Medical and health informatics Books

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Book Synopsis

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Book Synopsis

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Book Synopsis

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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 SynopsisThe development and application of bioinformatics tools to basic and translational cancer research is, in fact, a rapidly expanding field that deserves a timely review. Therefore, a publication of this type is needed. The editors have done an excellent job in recruiting well-established scientists to author the various chapters of the book. Dieter Naf, Jackson Laboratory, USA Cancer bioinformatics is now emerging as a new interdisciplinary field, which is facilitating an unprecedented synthesis of knowledge arising from the life and clinical sciences. This groundbreaking title provides a comprehensive and up-to-date account of the enormous range of bioinformatics for cancer therapy development from the laboratory to clinical trials. It functions as a guide to integrated data exploitation and synergistic knowledge discovery, and support the consolidation of the interdisciplinary research community involved.Trade Review"…recommended for purchase for medical, academic or special libraries serving basic or clinical cancer researchers and bioinformaticists." (E-STREAMS, September 2007) "…good reading for anyone entering into some aspect of cancer research, whether it is biological, mathematical, or computational…" (Biometrics, December 2006) "Overall … an excellent and well-edited book that could be read from cover to cover or used as a reference." (British Journal of Healthcare Computing and Information Management, July 2006)Table of ContentsPreface. List of Contributors. SECTION I CANCER SYSTEMS. 1 A Path to Knowledge: from Data to Complex Systems Models of Cancer (Sylvia Nagl). 1.1 Conceptual foundations: biological complexity. 1.2 A taxonomy of cancer complexity. 1.3 Modelling and simulation of cancer systems. 1.4 Data standards and integration. 1.5 Concluding remarks. 2 Theory of Cancer Robustness (Hiroaki Kitano). 2.1 Robustness: the fundamental organizational principle of biological systems. 2.2 Underlying mechanisms for robustness. 2.3 Intrinsic features of robust systems: evolvability and trade-offs. 2.4 Cancer as a robust system. 2.5 Therapy strategies. 2.6 A proper index of treatment efficacy. 2.7 Computational tools. 2.8 Conclusion. 3 Developing an Integrated Informatics Platform for Cancer Research (Richard Begent). 3.1 Background. 3.2 The challenge. 3.3 The UK National Cancer Research Institute (NCRI) informatics platform. 3.4 Developing the informatics platform. 3.5 Benefits of the platform. 3.6 Conclusions. SECTION II In silico MODELS. 4 Mathematical Models of Cancer (Manish Patel and Sylvia Nagl). 4.1 Growth models. 4.2 A very brief tour of cellular automata. 4.3 Angiogenesis models. 4.4 Treatment response models. 4.5 Dynamic pathways models. 4.6 Other models. 4.7 Simulations of complex biological systems. 4.8 Concluding remarks. 5 Some Mathematical Modelling Challenges and Approaches in Cancer (Philip Maini and Robert A. Gatenby). 5.1 Introduction. 5.2 Multiscale modelling. 5.3 Tumour vascular modelling. 5.4 Population models. 5.5 Conclusion. 6 Computer Simulation of Tumour Response to Therapy (Georgios S. Stamatakos and Nikolaos Uzunoglu). 6.1 Introduction. 6.2 Tumour growth simulation. 6.3 Radiotherapy response simulation. 6.4 Chemotherapy response simulation. 6.5 Simulation of tumour response to other therapeutic modalities. 6.6 Simulation of normal tissue response to antineoplastic interventions. 6.7 Integration of molecular networks into tumour behaviour simulations. 6.8 Future directions. 7 Structural Bioinformatics in Cancer (Stephen Neidle). 7.1 Introduction. 7.2 Macromolecular crystallography. 7.3 Molecular modelling. 7.4 Conclusions. SECTION III In vivo MODELS. 