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DE VEDVARENDE HVA' FOR NOGEN!?De vedvarende uforklarede fysiske symptomer anslås at gælde for 10-20 % af den danske befolkning, men selvom prævalensen er høj, er sygdomsgruppen præget af et manglende forfæste i det danske sundhedssystem.Denne bog er et opgør med de mest sejlivede myter inden for feltet. Den giver et overblik over den opdaterede tilgængelige viden og placerer de uforklarede symptomer i en aktuel samfundsmæssig kontekst.De vedvarende uforklarede symptomer udgør en stor udfordring i sundheds- og socialsystemet, og evidensbaseret viden er nødvendigt for at imødekomme de mange patienter og familiers legitime behov. Det drejer sig blandt andet om:FibromyalgiIrritabel tyktarmME/postviralt træthedssyndromPost comootio-syndrom/whiplashMultipel kemisk intoleranceFunktionelle bevægeforstyrrelserSygdomsangstBogen er opbygget med en beskrivende del, en klinisk orienteret del, diagnostiske værktøjer samt en beskrivelse af, hvordan en opdateret helhedsorienteret sygdomsmodel kan forstås. Den præsenterer et nutidigt vidensgrundlag for alle sundhedsprofessioner, der har med denne patientgruppe at gøre, herunder især de praktiserende læger, samt til patienter og pårørende.
I løbet af et livsforløb kommer de fleste mennesker ud for kriser i forbindelse med fx alvorlig sygdom, ulykker eller tab af deres nærmeste. Ofte mødes disse mennesker af professionelle i sundhedsvæsnet, for hvem det bliver naturligt at ville vække håb. Skal det lykkes, må den professionelle have et nuanceret blik for håbets betydning og være opmærksom på, hvad der kan styrke det enkelte menneskes håb. Helt grundlæggende må man også vide, hvad håb egentlig er.Denne bog samler og diskuterer en række centrale teorier om håb fra filosofi, psykologi og samfundsvidenskab. Undervejs sættes teorierne i relation til praksis, og da håb også har væsentlig betydning for den sundhedsprofessionelle selv, bliver bogens tema også belyst ud fra en aktuel samfundsmæssig sammenhæng. Bogen er med dens systematiske teoretiske fremstilling den første af sin art på dansk og er et tiltrængt bidrag til at nuancere forståelsen af håb som andet og mere end hverdagens håb om, at dette eller hint vil ske i fremtiden.Bogen retter sig i kraft af dens eksempelmateriale primært mod sundhedsprofessionerne men er relevant for alle fagprofessionelle, som beskæftiger sig med mennesker i sårbare situationer. Endelig kan bogen læses af alle, som ønsker at få en større forståelse for håbets mange nuancer.
Sundhedsstyrelsens beskrivelse af de 7 lægeroller har i høj grad præget opfattelsen af, hvad en professionel læge skal kunne i Danmark. Men hvordan træner man de forskellige lægeroller som studerende? Og hvilke elementer er vigtige i overgangen til lægerollen? "Lægens roller" giver både et overblik over og en guide til, hvordan man som studerende, yngre læge eller underviser arbejder målrettet med lægefaglig professionalisme. Bogen er ideel som lærebog i professionskurser ved medicinstudiet i København, Aarhus, Odense og Aalborg,men vil også være nyttig for nyuddannede læger, som ønsker at genopfriske viden og færdigheder indenfor f.eks. lovgivning, kommunikationsredskaber, ledelse og forskning. Bogen har fokus på rollerne som professionel, sundhedsfremmer, kommunikator, leder, samarbejder og akademiker, der støtter og supplerer rollen som medicinsk ekspert. I én bog får du en samlet guide til de vigtigste elementer i lægelig professionalisme. Derudover er bogen velegnet som inspirationskilde i forbindelse med jobstart og karrierevalg.
Brug for hjælp til biokemien?Nu kan FADL’s Forlag præsentere en planche med et samlet overblik over kroppens metaboliske reaktionsveje. Herunder citratcyklussen, som er helt central for kroppens anaboliske og kataboliske reaktioner. Kortet er overskueligt opbygget med forskellige farver – og er nem at have med og opbevare, da den er lamineret på begge sider. Så planchen holder også til kaffepletter i de travle eksamensperioder.
