 14.4.1 Using Cognitive Services for a Patient’s Healthcare Management 375 14.4.2 Using Cognitive Services for Healthcare Providers 376 14.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management 377 14.6 Future Directions for Extending Heathcare Services Using CATs 380 14.7 Addressing CAT Challenges in Healthcare as a General Framework 384 14.8 Conclusion 384 References 385 Index 391

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Book SynopsisTable of ContentsPreface xv 1 Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction 1Jaspreet Kaur, Bharti Joshi and Rajashree Shedge 1.1 Introduction 2 1.1.1 Scope and Motivation 3 1.2 Literature Review 4 1.2.1 Comparative Analysis 5 1.2.2 Survey Analysis 5 1.3 Tools and Techniques 10 1.3.1 Description of Dataset 11 1.3.2 Machine Learning Algorithm 12 1.3.3 Decision Tree 14 1.3.4 Random Forest 15 1.3.5 Naive Bayes Algorithm 16 1.3.6 K Means Algorithm 18 1.3.7 Ensemble Method 18 1.3.7.1 Bagging 19 1.3.7.2 Boosting 19 1.3.7.3 Stacking 19 1.3.7.4 Majority Vote 19 1.4 Proposed Method 20 1.4.1 Experiment and Analysis 20 1.4.2 Method 22 1.5 Conclusion 25 References 26 2 Cancerous Cells Detection in Lung Organs of Human Body: IoT-Based Healthcare 4.0 Approach 29Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Mukund Rastogi 2.1 Introduction 30 2.1.1 Motivation to the Study 30 2.1.1.1 Problem Statements 31 2.1.1.2 Authors’ Contributions 31 2.1.1.3 Research Manuscript Organization 31 2.1.1.4 Definitions 32 2.1.2 Computer-Aided Diagnosis System (CADe or CADx) 32 2.1.3 Sensors for the Internet of Things 32 2.1.4 Wireless and Wearable Sensors for Health Informatics 33 2.1.5 Remote Human’s Health and Activity Monitoring 33 2.1.6 Decision-Making Systems for Sensor Data 33 2.1.7 Artificial Intelligence and Machine Learning for Health Informatics 34 2.1.8 Health Sensor Data Management 34 2.1.9 Multimodal Data Fusion for Healthcare 35 2.1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT 35 2.2 Literature Review 35 2.3 Proposed Systems 37 2.3.1 Framework or Architecture of the Work 38 2.3.2 Model Steps and Parameters 38 2.3.3 Discussions 39 2.4 Experimental Results and Analysis 39 2.4.1 Tissue Characterization and Risk Stratification 39 2.4.2 Samples of Cancer Data and Analysis 40 2.5 Novelties 42 2.6 Future Scope, Limitations, and Possible Applications 42 2.7 Recommendations and Consideration 43 2.8 Conclusions 43 References 43 3 Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case 47Carlos Polanco, Manlio F. Márquez and Gilberto Vargas-Alarcón 3.1 Introduction 48 3.2 Human Coronavirus Types 49 3.3 The SARS-CoV-2 Pandemic Impact 50 3.3.1 RNA Virus vs DNA Virus 51 3.3.2 The Coronaviridae Family 51 3.3.3 The SARS-CoV-2 Structural Proteins 52 3.3.4 Protein Representations 52 3.4 Computational Predictors 53 3.4.1 Supervised Algorithms 53 3.4.2 Non-Supervised Algorithms 54 3.5 Polarity Index Method® 54 3.5.1 The PIM® Profile 54 3.5.2 Advantages 55 3.5.3 Disadvantages 55 3.5.4 SARS-CoV-2 Recognition Using PIM® Profile 55 3.6 Future Implications 59 3.7 Acknowledgments 60 References 60 4 Deep Learning in Gait Abnormality Detection: Principles and Illustrations 63Saikat Chakraborty, Sruti Sambhavi and Anup Nandy 4.1 Introduction 63 4.2 Background 65 4.2.1 LSTM 65 4.2.1.1 Vanilla LSTM 65 4.2.1.2 Bidirectional LSTM 66 4.3 Related Works 67 4.4 Methods 68 4.4.1 Data Collection and Analysis 68 4.4.2 Results and Discussion 69 4.5 Conclusion and Future Work 71 4.6 Acknowledgments 71 References 71 5 Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health 73Akanksha Jaiswar, Devender Arora, Manisha Malhotra, Abhimati Shukla and Nivedita Rai 5.1 Introduction 74 5.2 Types of Biological Networks 76 5.3 Methodologies in Network Embedding 76 5.4 Attributed and Non-Attributed Network Embedding 82 5.5 Applications of Network Embedding in Computational Biology 83 5.5.1 Understanding Genomic and Protein Interaction via Network Alignment 83 5.5.2 Pharmacogenomics 84 5.5.2.1 Drug-Target Interaction Prediction 84 5.5.2.2 Drug-Drug Interaction 84 5.5.2.3 Drug-Disease Interaction Prediction 85 5.5.2.4 Analysis of Adverse Drug Reaction 85 5.5.3 Function Prediction 86 5.5.4 Community Detection 86 5.5.5 Network Denoising 87 5.5.6 Analysis of Multi-Omics Data 87 5.6 Limitations of Network Embedding in Biology 87 5.7 Conclusion and Outlook 89 References 89 6 Heart Disease Classification Using Regional Wall Thickness by Ensemble Classifier 99Prakash J., Vinoth Kumar B. and Sandhya R. 6.1 Introduction 100 6.2 Related Study 101 6.3 Methodology 103 6.3.1 Pre-Processing 103 6.3.2 Region of Interest Extraction 104 6.3.3 Segmentation 