8 The Mouse Tumour Biology Database: an Online Resource for Mouse Models of Human Cancer (Carol J. Bult, Debra M. Krupke, Matthew J. Vincent, Theresa Allio, John P. Sundberg, Igor Mikaelian and Janan T. Eppig). 8.1 Introduction. 8.2 Background. 8.3 Database content. 8.4 Data acquisition. 8.5 Using the MTB database. 8.6 Connecting the MTB database with related databases. 8.7 Summary. 9 Bioinformatics Approaches to Integrate Cancer Models and Human Cancer Research (Cheryl L. Marks and Sue Dubman). 9.1 Background. 9.2 The MMHCC Informatics at the outset of the programme. 9.3 Initial NCI bioinformatics infrastructure development. 9.4 Future directions for informatics support. 9.5 Summary. SECTION IV DATA. 10 The FAPESP/LICR Human Cancer Genome Project: Perspectives on Integration (Ricardo Brentani, Anamaria A. Camargo, Helena Brentani and Sandro J. De Souza). 10.1 Introduction. 10.2 The FAPESP/LICR Human Cancer Genome Project. 10.3 An integrated view of the tumour transcriptome. 10.4 Summary. 11 Today’s Science, Tomorrow’s Patient: the Pivotal Role of Tissue, Clinical Data and Informatics in Modern Drug Development (Kirstine Knox, Amanda Taylor and David J. Kerr). 11.1 Introduction. 11.2 A new national strategy for the provision of tissue annotated with clinical information to meet current and future needs of academic researchers and industry. 11.3 The NCRI National Cancer Tissue Resource for cancer biology and treatment development. 11.4 A potential future world-class resource integrating research and health service information systems and bioinformatics for cancer diagnosis and treatment. 11.5 A proposed information system architecture that will meet the challenges and deliver the required functionality: an overview. 11.6 Consent and confidentiality: ensuring that the NCTR is embedded in the UK’s legal and ethical framework. 11.7 Concluding remarks: future challenges and opportunities. SECTION V ETHICS. 12 Software Design Ethics for Biomedicine (Don Gotterbarn and Simon Rogerson). 12.1 The problem: software and research. 12.2 Risk identification. 12.3 Biomedical software example. 12.4 Is an ethical risk analysis required? 12.5 Details of SoDIS. 12.6 A SoDIS analysis of the biomedical software example. 12.7 Conclusion. 13 Ethical Issues of Electronic Patient Data and Informatics in Clinical Trial Settings (Dipak Kalra and David Ingram). 13.1 Introduction. 13.2 Ethical aspects of using patient-identifiable health data. 13.3 Legislation and policies pertaining to patient-identifiable health data. 13.4 Using anonymized and pseudonymized data. 13.5 Protecting personal health data. 14 Pharmacogenomics and Cancer: Ethical, Legal and Social Issues (Mary Anderlik Majumder and Mark Rothstein). 14.1 Introduction. 14.2 Getting pharmacogenomic tests and drugs to market. 14.3 Cost and coverage issues. 14.4 Ethical challenges of pharmacogenomics. 14.5 Conclusion. Index

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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 SynopsisIn an age when electronic health records (EHRs) are an increasingly important source of data, this essential textbook provides both practical and theoretical guidance to researchers conducting epidemiological or clinical analysis through EHRs.Table of Contents1: The Rise of Electronic Health Records. 2: Concepts in Electronic Health Record Research. Section I: EHR Data for Research. 3: Planning for Electronic Health Record Research. 4: Accessing Electronic Health Record Data. 5: Data Management. 6: Perils of Electronic Health Record Data. Section II: Epidemiology and Data Analysis. 7: Study Design and Sampling Strategies. 8: Epidemiologic Measures. 9: Bias and Validity in Observational Research. 10: Epidemiologic Analysis I. 11: Epidemiologic Analysis II. 12: Advanced and Emerging Methods and Applications. Section III: Interpretation to Application. 13: Publication and Presentation. 14: Applications of Electronic Health Record Research. 15: Case Studies in Electronic Health Record Research. Appendix 1: Secondary Data Research Planner. Appendix 2: Example Code using R.