Medicinens verden er aldrig beskrevet så Enkelt og Logisk. Dette skal vi alle lære, inkl. selvbehandling af Autoimmune Sygdomme og Kræft. Ingen Rygsmerter eller Hovedpiner mere. Behandlingerne beskrevet helt i detaljer, også akut selvbehandling af kræft og for eksempel behandling af Nakkeskader og Piskesmældsskader. Læs evt. forordet på www.birgerfeldt.dk
AKUTTE MEDICINSKE TILSTANDE14. UDGAVEI over 45 år har AKUTTE MEDICINSKE TILSTANDE været den foretrukne kittelbog, og nu foreligger klassikeren i en helt ny og gennemrevideret 14. udgave. Siden seneste udgave udkom, har et nyt speciale i akut medicin set dagens lys, og i forbindelse med omstrukturering af akutområdet på landets afdelinger er bogen blevet endnu mere relevant. Alle bogens kapitler er opdaterede med nyeste nationale og internationale guidelines for akut diagnostik, monitorering og behandling. Derudover får du også FADL’s Forlags populære journalkoncept, psykiatrikoncept og kommunikationskoncept med.AKUTTE MEDICINSKE TILSTANDE er uundværlig for den medicinstuderende i klinik, ligesom den er velegnet til personalet på landets skadestuer, modtagecentre og intensivafdelinger.
Precision medicine holds great promise for the treatment of cancer and represents a unique opportunity for accelerated development and application of novel and repurposed therapeutic approaches. Current studies and clinical trials demonstrate the benefits of genomic profiling for patients whose cancer is driven by specific, targetable alterations. However, precision oncologists continue to be challenged by the widespread heterogeneity of cancer genomes and drug responses in designing personalized treatments. Chapters provide a comprehensive overview of the computational approaches, methods, and tools that enable precision oncology, as well as related biological concepts. Covered topics include genome sequencing, the architecture of a precision oncology workflow, and introduces cutting-edge research topics in the field of precision oncology. This book is intended for computational biologists, bioinformaticians, biostatisticians and computational pathologists working in precision oncology and related fields, including cancer genomics, systems biology, and immuno-oncology.
This extensively revised textbook describes and defines the US healthcare delivery system, its many systemic challenges and the prior efforts to develop and deploy informatics tools to help overcome these problems. Now that electronic health record systems are widely deployed, the HL7 Fast Healthcare Interoperability standard is being rapidly accepted as the means to access and share the data stored in those systems and analytics is increasing being used to gain new knowledge from that aggregated clinical data, this book goes on to discuss health informatics from an historical perspective, its current state and likely future state. It then turns to some of the important and evolving areas of informatics including electronic healt\h records, clinical decision support,. population and public health, mHealth and analytics. Numerous use cases and case studies are employed in all of these discussions to help readers connect the technologies to real world challenges.Health Informatics on FHIR: How HL7's API is Transforming Healthcare is for introductory health informatics courses for health sciences students (e.g., doctors, nurses, PhDs), the current health informatics community, computer science and IT professionals interested in learning about the field and practicing healthcare providers. Though this textbook covers an important new technology, it is accessible to non-technical readers including healthcare providers, their patients or anyone interested in the use of healthcare data for improved care, public/population health or research.
Adding to a growing body of knowledge about how the social-ecological dynamics of the Anthropocene affect human health, this collection presents strategies that both address core challenges, including climate change, stagnating economic growth, and rising socio-political instability, and offers novel frameworks for living well on a finite planet.Rather than directing readers to more sustainable ways to structure health systems, Health in the Anthropocene navigates the transition toward social-ecological systems that can support long-term human and environmental health, which requires broad shifts in thought and action, not only in formal health-related fields, but in our economic models, agriculture and food systems, ontologies, and ethics.Arguing that population health will largely be decided at the intersection of experimental social innovations and appropriate technologies, this volume calls readers to turn their attention toward social movements, practices, and ways of living that build resilience for an era of systemic change. Drawing on diverse disciplines and methodologies from fields including anthropology, ecological economics, sociology, and public health, Health in the Anthropocene maps out alternative pathways that have the potential to sustain human wellbeing and ecological integrity over the long term.
