As in many other fields, biomedical engineers benefit from the use of computational intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even greater if there were scientific literature that specifically focused on the biomedical applications of computational intelligence techniques. The first comprehensive field-specific reference, Computational Intelligence in Biomedical Engineering provides a unique look at how techniques in CI can offer solutions in modelling, relationship pattern recognition, clustering, and other problems particular to the field. The authors begin with an overview of signal processing and machine learning approaches and continue on to introduce specific applications, which illustrate CI's importance in medical diagnosis and healthcare. They provide an extensive review of signal processing techniques commonly employed in the analysis of biomedical signals and in the improvement of signal to noise ratio. The text covers recent CI techniques for post processing ECG signals in the diagnosis of cardiovascular disease and as well as various studies with a particular focus on CI's potential as a tool for gait diagnostics. In addition to its detailed accounts of the most recent research, Computational Intelligence in Biomedical Engineering provides useful applications and information on the benefits of applying computation intelligence techniques to improve medical diagnostics.

Bayesian analyses have made important inroads in modern clinical research due, in part, to the incorporation of the traditional tools of noninformative priors as well as the modern innovations of adaptive randomization and predictive power. Presenting an introductory perspective to modern Bayesian procedures, Elementary Bayesian Biostatistics explores Bayesian principles and illustrates their application to healthcare research. Building on the basics of classic biostatistics and algebra, this easy-to-read book provides a clear overview of the subject. It focuses on the history and mathematical foundation of Bayesian procedures, before discussing their implementation in healthcare research from first principles. The author also elaborates on the current controversies between Bayesian and frequentist biostatisticians. The book concludes with recommendations for Bayesians to improve their standing in the clinical trials community. Calculus derivations are relegated to the appendices so as not to overly complicate the main text. As Bayesian methods gain more acceptance in healthcare, it is necessary for clinical scientists to understand Bayesian principles. Applying Bayesian analyses to modern healthcare research issues, this lucid introduction helps readers make the correct choices in the development of clinical research programs.

While biomedical researchers may be able to follow instructions in the manuals accompanying the statistical software packages, they do not always have sufficient knowledge to choose the appropriate statistical methods and correctly interpret their results. Statistical Thinking in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research: mathematical coupling, regression to the mean, collinearity, the reversal paradox, and statistical interaction. Statistical Thinking in Epidemiology is about thinking statistically when looking at problems in epidemiology. The authors focus on several methods and look at them in detail: specific examples in epidemiology illustrate how different model specifications can imply different causal relationships amongst variables, and model interpretation is undertaken with appropriate consideration of the context of implicit or explicit causal relationships. This book is intended for applied statisticians and epidemiologists, but can also be very useful for clinical and applied health researchers who want to have a better understanding of statistical thinking. Throughout the book, statistical software packages R and Stata are used for general statistical modeling, and Amos and Mplus are used for structural equation modeling.

In the past, disease pattern mapping depended on census tracts based on political units, such as states and counties. However, with the advent of geographic information systems (GIS), researchers can now achieve a new level of precision and flexibility in geographic locating. This emerging technology allows the mapping of many different kinds of geographies, including disease rates in relation to pollution sources. Geocoding Health Data presents a state-of-the-art discussion on the current technical and administrative developments in geographic information science. In particular, it discusses how geocoded residential addresses can be used to examine the spatial patterns of cancer incidence, staging, survival, and mortality. The book begins with an introduction of various codes and their uses, including census geographic, health area, and street level codes. It goes on to describe the specific application of geocodes to cancer, detailing methods, materials, and technical issues. The text illustrates how to compile data maps for analysis and addresses issues, such as mismatch correction and data quality. It describes the current state of geocoding practices and discusses the use of individually geocoded cancer incidences in spatial epidemiology, distance estimation and spatial accessibilities, and tips for handling non-geocoded cases. Special consideration is given to privacy and confidentiality issues by focusing on disclosure limitation methods. With recent disease outbreaks and escalating concerns about bioterrorism, interest in the application of GIS to individual data is growing. The fundamental concepts presented by this book are of great value to anyone trying to understand the causes, prevention, and control of cancer as well as a variety of other diseases.

"Provides current models, tools, and examples for the formulation and evaluation of scientific hypotheses in causal terms. Introduces a new method of model parametritization. Illustrates structural equations and graphical elements for complex causal systems."