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Book SynopsisIn an era of digital transformation within healthcare management, this important book outlines an ecosystem perspective to illustrate how a range of actors can use digital technologies to offer better value within the provision of healthcare services. From mobile applications to point-of-care diagnostic devices, from AI-enabled applications for data analysis to cloud models for service delivery and blockchain infrastructures, it provides a roadmap for how healthcare organizations can leverage these digital technologies. The book is also illustrated with case studies from different areas, including software for medical diagnostics, blockchain infrastructures for use in pharmaceutical supply chains and clinical trials, and federated learning platforms for genomics. Covering key issues such as patients' rights to data and written in the aftermath of the COVID-19 pandemic, the book will be essential reading for researchers, postgraduate students, and professionals interested in Trade Review“This is a valuable book that comes at a critical time, when digital health has accelerated across the globe fanned by the fire of the pandemic. It provides an informed account of the factors to consider and practical steps to make the most of the opportunity.”Tara Donelly, Founder Digital Care, Ex-Chief Digital Officer, NHS Digital“Digital healthcare is not merely a subset of traditional healthcare; it represents a reconfiguration of the entire healthcare system. Therefore, any effort to digitally transform organisations and effectively prepare them for the future must adopt an ecosystem approach. This book offers a practical framework for implementing this approach, highlighting the necessity for collective action as no single organisation can undertake this transformation alone.”Jorge Armanet, Entrepeneur, Operator Investor, and Advisor. Founder and Former CEO of HealthUnlocked. Cambridge Digital Innovation Fellow.“This book gives excellent insight into the benefits, risks and their mitigation of the use of digital technologies in healthcare. It then covers proposals on how we should regulate and respond to these new technologies to avoid the risks this new paradigm brings. I would recommend this book to anyone working in digital healthcare.”Stephen Critchlow, Founder, CEO and Chair of Wellbeing Team at Evergreen Life, Chair of the NIHR AI in Healthcare Board.“This book illustrates the key changes in the industry when COVID-19 accelerated digital healthcare. When I first presented patient portals in the UK it was something the NHS could not see would be used or adopted. Data silos cause so much complexity and create gaps in patient records. As these challenges are addressed we start to see the value being created and this is a process explored in this book. The book offers a comprehensive understanding of the complexity of digital transformation in healthcare.”Chris Rushworth, Head of Product, Doctor Care Anywhere“This is an important book. We are facing a global health crisis and the current ‘egosystem’ approach is never going to scale and offer the right economics to address the global need. This book illustrates how digital technologies can create and transform health ecosystems in way that was never possible and how these digital arenas change the economics of the market allowing greater participation and delivering additional value to those participants. The framework it offers provides a pragmatic guide to the journey to this ecosystem model, allow the reader to embark on the journey without the associated risks such undertakings often attract. Highly recommended.”Marcus Robbins, Chief Digital Advisor - Head of Strategy and Growth - DX Services – Fujitsu UK“This rich and engaging book about contemporary challenges in healthcare offers a unique perspective. Targeting practitioners and managers, it operationalises key insights from otherwise hard-to-get academic discussions by carefully selecting, presenting and illustrating these in accessible form -- without trivialising them.”Eric Monteiro, Professor of information systems, Norwegian University of Science and Technology“Professor Constantinides brilliantly navigates healthcare's digital transformation using powerful concepts and vivid examples. His framework illuminates how ecosystem strategy, technology, and human resources empower diverse actors to co-innovate and co-create value. Thoughtfully balancing the possibilities and risks of disruptive technologies like Generative AI and blockchain, he provides an indispensable roadmap for this evolving landscape. A must-read for anyone vested in the future of healthcare!”Arun Rai, Regents’ Professor and Howard S. Starks Distinguished Chair, Georgia State University“’Digital Transformation in Healthcare: An Ecosystem Approach’ by Professor Constantinides expertly traverses the healthcare