Industrial Tomography: Systems and Applications, Second Edition thoroughly explores the important techniques of industrial tomography, also discusses image reconstruction, systems, and applications. This book presents complex processes, including the way three-dimensional imaging is used to create multiple cross-sections, and how computer software helps monitor flows, filtering, mixing, drying processes, and chemical reactions inside vessels and pipelines. This book is suitable for materials scientists and engineers and applied physicists working in the photonics and optoelectronics industry or in the applications industries. Provides a comprehensive discussion on the different formats of tomography, including advances in visualization and data fusion Includes an excellent overview of image reconstruction using a wide range of applications Presents a comprehensive discussion of tomography systems and their applications in a wide variety of industrial processes
High-throughput sequencing and functional genomics technologies have given us the human genome sequence as well as those of other experimentally, medically, and agriculturally important species, and have enabled large-scale genotyping and gene expression profiling of human populations. Databases containing large numbers of sequences, polymorphisms, structures, and gene expression profiles of normal and diseased tissues are being rapidly generated for human and model organisms. Bioinformatics is thus rapidly growing in importance in the annotation of genomic sequences; the understanding of the interplay among and between genes and proteins; the analysis of genetic variability of species; the identification of pharmacological targets; and the inference of evolutionary origins, mechanisms, and relationships. This proceedings volume contains an up-to-date exchange of knowledge, ideas, and solutions to conceptual and practical issues of bioinformatics by researchers, professionals, and industrial practitioners at the 5th Asia-Pacific Bioinformatics Conference held in Hong Kong in January 2007.
High-throughput sequencing and functional genomics technologies have given us the human genome sequence as well as those of other experimentally, medically, and agriculturally important species, thus enabling large-scale genotyping and gene expression profiling of human populations. Databases containing large numbers of sequences, polymorphisms, structures, metabolic pathways, and gene expression profiles of normal and diseased tissues are rapidly being generated for human and model organisms. Bioinformatics is therefore gaining importance in the annotation of genomic sequences; the understanding of the interplay among and between genes and proteins; the analysis of the genetic variability of species; the identification of pharmacological targets; and the inference of evolutionary origins, mechanisms, and relationships. This proceedings volume contains an up-to-date exchange of knowledge, ideas, and solutions to conceptual and practical issues of bioinformatics by researchers, professionals, and industry practitioners at the 6th Asia-Pacific Bioinformatics Conference held in Kyoto, Japan, in January 2008.
Artificial Intelligence Applications in Human Pathology deals with the latest topics in biomedical research and clinical cancer diagnostics. With chapters provided by true international experts in the field, this book gives real examples of the implementation of AI and machine learning in human pathology.Advances in machine learning and AI in general have propelled computational and general pathology research. Today, computer systems approach the diagnostic levels achieved by humans for certain well-defined tasks in pathology. At the same time, pathologists are faced with an increased workload both quantitatively (numbers of cases) and qualitatively (the amount of work per case, with increasing treatment options and the type of data delivered by pathologists also expected to become more fine-grained). AI will support and leverage mathematical tools and implement data-driven methods as a center for data interpretation in modern tissue diagnosis and pathology. Digital or computational pathology will also foster the training of future computational pathologists, those with both pathology and non-pathology backgrounds, who will eventually decide that AI-based pathology will serve as an indispensable hub for data-related research in a global health care system.Some of the specific topics explored within include an introduction to DL as applied to Pathology, Standardized Tissue Sampling for Automated Analysis, integrating Computational Pathology into Histopathology workflows. Readers will also find examples of specific techniques applied to specific diseases that will aid their research and treatments including but not limited to; Tissue Cartography for Colorectal Cancer, Ki-67 Measurements in Breast Cancer, and Light-Sheet Microscopy as applied to Virtual Histology.The key role for pathologists in tissue diagnostics will prevail and even expand through interdisciplinary work and the intuitive use of an advanced and interoperating (AI-supported) pathology workflow delivering novel and complex features that will serve the understanding of individual diseases and of course the patient.
Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life.Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains.Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere.This book's material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.
Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorough introduction to the biological and technical principles behind multiplatform high-throughput experimentation.
BIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology. Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformatician s important tools and examines how they are used to evaluate biological data and advance disease knowledge. The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information. Audience The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues.