105 6.3.4 Feature Extraction 106 6.3.5 Disease Classification 107 6.4 Implementation and Result Analysis 108 6.4.1 Dataset Description 108 6.4.2 Testbed 108 6.4.3 Discussion 108 6.4.3.1 K-Fold Cross-Validation 110 6.4.3.2 Confusion Matrix 110 6.5 Conclusion 115 References 115 7 Deep Learning for Medical Informatics and Public Health 117K. Aditya Shastry, Sanjay H. A., Lakshmi M. and Preetham N. 7.1 Introduction 118 7.2 Deep Learning Techniques in Medical Informatics and Public Health 121 7.2.1 Autoencoders 122 7.2.2 Recurrent Neural Network 123 7.2.3 Convolutional Neural Network (CNN) 124 7.2.4 Deep Boltzmann Machine 126 7.2.5 Deep Belief Network 127 7.3 Applications of Deep Learning in Medical Informatics and Public Health 128 7.3.1 The Use of DL for Cancer Diagnosis 128 7.3.2 DL in Disease Prediction and Treatment 129 7.3.3 Future Applications 133 7.4 Open Issues Concerning DL in Medical Informatics and Public Health 135 7.5 Conclusion 139 References 140 8 An Insight Into Human Pose Estimation and Its Applications 147Shambhavi Mishra, Janamejaya Channegowda and Kasina Jyothi Swaroop 8.1 Foundations of Human Pose Estimation 147 8.2 Challenges to Human Pose Estimation 149 8.2.1 Motion Blur 150 8.2.2 Indistinct Background 151 8.2.3 Occlusion or Self-Occlusion 151 8.2.4 Lighting Conditions 151 8.3 Analyzing the Dimensions 152 8.3.1 2D Human Pose Estimation 152 8.3.1.1 Single-Person Pose Estimation 153 8.3.1.2 Multi-Person Pose Estimation 153 8.3.2 3D Human Pose Estimation 153 8.4 Standard Datasets for Human Pose Estimation 154 8.4.1 Pascal VOC (Visual Object Classes) Dataset 156 8.4.2 KTH Multi-View Football Dataset I 156 8.4.3 KTH Multi-View Football Dataset II 156 8.4.4 MPII Human Pose Dataset 157 8.4.5 BBC Pose 157 8.4.6 COCO Dataset 157 8.4.7 J-HMDB Dataset 158 8.4.8 Human3.6M Dataset 158 8.4.9 DensePose 158 8.4.10 AMASS Dataset 159 8.5 Deep Learning Revolutionizing Pose Estimation 159 8.5.1 Approaches in 2D Human Pose Estimation 159 8.5.2 Approaches in 3D Human Pose Estimation 163 8.6 Application of Human Pose Estimation in Medical Domains 165 8.7 Conclusion 166 References 167 9 Brain Tumor Analysis Using Deep Learning: Sensor and IoT-Based Approach for Futuristic Healthcare 171Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Akshit Rajan Rastogi 9.1 Introduction 172 9.1.1 Brain Tumor 172 9.1.2 Big Data Analytics in Health Informatics 172 9.1.3 Machine Learning in Healthcare 173 9.1.4 Sensors for Internet of Things 173 9.1.5 Challenges and Critical Issues of IoT in Healthcare 174 9.1.6 Machine Learning and Artificial Intelligence for Health Informatics 174 9.1.7 Health Sensor Data Management 175 9.1.8 Multimodal Data Fusion for Healthcare 175 9.1.9 Heterogeneous Data Fusion and Context-Aware Systems a Context-Aware Data Fusion Approach for Health-IoT 176 9.1.10 Role of Technology in Addressing the Problem of Integration of Healthcare System 176 9.2 Literature Survey 177 9.3 System Design and Methodology 179 9.3.1 System Design 179 9.3.2 CNN Architecture 180 9.3.3 Block Diagram 181 9.3.4 Algorithm(s) 181 9.3.5 Our Experimental Results, Interpretation, and Discussion 183 9.3.6 Implementation Details 183 9.3.7 Snapshots of Interfaces 184 9.3.8 Performance Evaluation 186 9.3.9 Comparison with Other Algorithms 186 9.4 Novelty in Our Work 186 9.5 Future Scope, Possible Applications, and Limitations 188 9.6 Recommendations and Consideration 188 9.7 Conclusions 188 References 189 10 Study of Emission From Medicinal Woods to Curb Threats of Pollution and Diseases: Global Healthcare Paradigm Shift in 21st Century 191Rohit Rastogi, Mamta Saxena, Devendra Kr. Chaturvedi, Sheelu Sagar, Neha Gupta, Harshit Gupta, Akshit Rajan Rastogi, Divya Sharma, Manu Bhardwaj and Pranav Sharma 10.1 Introduction 192 10.1.1 Scenario of Pollution and Need to Connect with Indian Culture 192 10.1.2 Global Pollution Scenario 192 10.1.3 Indian Crisis on Pollution and Worrying Stats 193 10.1.4 Efforts Made to Curb Pollution World Wide 194 10.1.5 Indian Ancient Vedic Sciences to Curb Pollution and Related Disease 196 10.1.6 The Yajna Science: A Boon to Human Race From Rishi-Muni 196 10.1.7 The Science of Mantra Associated With Yajna and Its Scientific Effects 197 10.1.8 Effect of Different Woods and Cow Dung Used in Yajna 197 10.1.9 Use of Sensors and IoT to Record Experimental Data 198 10.1.10 Analysis and Pattern Recognition by ML and AI 199 10.2 Literature Survey 200 10.3 The Methodology and Protocols Followed 201 10.4 Experimental Setup of an Experiment 202 10.5 Results and Discussions 202 10.5.1 Mango 202 10.5.2 Bargad 203 10.6 Applications of Yagya and Mantra Therapy in