landscape. Highlighting the importance of partnerships, digital platforms, and an ecosystem perspective, it offers strategic co-innovation insights for value creation. The book incisively illuminates the potentials of Generative AI, learning infrastructures, and blockchain technologies. An indispensable guide, the book offers profound insights into digital transformation for anyone in healthcare.”Elena Karahanna, Distinguished Research Professor and C. Herman & Mary Virginia Terry Distinguished Chair in Business Administration, University of Georgia“In Digital Transformation in Healthcare: An Ecosystem Approach, Panos Constantinides provides much-needed guidance for healthcare managers, indeed all managers. They take a “show, don’t tell” approach by methodically showing the steps, with examples, that organizations must follow to digitally transform their operations and strategy. The ecosystem approach is necessary for the future success of healthcare organizations so that they can focus on their core competency of providing health services in an efficient and effective manner.”Rajiv Kohli, John N. Dalton Memorial Professor of Business, Raymond A. Mason School of Business, William & Mary University“Corporations tend to focus on the efficiency gains from digital transformation but, often, fail to see the real transformative power that digital technologies provide: the ability to re-imagine the new possible to achieve what most actors from the established value chain would consider impossible. This timely and comprehensive book is all about the “impossible-new possible” in healthcare through digital transformation, and the enabling role played by digital technologies and ecosystems. Panos Constantinides presents a compelling framework for healthcare organizations to navigate the complex landscape of digital transformation. From telemedicine and blockchain to federated learning and generative AI technologies, this book provides a rich journey into how ecosystem approaches can activate collaborative partnerships and collective action to unlock innovation and drive transformative change in healthcare solutions. It is a “must read” for healthcare organizations, policymakers, and empowered patients alike who want to shape the new possible.”Carmelo Cennamo, Professor Copenhagen Business School"Digital Transformation in Healthcare: An Ecosystem Approach" tackles some of the most complex challenges in a critical industry and carefully develops the what, why, and how. Using concrete examples, the book lays out the key value propositions and architecture, makes the case for the need to change, and develops actionable strategies to make it happen while managing the risks and governance challenges that are sure to arise. I highly recommend it as a key manual to create positive change in healthcare.”Geoffrey Parker, Co-author of Platform Revolution, Charles E. Hutchinson '68A Professor of Engineering Innovation Dartmouth CollegeTable of ContentsPART 1. SETTING THE SCENE: THE HEALTHCARE CONTEXT. 1.Disruption and Digital Transformation in Healthcare. 2.Complexity in Healthcare Services. 3.Organizational Change and Digital Maturity. PART 2.DIGITAL TRANSFORMATION ACROSS HEALTHCARE ECOSYSTEMS. 4.An Ecosystem Approach to Digital Transformation. 5.Blockchain Infrastructures in Healthcare. 6.Cloud Computing and Federated Learning Infrastructures. PART 3.GENERATIVE TRANSFORMATION, THE RACE TO TECH ARMS AND REGULATION. 7.Generative Transformation and Regulatory Challenges.

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Book SynopsisTrue wellness innovation requires the recruitment of multi-disciplinary participants. This book breaks the mold with examples from healthcare experts and other professionals who have leveraged informatics to better the lives of their constituents. Jason Helgerson, Founder & CEO, Helgerson Solutions Group LLCDeveloped for those training in academic centers as well as for those already out in the field, this book looks at how attorneys, behavioral health experts, business development experts, chief information officers, chief medical officers, chief nursing information officers, consumer advocates, cryptographic experts, futurists, geneticists, informaticists, managed care executives, nurses, pharmacists, physicians, public health professionals, software developers, systems security officers, and workforce experts are collaborating on a team-based, IT-enabled approach to improve healthcare.Trade Review"Dr. Volpe has produced an excellent work in the field of informatics where the intersection of clinical pathways, technology, change management, and psychology cohabitate. Everyone interested in this field should read the appropriate chapters so that they are comfortable with this comprehensive field." Sam Amifar MD MS ABP-CI,CMIO CIO The Brooklyn Hospital Center"This text presents a multi-disciplinary view of health informatics, offering perspectives