Applied 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 research directions and highlight the smart technologies that are central to establishing proactive health management, supporting enhanced coordination of care, and improving the overall quality of healthcare services. Provides an overview of different deep learning applications for intelligent healthcare informatics management Describes novel methodologies and emerging trends in artificial intelligence and computational intelligence and their relevance to health information engineering and management Proposes IoT solutions that disseminate essential medical information for intelligent healthcare management Discusses mobile-based healthcare management, content-based image retrieval, and computer-aided diagnosis using machine and deep learning techniques Examines the use of exploratory data analysis in intelligent healthcare informatics systems Applied Smart Health Care Informatics: A Computational Intelligence Perspective is an invaluable text for graduate students, postdoctoral researchers, academic lecturers, and industry professionals working in the area of healthcare and intelligent soft computing.
The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis. The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images. This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector.Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines DL theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalitiesDr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India. Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India. Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).
Tremendous growth in healthcare treatment techniques and methods has led to the emergence of numerous storage and communication problems and need for security among vendors and patients. This book brings together latest applications and state-of-the-art developments in healthcare sector using Blockchain technology. It explains how blockchain can enhance security, privacy, interoperability, and data accessibility including AI with blockchains, blockchains for medical imaging to supply chain management, and centralized management/clearing houses alongside DLT. Features:Includes theoretical concepts, empirical studies and detailed overview of various aspects related to development of healthcare applications from a reliable, trusted, and secure data transmission perspective.Provide insights on business applications of Blockchain, particularly in the healthcare sector.Explores how Blockchain can solve the transparency issues in the clinical research.Discusses AI with Blockchains, ranging from medical imaging to supply chain management.Reviews benchmark testing of AI with Blockchains and its impacts upon medical uses.a This book aims at researchers and graduate students in healthcare information systems, computer and electrical engineering.
The book, Transformation in Healthcare with Emerging Technologies, presents healthcare industrial revolution based on service aggregation and virtualisation that can transform the healthcare sector with the aid of technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Bigdata and Blockchain. These technologies offer fast communication between doctors and patients, protected transactions, safe data storage and analysis, immutable data records, transparent data flow service, transaction validation process, and secure data exchanges between organizations.Features: Discusses the Integration of AI, IoT, big data and blockchain in healthcare industry Highlights the security and privacy aspect of AI, IoT, big data and blockchain in healthcare industry Talks about challenges and issues of AI, IoT, big data and blockchain in healthcare industry Includes several case studiesIt is primarily aimed at graduates and researchers in computer science and IT who are doing collaborative research with the medical industry. Industry professionals will also find it useful.
Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features:Covers the fundamentals of ML and DL in the context of healthcare applicationsDiscusses various data collection approaches from various sources and how to use them in ML/DL modelsIntegrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the fieldExplores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analyticsEmphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitlyThis book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios.Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India.Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.
This book focuses on the applications of different digital platforms in the field of healthcare. It describes different devices used in digital healthcare, their benefits, diagnosis, use in treatment, and use cases related to mobile healthcare. Further, it covers machine and deep learning, blockchain technology, big data analytics as relevant to digital healthcare, telehealth technology, and digital applications in the field of push-and-pull pharma marketing. Overall, it enables readers to understand the basics of decision-making processes using digital techniques for the healthcare field.Features:Discusses various aspects of digitization of healthcare systemsExamines deployment of machine learning including IoT and medical analyticsProvides studies on the design, implementation, development, and management of intelligent healthcare systemsIncludes sensor-based digitization of healthcare dataReviews real-time advancement and challenges of digital communication in the field of healthcareThis book is aimed at researchers and graduate students in healthcare, internet of things, machine learning, computer science, robotics, wearables, electrical engineering, and biomedical engineering.
Computational Modelling of Biomechanics and Biotribology in the Musculoskeletal System reviews how a wide range of materials are modelled and how this modelling is applied. Computational modelling is increasingly important in the design and manufacture of biomedical materials, as it makes it possible to predict certain implant-tissue reactions, degradation, and wear, and allows more accurate tailoring of materials' properties for the in vivo environment. Part I introduces generic modelling of biomechanics and biotribology with a chapter on the fundamentals of computational modelling of biomechanics in the musculoskeletal system, and a further chapter on finite element modelling in the musculoskeletal system. Chapters in Part II focus on computational modelling of musculoskeletal cells and tissues, including cell mechanics, soft tissues and ligaments, muscle biomechanics, articular cartilage, bone and bone remodelling, and fracture processes in bones. Part III highlights computational modelling of orthopedic biomaterials and interfaces, including fatigue of bone cement, fracture processes in orthopedic implants, and cementless cup fixation in total hip arthroplasty (THA). Finally, chapters in Part IV discuss applications of computational modelling for joint replacements and tissue scaffolds, specifically hip implants, knee implants, and spinal implants; and computer aided design and finite element modelling of bone tissue scaffolds. This book is a comprehensive resource for professionals in the biomedical market, materials scientists and mechanical engineers, and those in academia. Covers generic modelling of cells and tissues; modelling of biomaterials and interfaces; biomechanics and biotribologyDiscusses applications of modelling for joint replacements and applications of computational modelling in tissue engineering
About the BookThe book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing.Salient Features of the BookExhaustive coverage of Data Analysis using RReal-life healthcare models for:Visually ImpairedDisease Diagnosis and Treatment optionsApplications of Big Data and Deep Learning in HealthcareDrug DiscoveryComplete guide to learn the knowledge discovery process, build versatile real life healthcare applicationsCompare and analyze recent healthcare technologies and trendsTarget Audience This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.