Wildlife and the zoonotic pathogens they reservoir are the source of most emerging infectious diseases of humans. AIDS, hantavirus pulmonary syndrome, SARS, Monkeypox and the human ehrlichioses are a few examples of the devastating effect achieved by cross-species transmission of viral and bacterial pathogens of wildlife. Many factors contribute to the appearance and spread of a pathogen, including; changes in host/pathogen evolution and interaction, human demographics, behavior and technology, environmental factors, and the availability of health care and a public health infrastructure capable of providing surveillance and interventions aimed at disease prevention and control. Additionally, historical factors and the coalescence of particular circumstances modify the conditions by which pathogens and species have an opportunity to intermix, evolve and spread. This volume provides an overview of zoonotic pathogen emergence with an emphasis on the role of wildlife. The first sections of the book explore the mechanisms by which evolution, biology, pathology, ecology, history, and current context have driven the emergence of different zoonotic agents, the next sections provide specific example of disease emergence linked to wildlife, and the final section offers an overview of current methods directed at the surveillance, prevention and control of zoonotic pathogens at the level of the wildlife host and possible mechanisms to improve these activities. This book will be useful to microbiologists, ecologists, zoologists, entomologists as well as physicians and epidemiologists.

Cardiovascular Implications of Stress and Depression provides an in-depth examination on how exposure to stress influences risk for cardiovascular disease and how depression is associated with this relationship. This authoritative volume examines causal pathways linking stress, depression and cardiovascular disease. In addition, it provides mechanistic insights into how environmental stress can lead to cardiovascular diseases. Current information about mechanistic factors, clinical and epidemiological aspects, and management issues associated with stress/depression are presented. These insights demonstrate how the mechanisms behind chronic stress and depression lead to cardiovascular diseases. In addition, their role in existing diseases (such as obesity, hypertension, and diabetes) is explored.

Mathematical and Statistical Skills in the Biopharmaceutical Industry: A Pragmatic Approach describes a philosophy of efficient problem solving showcased using examples pertinent to the biostatistics function in clinical drug development. It was written to share a quintessence of the authors' experiences acquired during many years of relevant work in the biopharmaceutical industry. The book will be useful will be useful for biopharmaceutical industry statisticians at different seniority levels and for graduate students who consider a biostatistics-related career in this industry. Features: Describes a system of principles for pragmatic problem solving in clinical drug development. Discusses differences in the work of a biostatistician in small pharma and big pharma. Explains the importance/relevance of statistical programming and data management for biostatistics and necessity for integration on various levels. Describes some useful statistical background that can be capitalized upon in the drug development enterprise. Explains some hot topics and current trends in biostatistics in simple, non-technical terms. Discusses incompleteness of any system of standard operating procedures, rules and regulations. Provides a classification of scoring systems and proposes a novel approach for evaluation of the safety outcome for a completed randomized clinical trial. Presents applications of the problem solving philosophy in a highly problematic transfusion field where many investigational compounds have failed. Discusses realistic planning of open-ended projects.

An essential collection that advances our understanding of how cities influence our health More than half the world's population lives in cities - a figure that will grow to two-thirds by 2030. As global populations rapidly consolidate around urban centers, the scientific understanding of what this means for human health faces a new and greater urgency. Urban Health connects urban exposures - the experiences, choices, and behaviors shaped by living in a city - to their impact on population health. By using the ubiquitous aspects of the urban experience as a lens to study these exposures across borders and demographics, it offers a new, scalable framework for understanding health and disease. Its applications to public health, epidemiology, and social science are virtually unlimited. Enriched with case studies that consider the state of health in cities all over the world, this book does more than capture the state of a nascent field; it holds a critical mirror to itself, considering the next decade and arming a new generation with the tools for research and practice.

How does pollution impact our daily quality of life? What are the effects of pollution on children's development? Why do industry and environmental experts disagree about what levels of pollutants are safe? This clearly written book, traces the debates over five key pollutants - lead, mercury, noise, pesticides, and dioxins and PCBs - and provides an overview of the history of each pollutant, basic research findings, and the scientific and regulatory controversies surrounding it. It focuses, in particular, on the impact of these pollutants on children's psychological development, their intellectual functioning, behaviour, and emotional states. Only by understanding the impact of pollution can we prevent future negative effects on quality of life and even pollution disasters from occurring. This volume will be of great interest to parents, child health care experts, public health officials, regulators, and health and environmental lawyers.