Pollution Control and Its Significance 207 10.7 Future Research Perspectives 207 10.8 Novelty of Our Research 208 10.9 Recommendations 208 10.10 Conclusions 209 References 209 11 An Economical Machine Learning Approach for Anomaly Detection in IoT Environment 215Ambika N. 11.1 Introduction 215 11.2 Literature Survey 218 11.3 Proposed Work 229 11.4 Analysis of the Work 230 11.5 Conclusion 231 References 231 12 Indian Science of Yajna and Mantra to Cure Different Diseases: An Analysis Amidst Pandemic With a Simulated Approach 235Rohit Rastogi, Mamta Saxena, Devendra Kumar Chaturvedi, Mayank Gupta, Puru Jain, Rishabh Jain, Mohit Jain, Vishal Sharma, Utkarsh Sangam, Parul Singhal and Priyanshi Garg 12.1 Introduction 236 12.1.1 Different Types of Diseases 236 12.1.1.1 Diabetes (Madhumeha) and Its Types 236 12.1.1.2 TTH and Stress 237 12.1.1.3 Anxiety 237 12.1.1.4 Hypertension 237 12.1.2 Machine Vision 237 12.1.2.1 Medical Images and Analysis 238 12.1.2.2 Machine Learning in Healthcare 238 12.1.2.3 Artificial Intelligence in Healthcare 239 12.1.3 Big Data and Internet of Things (IoT) 239 12.1.4 Machine Learning in Association with Data Science and Analytics 239 12.1.5 Yajna Science 240 12.1.6 Mantra Science 240 12.1.6.1 Positive Impact of Recital of Gayatri Mantra and OM Chanting 241 12.1.6.2 Significance of Mantra on Indian Culture and Mythology 241 12.1.7 Usefulness and Positive Aspect of Yoga Asanas and Pranayama 241 12.1.8 Effects of Yajna and Mantra on Human Health 242 12.1.9 Impact of Yajna in Reducing the Atmospheric Solution 242 12.1.10 Scientific Study on Impact of Yajna on Air Purification 243 12.1.11 Scientific Meaning of Religious and Manglik Signs 244 12.2 Literature Survey 244 12.3 Methodology 246 12.4 Results and Discussion 249 12.5 Interpretations and Analysis 250 12.6 Novelty in Our Work 258 12.7 Recommendations 259 12.8 Future Scope and Possible Applications 260 12.9 Limitations 261 12.10 Conclusions 261 12.11 Acknowledgments 262 References 262 13 Collection and Analysis of Big Data From Emerging Technologies in Healthcare 269Nagashri K., Jayalakshmi D. S. and Geetha J. 13.1 Introduction 269 13.2 Data Collection 271 13.2.1 Emerging Technologies in Healthcare and Its Applications 271 13.2.1.1 RFID 272 13.2.1.2 WSN 273 13.2.1.3 IoT 274 13.2.2 Issues and Challenges in Data Collection 277 13.2.2.1 Data Quality 277 13.2.2.2 Data Quantity 277 13.2.2.3 Data Access 278 13.2.2.4 Data Provenance 278 13.2.2.5 Security 278 13.2.2.6 Other Challenges 279 13.3 Data Analysis 280 13.3.1 Data Analysis Approaches 280 13.3.1.1 Machine Learning 280 13.3.1.2 Deep Learning 281 13.3.1.3 Natural Language Processing 281 13.3.1.4 High-Performance Computing 281 13.3.1.5 Edge-Fog Computing 282 13.3.1.6 Real-Time Analytics 282 13.3.1.7 End-User Driven Analytics 282 13.3.1.8 Knowledge-Based Analytics 283 13.3.2 Issues and Challenges in Data Analysis 283 13.3.2.1 Multi-Modal Data 283 13.3.2.2 Complex Domain Knowledge 283 13.3.2.3 Highly Competent End-Users 283 13.3.2.4 Supporting Complex Decisions 283 13.3.2.5 Privacy 284 13.3.2.6 Other Challenges 284 13.4 Research Trends 284 13.5 Conclusion 286 References 286 14 A Complete Overview of Sign Language Recognition and Translation Systems 289Kasina Jyothi Swaroop, Janamejaya Channegowda and Shambhavi Mishra 14.1 Introduction 289 14.2 Sign Language Recognition 290 14.2.1 Fundamentals of Sign Language Recognition 290 14.2.2 Requirements for the Sign Language Recognition 292 14.3 Dataset Creation 293 14.3.1 American Sign Language 293 14.3.2 German Sign Language 296 14.3.3 Arabic Sign Language 297 14.3.4 Indian Sign Language 298 14.4 Hardware Employed for Sign Language Recognition 299 14.4.1 Glove/Sensor-Based Systems 299 14.4.2 Microsoft Kinect–Based Systems 300 14.5 Computer Vision–Based Sign Language Recognition and Translation Systems 302 14.5.1 Image Processing Techniques for Sign Language Recognition 302 14.5.2 Deep Learning Methods for Sign Language Recognition 304 14.5.3 Pose Estimation Application to Sign Language Recognition 305 14.5.4 Temporal Information in Sign Language Recognition and Translation 306 14.6 Sign Language Translation System—A Brief Overview 307 14.7 Conclusion 309 References 310 Index 315