Practical Biostatistics: A Step-by-Step Approach for Evidence-Based Medicine, Second Edition presents a complete resource of biostatistical knowledge meant for health sciences students, researchers and health care professionals. The book's content covers the investigator's hypothesis, collective health, observational studies, the biostatistics of intervention studies, clinical trials and additional concepts. Chapters are written in a didactic way, making them easier to comprehend by readers with little or no background on statistics. Evidence-based medicine aims to apply the best available evidence gained from the scientific method to medical decision-making using statistical analyses of scientific methods and outcomes to drive further experimentation and diagnosis. With a detailed outline of implementation steps complemented by a review of important topics, this book can be used as a quick reference or hands-on guide on how to effectively incorporate biostatistics in clinical trials and research projects. Explains biostatistics in a didactic way for students, researchers and professionals of health sciences with little or no background on mathematics Presents a new section dedicated to epidemiology and public health, broadening content from the previous edition Written by medical doctors with vast experience on biostatistics and teaching who develop the content based on real cases for better applicability by readers
New technologies like AI, medical apps and implants seem very exciting but they too often have bugs and are susceptible to cyberattacks. Even well-established technologies like infusion pumps, pacemakers and radiotherapy aren't immune. Until digital healthcare improves, digital risk means that patients may be harmed unnecessarily, and healthcare staff will continue to be blamed for problems when it's not their fault. This book tells stories of widespread problems with digital healthcare. The stories inspire and challenge anyone who wants to make hospitals and healthcare better. The stories and their resolutions will empower patients, clinical staff and digital developers to help transform digital healthcare to make it safer and more effective. This book is not just about the bugs and cybersecurity threats that affect digital healthcare. More importantly, it's about the solutions that can make digital healthcare much safer.
This practical guide is designed for students and researchers with an existing knowledge of R who wish to learn how to apply it in an epidemiological context and exploit its versatility. It also serves as a broader introduction to the quantitative aspects of modern practical epidemiology. The standard tools used in epidemiology are described and the practical use of R for these is clearly explained and laid out. R code examples, many with output, are embeddedthroughout the text. The entire code is also available on the companion website so that readers can reproduce all the results and graphs featured in the book. Epidemiology with R is an advanced textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of human and non-human epidemiology, public health, veterinary science, and biostatistics.
Distributional cost-effectiveness analysis aims to help health care and public health organisations make fairer decisions with better outcomes. Whereas standard cost-effectiveness analysis provides information about total costs and effects, distributional cost-effectiveness analysis provides additional information about fairness in the distribution of costs and effects - who gains, who loses, and by how much. It can also provide information about the trade-offsthat sometimes occur between efficiency objectives, such as improving total health, and equity objectives, such as reducing unfair inequality in health. This is a practical guide to a flexible suite of economic methods for quantifying the equity consequences of health programmes in high-, middle- and low-income countries. The methods can be tailored and combined in various ways to provide useful information to different decision-makers in different countries with different distributional equity concerns. The handbook is primarily aimed at postgraduate students and analysts specialising in cost-effectiveness analysis but is also accessible toa broader audience of health sector academics, practitioners, managers, policymakers and stakeholders. As well as offering an overview for research commissioners, users, and producers, the book includes systematic technical guidance on how to simulate and evaluate distributions, with accompanying hands-on spreadsheet training exercises, and discussions about how to handle uncertainty about facts and disagreement about values, and the future challenges facing this young and rapidly evolving field of study.