There is an increasing need for educational resources for statisticians and investigators. Reflecting this, the goal of this book is to provide readers with a sound foundation in the statistical design, conduct, and analysis of clinical trials. Furthermore, it is intended as a guide for statisticians and investigators with minimal clinical trial experience who are interested in pursuing a career in this area. The advancement in genetic and molecular technologies have revolutionized drug development. In recent years, clinical trials have become increasingly sophisticated as they incorporate genomic studies, and efficient designs (such as basket and umbrella trials) have permeated the field. This book offers the requisite background and expert guidance for the innovative statistical design and analysis of clinical trials in oncology. Key Features: Cutting-edge topics with appropriate technical background Built around case studies which give the work a "hands-on" approach Real examples of flaws in previously reported clinical trials and how to avoid them Access to statistical code on the book's website Chapters written by internationally recognized statisticians from academia and pharmaceutical companies Carefully edited to ensure consistency in style, level, and approach Topics covered include innovating phase I and II designs, trials in immune-oncology and rare diseases, among many others

The success of the Apgar score demonstrates the astounding power of an appropriate clinical instrument. This down-to-earth book provides practical advice, underpinned by theoretical principles, on developing and evaluating measurement instruments in all fields of medicine. It equips you to choose the most appropriate instrument for specific purposes. The book covers measurement theories, methods and criteria for evaluating and selecting instruments. It provides methods to assess measurement properties, such as reliability, validity and responsiveness, and interpret the results. Worked examples and end-of-chapter assignments use real data and well-known instruments to build your skills at implementation and interpretation through hands-on analysis of real-life cases. All data and solutions are available online. This is a perfect course book for students and a perfect companion for professionals/researchers in the medical and health sciences who care about the quality and meaning of the measurements they perform.

This updated second edition of Molecular Typing in Bacterial Infections, presented in two volumes, covers both common and neglected bacterial pathogenic agents, highlighting the most effective methods for their identification and classification in the light of their specific epidemiology. New chapters have been included to add new species, as well as another view of how bacterial typing can be used. These books are valuable resources for the molecular typing of infectious disease agents encountered in both research and hospital clinical laboratory settings, as well as in culture collections and in the industry. Each of the 21 chapters provides an overview of specific molecular approaches to efficiently detect and type different bacterial pathogens. The chapters are grouped in five parts, covering respiratory and urogenital pathogens (Volume I), and gastrointestinal and healthcare-associated pathogens, as well as a new group of vector-borne and Biosafety level 3 pathogens including a description of typing methods used in the traditional microbiology laboratory in comparison to molecular methods of epidemiology (Volume II). Comprehensive and updated, Molecular Typing in Bacterial Infections provides state-of-the-art methods for accurate diagnosis and for the correct classification of different types which will prove to be critical in unravelling the transmission routes of human pathogens.

This book describes a wide-ranging set of research approaches which have been used to study the health care problems of adults living in rural areas. It shows how these approaches can be used to define health care problems, measure levels of illness and health, and evaluate health care practices. For each approach, contributors provide a theoretical background from the health care delivery literature, details of how it can be carried out in the field, its strengths and weaknesses, and illustrative examples from both the literature and their own work.

Clinical Trial Optimization Using R explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development, through a series of examples and case studies. It provides the clinical researcher with a powerful evaluation paradigm, as well as supportive R tools, to evaluate and select among simultaneous competing designs or analysis options. It is applicable broadly to statisticians and other quantitative clinical trialists, who have an interest in optimizing clinical trials, clinical trial programs, or associated analytics and decision making. This book presents in depth the Clinical Scenario Evaluation (CSE) framework, and discusses optimization strategies, including the quantitative assessment of tradeoffs. A variety of common development challenges are evaluated as case studies, and used to show how this framework both simplifies and optimizes strategy selection. Specific settings include optimizing adaptive designs, multiplicity and subgroup analysis strategies, and overall development decision-making criteria around Go/No-Go. After this book, the reader will be equipped to extend the CSE framework to their particular development challenges as well.