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Book SynopsisTable of ContentsPreface xv Part I: Introduction 1 1 Machine Learning and Big Data: An Approach Toward Better Healthcare Services 3Nahid Sami and Asfia Aziz 1.1 Introduction 3 1.2 Machine Learning in Healthcare 4 1.3 Machine Learning Algorithms 6 1.3.1 Supervised Learning 6 1.3.2 Unsupervised Learning 7 1.3.3 Semi-Supervised Learning 7 1.3.4 Reinforcement Learning 8 1.3.5 Deep Learning 8 1.4 Big Data in Healthcare 8 1.5 Application of Big Data in Healthcare 9 1.5.1 Electronic Health Records 9 1.5.2 Helping in Diagnostics 9 1.5.3 Preventive Medicine 10 1.5.4 Precision Medicine 10 1.5.5 Medical Research 10 1.5.6 Cost Reduction 10 1.5.7 Population Health 10 1.5.8 Telemedicine 10 1.5.9 Equipment Maintenance 11 1.5.10 Improved Operational Efficiency 11 1.5.11 Outbreak Prediction 11 1.6 Challenges for Big Data 11 1.7 Conclusion 11 References 12 Part II: Medical Data Processing and Analysis 15 2 Thoracic Image Analysis Using Deep Learning 17Rakhi Wajgi, Jitendra V. Tembhurne and Dipak Wajgi 2.1 Introduction 18 2.2 Broad Overview of Research 19 2.2.1 Challenges 19 2.2.2 Performance Measuring Parameters 21 2.2.3 Availability of Datasets 21 2.3 Existing Models 23 2.4 Comparison of Existing Models 30 2.5 Summary 38 2.6 Conclusion and Future Scope 38 References 39 3 Feature Selection and Machine Learning Models for High-Dimensional Data: State-of-the-Art 43G. Manikandan and S. Abirami 3.1 Introduction 43 3.1.1 Motivation of the Dimensionality Reduction 45 3.1.2 Feature Selection and Feature Extraction 46 3.1.3 Objectives of the Feature Selection 47 3.1.4 Feature Selection Process 47 3.2 Types of Feature Selection 48 3.2.1 Filter Methods 49 3.2.1.1 Correlation-Based Feature Selection 49 3.2.1.2 The Fast Correlation-Based Filter 50 3.2.1.3 The INTERACT Algorithm 51 3.2.1.4 ReliefF 51 3.2.1.5 Minimum Redundancy Maximum Relevance 52 3.2.2 Wrapper Methods 52 3.2.3 Embedded Methods 53 3.2.4 Hybrid Methods 54 3.3 Machine Learning and Deep Learning Models 55 3.3.1 Restricted Boltzmann Machine 55 3.3.2 Autoencoder 56 3.3.3 Convolutional Neural Networks 57 3.3.4 Recurrent Neural Network 58 3.4 Real-World Applications and Scenario of Feature Selection 58 3.4.1 Microarray 58 3.4.2 Intrusion Detection 59 3.4.3 Text Categorization 59 3.5 Conclusion 59 References 60 4 A Smart Web Application for Symptom-Based Disease Detection and Prediction Using State-of-the-Art ML and ANN Models 65Parvej Reja Saleh and Eeshankur Saikia 4.1 Introduction 65 4.2 Literature Review 68 4.3 Dataset, EDA, and Data Processing 69 4.4 Machine Learning Algorithms 72 4.4.1 Multinomial Naïve Bayes Classifier 72 4.4.2 Support Vector Machine Classifier 72 4.4.3 Random Forest Classifier 73 4.4.4 K-Nearest Neighbor Classifier 74 4.4.5 Decision Tree Classifier 74 4.4.6 Logistic Regression Classifier 75 4.4.7 Multilayer Perceptron Classifier 76 4.5 Work Architecture 77 4.6 Conclusion 78 References 79 5 Classification of Heart Sound Signals Using Time-Frequency Image Texture Features 81Sujata Vyas, Mukesh D. Patil and Gajanan K. Birajdar 5.1 Introduction 81 5.1.1 Motivation 82 5.2 Related Work 83 5.3 Theoretical Background 84 5.3.1 Pre-Processing Techniques 84 5.3.2 Spectrogram Generation 85 5.3.2 Feature Extraction 88 5.3.4 Feature Selection 90 5.3.5 Support Vector Machine 91 5.4 Proposed Algorithm 91 5.5 Experimental Results 92 5.5.1 Database 92 5.5.2 Evaluation Metrics 94 5.5.3 Confusion Matrix 94 5.5.4 Results and Discussions 94 5.6 Conclusion 96 References 99 6 Improving Multi-Label Classification in Prototype Selection Scenario 103Himanshu Suyal and Avtar Singh 6.1 Introduction 103 6.2 Related Work 105 6.3 Methodology 106 6.3.1 Experiments and Evaluation 108 6.4 Performance Evaluation 108 6.5 Experiment Data Set 109 6.6 Experiment Results 110 6.7 Conclusion 117 References 117 7 A Machine Learning–Based Intelligent Computational Framework for the Prediction of Diabetes Disease 121Maqsood Hayat, Yar Muhammad and Muhammad Tahir 7.1 Introduction 121 7.2 Materials and Methods 123 7.2.1 Dataset 123 7.2.2 Proposed Framework for Diabetes System 124 7.2.3 Pre-Processing of Data 124 7.3 Machine Learning Classification Hypotheses 124 7.3.1 K-Nearest Neighbor 124 7.3.2 Decision Tree 125 7.3.3 Random Forest 126 7.3.4 Logistic Regression 126 7.3.5 Naïve Bayes 126 7.3.6 Support Vector Machine 126 7.3.7 Adaptive Boosting 126 7.3.8 Extra-Tree Classifier 127 7.4 Classifier Validation Method 127 7.4.1 K-Fold Cross-Validation Technique 127 7.5 Performance Evaluation Metrics 127 7.6 Results and Discussion 129 7.6.1 Performance of All Classifiers Using 5-Fold CV Method 129 7.6.2 Performance of All Classifiers Using the 7-Fold Cross-Validation Method 131 7.6.3 Performance of All Classifiers Using 10-Fold CV Method 133 7.7 Conclusion 137 References 137 8 Hyperparameter Tuning of Ensemble Classifiers Using Grid Search and Random Search for Prediction of Heart Disease 139Dhilsath Fathima M. and S. Justin Samuel 8.1 Introduction 140 8.2 Related Work 140 8.3 Proposed Method 