to help us better understand the successes, challenges, as well as opportunities towards a more equitable healthcare system that prioritizes patient care and public health."Vibhuti Arya, PharmD, MPH, FAPhA | Curating Brave SpacesGlobal Lead, Gender Equity and Diversity Workforce DevelopmentInternational Pharmaceutical Federation (FIP)Professor, St. John's University"Health Informatics Multidisciplinary Approaches for Current and Future Professionals is essential reading for policy makers, doctors, administrators and frontline healthcare workers. The book illustrates that deep integration by and between all aspects of care delivery, including IT, employee training and education, finance, and clinical practice will produce improved health outcomes and reduced costs."John August, Program Director, Partners Program, ILR Scheinman Institute, ILR School, Cornell University"As health care advances, there is more patient data in different platforms ranging from providers to insurance plans. The need for timely patient data exchange and integration is even more imperative to achieve a holistic picture of the patient from the realms of medical, behavioral health and social determinants of health services. This welcome edition brings together the many disciplines and perspectives to enhance the understanding of the many opportunities that informatics and health IT play towards transforming patient care and population health."Peggy Chan, MPH – former NYS DOH DSRIP Director"This book eclipses many others with the expansiveness of the contributions from so many accomplished authors. The foundation of 21st century healthcare is the use of effective and ethical use of informatics. The space has moved from niche to center stage - it is in this context that the next innovators in technology and patient care will find their opportunity."Joseph Conte, PhD, CPHQ, Executive Director Staten Island Performing Provider System"Dr. Volpe has created perhaps the first truly comprehensive book on Health Informatics in print. Its breadth of coverage is truly inspiring. It should become a required text for the rapidly increasing number of online, hybrid and in-person Masters' Degrees programs in Health Informatics becoming available both to recent college graduates and established professionals."James B. Couch, M.D., J.D., FACPEInterim Director, M.S. in Health Administration Degree ProgramFordham University"True wellness innovation requires the recruitment of multi-disciplinary participants. This book breaks the mold with examples from healthcare and other professionals who have leveraged informatics to better the lives of their constituents."Jason Helgerson, Founder & CEO, Helgerson Solutions Group LLC"I am saying nothing new when I say, "healthcare is complicated". Dr. Salvatore Volpe has assembled a team of many of America’s best-versed experts from a variety of disciplines and made complex, technical issues of today’s healthcare easy to understand. Not only informative, but a road map for developing best of class healthcare practices. A must read of healthcare leaders, present and future."Russ JonesExecutive AdvisorFelix Global"Whether for management of a global pandemic at the population level or treating individual patients in a high stake, fast-paced emergency department, the urgent imperative for better collection, analysis, and availability of actionable health data is abundantly clear. Through this valuable text, Dr. Volpe and his co-authors empower healthcare professionals to be catalysts and leaders equipped to create the robust, reliable, and interoperable health informatics processes and tools so urgently needed by patients, clinicians, and policymakers."Steven J. Stack, MD, MBA, FACEP"An interprofessional owner’s manual on Health Informatics for all health care providers! Educators must embrace the notion that the future of health care lies in encouraging technology and innovations and this book will benefit future nursing professionals to be practice ready when they graduate. Just what we need in higher education."Patricia A. Tooker, RN, DNPDean, Evelyn L. Spiro School of NursingWagner College"Information technology (IT) has revolutionized healthcare over the last decade and promises to transform healthcare for the foreseeable future. How can we harness an IT-enabled approach to address future health care challenges? How can IT be leveraged to prevent the next pandemic and for surveillance of other epidemics? How will IT rectify the inequities already present in the healthcare system? These are the questions at the core of this remarkable book. Health Informatics is required reading for anyone who plans on changing the future of healthcare. A must read for everyone!"Angelo Volandes, MD, President, ACP Decisions; Faculty, Harvard Medical School and Massachusetts General HospitalAretha Delight Davis, MD, JD, ACP Decisions, Co-Founder/Chief Executive