142 8.3.1 Dataset Description 143 8.3.2 Ensemble Learners for Classification Modeling 144 8.3.2.1 Bagging Ensemble Learners 145 8.3.2.2 Boosting Ensemble Learner 147 8.3.3 Hyperparameter Tuning of Ensemble Learners 151 8.3.3.1 Grid Search Algorithm 151 8.3.3.2 Random Search Algorithm 152 8.4 Experimental Outcomes and Analyses 153 8.4.1 Characteristics of UCI Heart Disease Dataset 153 8.4.2 Experimental Result of Ensemble Learners and Performance Comparison 154 8.4.3 Analysis of Experimental Result 154 8.5 Conclusion 157 References 157 9 Computational Intelligence and Healthcare Informatics Part III—Recent Development and Advanced Methodologies 159Sankar Pariserum Perumal, Ganapathy Sannasi, Santhosh Kumar S.V.N. and Kannan Arputharaj 9.1 Introduction: Simulation in Healthcare 160 9.2 Need for a Healthcare Simulation Process 160 9.3 Types of Healthcare Simulations 161 9.4 AI in Healthcare Simulation 163 9.4.1 Machine Learning Models in Healthcare Simulation 163 9.4.1.1 Machine Learning Model for Post-Surgical Risk Prediction 163 9.4.2 Deep Learning Models in Healthcare Simulation 169 9.4.2.1 Bi-LSTM–Based Surgical Participant Prediction Model 170 9.5 Conclusion 174 References 174 10 Wolfram’s Cellular Automata Model in Health Informatics 179Sutapa Sarkar and Mousumi Saha 10.1 Introduction 179 10.2 Cellular Automata 181 10.3 Application of Cellular Automata in Health Science 183 10.4 Cellular Automata in Health Informatics 184 10.5 Health Informatics–Deep Learning–Cellular Automata 190 10.6 Conclusion 191 References 191 Part III: Machine Learning and COVID Prospective 193 11 COVID-19: Classification of Countries for Analysis and Prediction of Global Novel Corona Virus Infections Disease Using Data Mining Techniques 195Sachin Kamley, Shailesh Jaloree, R.S. Thakur and Kapil Saxena 11.1 Introduction 195 11.2 Literature Review 196 11.3 Data Pre-Processing 197 11.4 Proposed Methodologies 198 11.4.1 Simple Linear Regression 198 11.4.2 Association Rule Mining 202 11.4.3 Back Propagation Neural Network 203 11.5 Experimental Results 204 11.6 Conclusion and Future Scopes 211 References 212 12 Sentiment Analysis on Social Media for Emotional Prediction During COVID-19 Pandemic Using Efficient Machine Learning Approach 215Sivanantham Kalimuthu 12.1 Introduction 215 12.2 Literature Review 218 12.3 System Design 222 12.3.1 Extracting Feature With WMAR 224 12.4 Result and Discussion 229 12.5 Conclusion 232 References 232 13 Primary Healthcare Model for Remote Area Using Self-Organizing Map Network 235Sayan Das and Jaya Sil 13.1 Introduction 236 13.2 Background Details and Literature Review 239 13.2.1 Fuzzy Set 239 13.2.2 Self-Organizing Mapping 239 13.3 Methodology 240 13.3.1 Severity_Factor of Patient 244 13.3.2 Clustering by Self-Organizing Mapping 249 13.4 Results and Discussion 250 13.5 Conclusion 252 References 252 14 Face Mask Detection in Real-Time Video Stream Using Deep Learning 255Alok Negi and Krishan Kumar 14.1 Introduction 256 14.2 Related Work 257 14.3 Proposed Work 258 14.3.1 Dataset Description 258 14.3.2 Data Pre-Processing and Augmentation 258 14.3.3 VGG19 Architecture and Implementation 259 14.3.4 Face Mask Detection From Real-Time Video Stream 261 14.4 Results and Evaluation 262 14.5 Conclusion 267 References 267 15 A Computational Intelligence Approach for Skin Disease Identification Using Machine/Deep Learning Algorithms 269Swathi Jamjala Narayanan, Pranav Raj Jaiswal, Ariyan Chowdhury, Amitha Maria Joseph and Saurabh Ambar 15.1 Introduction 270 15.2 Research Problem Statements 274 15.3 Dataset Description 274 15.4 Machine Learning Technique Used for Skin Disease Identification 276 15.4.1 Logistic Regression 277 15.4.1.1 Logistic Regression Assumption 277 15.4.1.2 Logistic Sigmoid Function 277 15.4.1.3 Cost Function and Gradient Descent 278 15.4.2 SVM 279 15.4.3 Recurrent Neural Networks 281 15.4.4 Decision Tree Classification Algorithm 283 15.4.5 CNN 286 15.4.6 Random Forest 288 15.5 Result and Analysis 290 15.6 Conclusion 291 References 291 16 Asymptotic Patients’ Healthcare Monitoring and Identification of Health Ailments in Post COVID-19 Scenario 297Pushan K.R. Dutta, Akshay Vinayak and Simran Kumari 16.1 Introduction 298 16.1.1 Motivation 298 16.1.2 Contributions 299 16.1.3 Paper Organization 299 16.1.4 System Model Problem Formulation 299 16.1.5 Proposed Methodology 300 16.2 Material Properties and Design Specifications 301 16.2.1 Hardware Components 301 16.2.1.1 Microcontroller 301 16.2.1.2 ESP8266 Wi-Fi Shield 301 16.2.2 Sensors 301 16.2.2.1 Temperature Sensor (LM 35) 301 16.2.2.2 ECG Sensor (AD8232) 301 16.2.2.3 Pulse Sensor 301 16.2.2.4 GPS Module (NEO 6M V2) 302 16.2.2.5 Gyroscope (GY-521) 302 16.2.3 Software Components 302 16.2.3.1 Arduino Software 302 16.2.3.2 MySQL Database 302 16.2.3.3 Wireless Communication 302 16.3 Experimental Methods and Materials 303 16.3.1 Simulation Environment 303 16.3.1.1 System Hardware 303 16.3.1.2 Connection and Circuitry 304 16.3.1.3 Protocols Used 306 16.3.1.4 Libraries Used 307 16.4 Simulation Results 307 16.5 Conclusion 310 16.6 Abbreviations and Acronyms 310 References 311 17 COVID-19 Detection System Using Cellular Automata–Based Segmentation Techniques 313Rupashri Barik, M. Nazma B. J. Naskar and Sarbajyoti Mallik 17.1 Introduction 313 17.2 Literature Survey 314 17.2.1 Cellular Automata 315 17.2.2 Image Segmentation 316 17.2.3 Deep Learning Techniques 316 17.3 Proposed Methodology 317 17.4 Results and Discussion 320 17.5 Conclusion 322 References 322 18 Interesting Patterns From COVID-19 Dataset Using Graph-Based Statistical Analysis for Preventive Measures 325Abhilash C. B. and Kavi Mahesh 18.1 Introduction 326 18.2 Methods 326 18.2.1 Data 326 18.3 GSA Model: Graph-Based Statistical Analysis 327 18.4 Graph-Based Analysis 329 18.4.1 Modeling Your Data as a Graph 329 18.4.2 RDF for Knowledge Graph 331 18.4.3 Knowledge Graph Representation 331 18.4.4 RDF Triple for KaTrace 333 18.4.5 Cipher Query Operation on Knowledge Graph 335 18.4.5.1 Inter-District Travel 335 18.4.5.2 Patient 653 Spread Analysis 336 18.4.5.3 Spread Analysis Using Parent-Child Relationships 337 18.4.5.4 Delhi Congregation Attended the Patient’s Analysis 339 18.5 Machine Learning Techniques 339 18.5.1 Apriori Algorithm 339 18.5.2 Decision Tree Classifier 341 18.5.3 System Generated Facts on Pandas 343 18.5.4 Time Series Model 345 18.6 Exploratory Data Analysis 346 18.6.1 Statistical Inference 347 18.7 Conclusion 356 18.8 Limitations 356 Acknowledgments 356 Abbreviations 357 References 357 Part IV: Prospective of Computational Intelligence in Healthcare 359 19 Conceptualizing Tomorrow’s Healthcare Through Digitization 361Riddhi Chatterjee, Ratula Ray, Satya Ranjan Dash and Om Prakash Jena 19.1 Introduction 361 19.2 Importance of IoMT in Healthcare 362 19.3 Case Study I: An Integrated Telemedicine Platform in Wake of the COVID-19 Crisis 363 19.3.1 Introduction to the Case Study 363 19.3.2 Merits 363 19.3.3 Proposed Design 363 19.3.3.1 Homecare 363 19.3.3.2 Healthcare Provider 365 19.3.3.3 Community 367 19.4 Case Study II: A Smart Sleep Detection System to Track the Sleeping Pattern in Patients Suffering From Sleep Apnea 371 19.4.1 Introduction to the Case Study 371 19.4.2 Proposed Design 373 19.5 Future of Smart Healthcare 375 19.6 Conclusion 375 References 375 20 Domain Adaptation of Parts of Speech Annotators in Hindi Biomedical Corpus: An NLP Approach 377Pitambar Behera and Om Prakash Jena 20.1 Introduction 377 20.1.1 COVID-19 Pandemic Situation 378 20.1.2 Salient Characteristics of Biomedical Corpus 378 20.2 Review of Related Literature 379 20.2.1 Biomedical NLP Research 379 20.2.2 Domain Adaptation 379 20.2.3 POS Tagging in Hindi 380 20.3 Scope and Objectives 380 20.3.1 Research Questions 380 20.3.2 Research Problem 380 20.3.3 Objectives 381 20.4 Methodological Design 381 20.4.1 Method of Data Collection 381 20.4.2 Method of Data Annotation 381 20.4.2.1 The BIS Tagset 381 20.4.2.2 ILCI Semi-Automated Annotation Tool 382 20.4.2.3 IA Agreement 383 20.4.3 Method of Data Analysis 383 20.4.3.1 The Theory of Support Vector Machines 384 20.4.3.2 Experimental Setup 384 20.5 Evaluation 385 20.5.1 Error Analysis 386 20.5.2 Fleiss’ Kappa 388 20.6 Issues 388 20.7 Conclusion and Future Work 388 Acknowledgements 389 References 389 21 Application of Natural Language Processing in Healthcare 393Khushi Roy, Subhra Debdas, Sayantan Kundu, Shalini Chouhan, Shivangi Mohanty and Biswarup Biswas 21.1 Introduction 393 21.2 Evolution of Natural Language Processing 395 21.3 Outline of NLP in Medical Management 396 21.4 Levels of Natural Language Processing in Healthcare 397 21.5 Opportunities and Challenges From a Clinical Perspective 399 21.5.1 Application of Natural Language Processing in the Field of Medical Health Records 399 21.5.2 Using Natural Language Processing for Large-Sample Clinical Research 400 21.6 Openings and Difficulties From a Natural Language Processing Point of View 401 21.6.1 Methods for Developing Shareable Data 401 21.6.2 Intrinsic Evaluation and Representation Levels 402 21.6.3 Beyond Electronic Health Record Data 403 21.7 Actionable Guidance and Directions for the Future 403 21.8 Conclusion 406 References 406 Index 409