Officer"Dr. Volpe makes an inspiring case for combining the principles of computer and information science with life sciences research, health professions, education, public health and patient care by recruiting a broad network of contributors to the field of Health Information Technologies. This book is a mind expander. Health Informatics will make you think differently about the future of healthcare and how multidisciplinary teams come together to help those in need."Aiyemobisi Williams, Co-founder, Massive Change Network and Co-host and creator of Health2049, a podcast about the future of health. "Advances in health information technology (IT) have impacted the healthcare stakeholders, especially during the COVID-19 pandemic. This book is a must-read for healthcare leaders who are helping endorse the development of cutting-edge health IT and integrating it into clinical practice! The book brings into focus strategic health IT opportunities for care improvements, enhancement of preventative care, optimization of patient care quality and outcomes, reduction of cost, and maintenance of provider and patient satisfaction, and more. It is a fantastic resource for current and future healthcare professionals!"Aleksandra Zagorin, DNP, MA, AGPCNP-BC, RNMaimonides Medical Center Department of Medicine and GeriatricsClinical Advisor and Scholar NYU Hartford Institute for Geriatric NursingDirector of Undergraduate Studies and Associate ProfessorEvelyn L. Spiro School of Nursing, Wagner College"Thank you, Dr. Volpe and co-authors for investing in this multi-disciplinary book that sheds a bright light on the opportunity and challenges of health informatics. Managed care organizations, health care delivery systems and community health workers can all benefit from understanding the new world of shared decision making, digital connection and use of data to drive meaningful insights and findings, while protecting patient privacy and promoting equity."Susan Beane, MD FACPExecutive Medical DirectorHealthfirst Partnerships – Medical Outcomes"Dr. Volpe is highly regarded not only for his extensive expertise working at the intersection of clinical care and informatics, but also as a student of the art and science of the field - an expert who readily identifies and learns from the insights of leaders across disciplines who are making change, every day. The result is this uniquely valuable compendium that will serve as the go-to resource for those seeking to understand all aspects health informatics to make care better, more efficient, and more effective."Amy Boutwell, MD, MPP, Founder and President, Collaborative Healthcare Strategies Table of ContentsChapter 1: The Value of Health IT – Nancy C. Beale, MSN, RN-BCChapter 2: Personal Health Engagement – Jan Oldenburg, FHIMSSChapter 3: Fostering Innovation in Health IT – Anuj Desai, MBAChapter 4: Ambulatory Systems: Electronic Health Records – Curtis L. Cole, MD, Adam D. Cheriff, MD, J. Travis Gossey, MD, MS, MPH, Sameer Malhotra, MD, MA, and Daniel M. Stein, MD, PhDChapter 5: Clinical Decision Support System – Parag Mehta, MD, FACPChapter 6: Medication Errors – J. Barmecha, MD, MPH, SFHM, FACPChapter 7: Racing against the Clock, Winning Back Time Spent in EHR – Parag Mehta, MD, FACPChapter 8: Hospital Systems: History and Rationale for Hospital Health IT – Virginia Lorenzi, MS, CPHIMS Chapter 9: Artificial Intelligence and Hospital Automation – Daniel J. Barchi, MEMChapter 10: Clinical and Business Intelligence – Ray Hess, MSA, RRT, FHIMSSChapter 11: Promoting Interoperability and Quality Payment Programs: The Evolving Paths of Meaningful Use – Anantachai (Tony) Panjamapirom, PhD, MBA, CPHIMS, Naomi Levinthal, MA, MS, CPHIMS, Ye Hoffman, MS, CPHIMChapter 12: Telebehavioral Health: Mental Health Landscape – Teresa Rufin, MPH, David Mou, MD, Thomas Tsang, MDChapter 13: Optimizing Medication Use through Health Information Technology: A Pharmacist’s Perspective – Troy Trygstad, PharmD, MBA, PhD; Mary Ann Kliethermes, B.S., Phar., Pharm D., FAPha, FCIOM; Anne Burns B.S. Pharm, RPh; Mary Roth McClurg, PharmD, MHS; Marie Smith, PharmD, FNAP; Jon Easter, BSPharm, RPhChapter 14: Nursing Informatics Today and Future Perspectives for Healthcare – Victoria L. Tiase, PhD, RN-BC and Whende M. Carroll, MSN, RN-BCChapter 15: Health Information Exchange: An Overview and New York State’s Model – Valerie Grey. M.A. and Nathan Donnelly, M.S. Chapter 16: Direct Interoperability Enhancing Transitions Across the Spectrum of Healthcare – Holly Miller, MD, MBA, FHIMSSChapter 17: Privacy and Security – Keith Richard Weiner, PhD, RN-BCChapter 18: Blockchain Primer – Paul Quigley, MBAChapter 19: IoT Is Watching You – Salvatore G. Volpe, MA; Paul Quigley, MBA Chapter 20: Case Study: New York City Department of Health and Mental Hygiene: Uses of Public Health Informatics in Response to COVID-19 Chapter 21: Genomic Informatics in Healthcare System – Chang-Hui