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Book SynopsisAlberta: A Health System Profile provides the first detailed description of Alberta’s health care system and the underpinning political and social forces that have shaped it. Drawing on significant wealth from government revenues generated through the energy sector, Alberta has been able to develop an extensive public health and health care infrastructure. Alberta has used its financial resources to attract health professionals by offering the highest levels of financial compensation in Canada. However, although it spends more per capita than other Canadian jurisdictions, Alberta’s health care system costs and health outcomes are mediocre compared to those of many other Canadian jurisdictions. This unexpected outcome is the consequence of the unique interplay of economic and political forces within Alberta’s political economy. Through an examination of Alberta’s political and economic history, and using research on the structures anTable of ContentsList of Figures, Tables, and Boxes Series Editor’s Foreword Preface and Acknowledgements List of Acronyms Chapter 1: Introduction and Overview 1.1 Geography and Demography 1.2 Political Context 1.3 Alberta’s Economy 1.4 Health Status 1.5 Conclusion Chapter 2: Organization and Regulation 2.1 Overview of the health system 2.1.1 Early History 2.1.2 Health System Restructuring 1993-2007 2.2 Organization of the Provincial Health System 2.2.1 Alberta Health 2.2.2 Alberta Health Services 2.2.3 Contractors 2.3 Health System Planning 2.4 Coverage and Benefits 2.4.1 Eligibility for Benefits 2.5 Regulation 2.5.1 Providers 2.5.2 Hospitals 2.5.3 Continuing Care 2.5.4 Public Health 2.5.5 Diagnostic Imaging 2.5.6 Prescription Drugs 2.5.7 Patient Health Information 2.6 Patients 2.7 Health Research 2.8 Summary Chapter 3: Health Expenditures and Financing 3.1 Health System Financing Flows 3.2 Health Expenditures and Trends 3.2.1 Payment Methods 3.2.2 Private and Out-of-Pocket Spending 3.3 Public Revenues 3.3.1 Provincial Own-Source Revenues 3.3.2 Federal Transfers 3.4 Summary Chapter 4: Physical Infrastructure 4.1 Hospitals and other treatment Facilities 4.1.1 Size and Geography 4.1.2 Ownership 4.1.3 Specialization 4.1.4 Structural condition 4.2 Long Term (Continuing Care) facilities 4.2.1 Size and Geography 4.2.2 Ownership 4.2.3 Specialization 4.2.4 Age and Design 4.3 Diagnostic Imaging and Laboratory (DIAL) services 4.3.1 Laboratory Services 4.3.2 Diagnostic Imaging 4.4 Public Health Facilities and Community Health Centres 4.5 Information and Communications Technology Infrastructure 4.5.1 Electronic Health Records (EHRs) and Electronic Medical Records (EMRs) 4.5.2 Telehealth 4.6 Health Research Infrastructure 4.7 Summary Chapter 5: Health Workforce 5.1 Main Workforce Challenges 5.2 Physicians 5.3 Regulated Nurses 5.4 Other Health Care Providers 5.4.1 Complimentary Health Care Providers 5.4.2 Pharmaceutical Workforce 5.4.3 Emergency Medical Workforce 5.4.4 Diagnostic Workforce 5.4.5 Rehabilitation Workforce 5.4.6 Dental Workforce 5.4.7 Eye Care Workforce 5.4.8 Public Health Workforce 5.5 Health Human Resource Planning and Collective Bargaining 5.5.1 HHR Planning 5.5.2 Collective Bargaining 5.6 Conclusion Chapter 6: Services and Programs Provided in Alberta’s Health System 6.1 Public Health Services 6.1.1 Public Health Nursing and Communicable Disease Control 6.1.2 Environment Health Services 6.1.3 Health Promotion 6.2 Primary Care 6.3 Acute (secondary, tertiary) care including emergency services 6.3.1 Emergency Services 6.4 Diagnostic Imaging and Laboratory Services 6.5 Long-term and Continuing Care services 6.5.1 Long-term care (LTC) 6.5.2 Home and Community Care 6.6 Prescription drugs 6.7 Occupation Health Services and Rehabilitation Care 6.7.1 Occupational Health and Safety 6.7.2 Rehabilitation Care 6.8 Mental Health Care and Addictions Services 6.9 Dental Health Care Services 6.10 Complementary and Alternative Medicines and Care 6.11 Targeted Services for Indigenous and/or Minority Groups 6.12 Palliative (end-of-life) care 6.12.1 Palliative Care 6.12.2 Medical Assistance in Dying 6.13 Summary Chapter 7: Recent Health Reforms 7.1 Alberta Health Services 7.2 Strategic Clinical Networks 7.3 Primary Care 7.4 Wait Times 7.5 Patient Safety 7.6 Patient Advocacy 7.7 Health Research 7.8 Conclusion Chapter 8: Assessing Alberta’s Health Care System 8.1 Stated Objectives of the Health System 8.2 Financial Protection and Equity 8.3 Health System and Service Outcomes 8.3.1 Access to Care 8.3.2 Wait Times 8.3.3 Patient Safety 8.4 User Experience and Satisfaction 8.4.1 Access 8.4.2 Satisfaction 8.4.3 Continuity of Care 8.5 Efficiency (technical and allocative) 8.5.1 Financial Costs 8.5.2 Utilization 8.5.3 Public Health 8.5.4 Integration 8.5.5 Resource Allocation 8.5.6 Mortality 8.6 Accountability 8.7 Information, Performance Measurement and Quality Assurance 8.8 Conclusion Chapter 9: Conclusion 9.1 The Economy and People 9.2 Health Care Costs 9.3 Health System Governance 9.4 Health Workforce 9.5 Professionalism 9.6 Infrastructure and Services 9.7 Performance Measurement 9.8 Performance Outcomes 9.9 Final Thoughts Afterwards References Index

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Book SynopsisAs individuals increasingly seek ways of accessing, understanding and sharing data about their own bodies, this book offers a critique of the popular claim that ‘more information’ equates to ‘better health’. In a study that redefines the public, academic and policy related debates around health, bodies, information and data, the authors consider the ways in which the phenomenon of self-diagnosis has created alternative worlds of knowledge and practises which are often at odds with professional medical advice. With a focus on data that concerns significant life changes, this book explores the potential challenges related to people’s changing relationships with traditional health systems as access to, and control over, data shifts.Trade Review“This is an original and timely text – an absolute pleasure to read and a unique contribution to the field.” Emma Rich, University of Bath''This book presents a compelling account of people's engagements with biosensors. Drawing on their long history of research in science and technology studies, the authors elucidate how people can be helped or disappointed by these new technologies.'' Deborah Lupton, University of New South WalesTable of ContentsIntroduction: What Does Biosensing Do? Fertility Biosensing Biosensing Stress Platform Biosensing and Post- Genomic Relatedness Biosensing in Old Age Conclusion: What Might Biosensing Do?

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Book SynopsisHealthcare systems around the world are grappling with several major challenges such as the growing prevalence of chronic diseases, rising healthcare costs, and a shortage of healthcare workers. Without action, the Organisation for Economic Co-operation and Development has estimated that the average public spending on healthcare costs will increase to 10% of GDP. The shortage of healthcare workers has been a long-standing issue in Europe, even before the COVID-19 pandemic. According to a 2018 report by the European Commission's Joint Research Centre, there was a shortage of over 1 million healthcare workers across the European Union. Such shortages have resulted in longer waiting times for patients, increased workload and stress for healthcare workers, and lower quality of care for patients. The pandemic has further underscored the importance of addressing this issue and ensuring that healthcare systems have adequate capacity to meet the needs of patients, both now and in the future. It is clear that the healthcare sector needs to undergo radical changes to ensure that future generations have easy access to quality care that is also affordable. One way to achieve this is by leveraging the increasing use of digitisation in the sector. According to a 2019 report by the European Commission, the volume of healthcare data in the EU could reach 2.8 zettabytes by 2020, with an annual growth rate of 36%. The question then is, how can we leverage the insights that lie within this vast amount of data to transform the healthcare sector and address its most pressing issues, namely cost, quality, and accessibility of care. This book captures the learnings from the BigMedilytics project, funded by the EC from 2018 to 2021. The project aimed to transform Europe’s healthcare sector by using state-of-the-art Big Data technologies to achieve breakthrough productivity in the sector by reducing cost, improving patient outcomes, and delivering better access to healthcare facilities, covering the entire Healthcare Continuum. The project executed 12 real-life, in-hospital, big data pilots across three different themes: (i) Population health and chronic disease management, (ii) Oncology, and (iii) Industrialisation of healthcare. The pilots spanned 8 European countries, the health data of 11 million patients, involved 35 consortium partners, and incorporated diverse data sets originating from the public health sector, insurance companies, IoT devices, pharmaceutical industry, and public data sets.

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