Shen, PHDChapter 22: Managed Care Organizations Leverage Health Information from Multiple Sources to Drive Value – Michael Renzi, DO and Owen Moss, MPHChapter 23: Workforce Application of Informatics to Target Initiatives – William D. Myhre, MPAChapter 24: Patient-Centered Medical Home and Social Determinants of Health (SDoH) – Salvatore Volpe, MD FAAP, FACP ABP-CI, FHIMSS, CHCQM and Rick A. Moore, Ph.D.Chapter 25: eMOLST: Electronic System for Completing Medical Orders for Life-Sustaining Treatment – Patricia Bomba, M.D., M.A.C.P., F.R.C.P. and Katie Orem, M.P.H.Chapter 26: Medical Liability Insurance Data Analytics: An Opportunity to Identify Risks, Target Interventions, and Impact Policy – Thomas R. Gray, ESQ.Chapter 27: Medical-Legal: Attorney’s Perspective – Joshua R. Cohen, J.D.Chapter 28: Telehealth – Salvatore Volpe, MD, FAAP, FACP ABP-CI, FHIMSS, CHCQMChapter 29: Future Possibilities– Salvatore Volpe, MDChapter 29: Future Possibilities – Salvatore Volpe

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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 SynopsisThis innovative text uses a simulation approach to give readers interested in healthcare documentation and medical transcription careers a working knowledge of medical reports common in both acute and chronic care settings. Readers have access to transcription of 107 patient medical reports, including 56 new reports exclusive to the Eighth Edition. This edition also features 20 new speech recognition technology/medical editing (SRT) reports, as well as information on electronic health records (EHRs), quality assurance (QA), and scribes to keep readers up-to-date on the latest advances in the field. Organized by body system, the text includes full-color anatomy and physiology illustrations to make medical terminology easier to master. In addition, the authors have included a review of proper formatting, grammar, and style in accordance with the AHDI's BOOK OF STYLE, and a master glossary list compiles key terms in one section for convenient study and quick reference.Table of ContentsPreface. New to this Edition���Featured Items. Prerequisites. Course Description. Teaching Environment. Objectives. Student Text-Workbook. Audio Transcription Exercises. Supplements. Acknowledgments. About the Author. Supplements at a Glance. 1. Introduction. 2. Model Report Forms. 3. References. 4. Case Studies. Case Study 1: Reproductive System. Case Study 2: Gastrointestinal System. Case Study 3: Cardiopulmonary System. Case Study 4: Pediatric Orthopedics/Neurology Systems. Case Study 5: Psychology/Neurology System. Case Study 6: Reproductive System/Mammary Glands. Case Study 7:Orthopedics/Endocrine Systems. Case Study 8: Vascular/Renal Systems. Case Study 9: Orthopedics. Case Study 10: Respiratory System. 5. Quali-Care Clinic. 6. Speech Recognition Editing. Appendix. Proofreader���s Marks. Challenging Medical Words/Phrases/Prefixes. Sample Patient History Form. The Lund-Browder Chart. Laboratory Test Information. Sample Forms for Ordering Laboratory Tests, Scheduling Radiology Tests, and Consults for Physical Therapy, Sleep Studies, etc. Building a Reference Library. Official ���Do Not Use��� List from The Joint Commission. Bibliography. Index.

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Book SynopsisDespite all the jokes about the poor quality of physician handwriting, physician adoption of computerized provider order entry (CPOE) in hospitals still lags behind other industries'' use of technology. As of the end of 2010, less than 22% of hospitals had deployed CPOE. Yet experts claim that this technology reduces over 80% of medication errors and could prevent an estimated 522,000 serious medication errors annually in the US. Even though the federal government has offered $20 billion dollars in incentives to hospitals and health systems through the 2009 stimulus (the ARRA HITECH section of the American Recovery and Reinvestment Act of 2009), many organizations are struggling to implement advanced clinical information systems including CPOE. In addition, industry experts estimate that the healthcare industry is lacking as many as 40,000 persons with expertise in clinical informatics necessary to make it all happen by the 2016 deadline for these incentives. While the scientific litTable of ContentsForeword.- Introduction.- Why the Concern for CPOE Now?.- Vision: How You Start.- Leadership and Governance.- Project Management.- Change Management.- Building Momentum.- Avoiding Common Pitfalls along the Way.- Implementation.- Stabilization and Optimization.- Putting It All Together.- Appendix A Sample Forms and Other Tools.- Glossary.- Bibliography/Suggested Readings.- Index.​

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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 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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