"This is truly an outstanding book. [It] brings together all of the latest research in clinical trials methodology and how it can be applied to drug development.... Chang et al provide applications to industry-supported trials. This will allow statisticians in the industry community to take these methods seriously." Jay Herson, Johns Hopkins University The pharmaceutical industry's approach to drug discovery and development has rapidly transformed in the last decade from the more traditional Research and Development (R & D) approach to a more innovative approach in which strategies are employed to compress and optimize the clinical development plan and associated timelines. However, these strategies are generally being considered on an individual trial basis and not as part of a fully integrated overall development program. Such optimization at the trial level is somewhat near-sighted and does not ensure cost, time, or development efficiency of the overall program. This book seeks to address this imbalance by establishing a statistical framework for overall/global clinical development optimization and providing tactics and techniques to support such optimization, including clinical trial simulations. Provides a statistical framework for achieve global optimization in each phase of the drug development process. Describes specific techniques to support optimization including adaptive designs, precision medicine, survival-endpoints, dose finding and multiple testing. Gives practical approaches to handling missing data in clinical trials using SAS. Looks at key controversial issues from both a clinical and statistical perspective. Presents a generous number of case studies from multiple therapeutic areas that help motivate and illustrate the statistical methods introduced in the book. Puts great emphasis on software implementation of the statistical methods with multiple examples of software code (both SAS and R). It is important for statisticians to possess a deep knowledge of the drug development process beyond statistical considerations. For these reasons, this book incorporates both statistical and "clinical/medical" perspectives.

Including recent advances, this edition focuses on sustainable development and human welfare in biology, genetics, microbial biotechnology, and molecular medicine. While written for engineers specializing in biotechnology, those in agriculture, veterinary science, and medicine, will find new information relevant to their practice. It links biological principles to plant, animal, environmental, industrial, and medical biotechnologies, discusses concepts of genetics and molecular biology, and examines developments in the production of biopolymers, vaccines, gene therapy, bioremediation, biofuels, and biofertilizers.

Over the past decades, infectious disease epidemics have come to increasingly pose major global health challenges to humanity. The Anthropology of Epidemics approaches epidemics as total social phenomena: processes and events which encompass and exercise a transformational impact on social life whilst at the same time functioning as catalysts of shifts and ruptures as regards human/non-human relations. Bearing a particular mark on subject areas and questions which have recently come to shape developments in anthropological thinking, the volume brings epidemics to the forefront of anthropological debate, as an exemplary arena for social scientific study and analysis.

The U.S. healthcare system is in "complete chaos-disarray." Medical costs have increased significantly over the past 6 years with 70% increase for deductibles and 24% or more for health insurance premiums. All the while, workers earnings have either not increased or if they did, the pay raises were for less than the increase in the cost of medical care. The situation is unsustainable and the public wants the system fixed. This book offers ways of fixing the problems in healthcare. HEALTHCARE's OUT SICK - PREDICTING A CURE - Solutions that WORK !!!! first defines the "healthcare in crisis" problem. Through real patient experiences, the book describes the difficulties of getting through the maze of complexity among the plethora of "silo providers" which make up the industry. The heart of the book provides readers with a comprehensive solution that can work, a disruption that is necessary to provide Americans the medical care they need without the US public and healthcare providers and payors going into bankruptcy, insolvency or closure. This book delves into digitized medicine, payor and provider reimbursement models, and value-based healthcare delivery. It also includes a philosophy or mode of thinking and operation for the solutions that are needed for diagnosis-effective, cost-effective, and time-efficient healthcare delivery, of which digitized medicine, value-based care, and payor reimbursement modes are just some of the factors. The authors propose that the real solution involves having the patient at the center of the issues and changing from an archaic gold standard way of thinking to a "Predictive Analytic thinking" where one gets at the real truth by doing "real science" that in the end becomes effective not only for the population but for the individual person. This all leads to real person-centered and person-directed medicine and healthcare delivery.

The book examines the role of artificial intelligence during the COVID-19 pandemic, including its application in i) early warnings and alerts, ii) tracking and prediction, iii) data dashboards, iv) diagnosis and prognosis, v) treatments, and cures, and vi) social control. It explores the use of artificial intelligence in the context of population screening and assessing infection risks, and presents mathematical models for epidemic prediction of COVID-19. Furthermore, the book discusses artificial intelligence-mediated diagnosis, and how machine learning can help in the development of drugs to treat the disease. Lastly, it analyzes various artificial intelligence-based models to improve the critical care of COVID-19 patients.

The Eradication of Dracunculiasis (Guinea Worm Disease) in Nigeria: An Eyewitness Account documents the process used to eradicate one of the most neglected public health challenges in Nigeria. The book's chapters discuss the need for well developed and implemented eradication strategies, the availability of human and material resources, and the collaboration that is necessary with international partners. In addition, sections highlight challenges, the benefits of perseverance, and the international support and multi-sectoral approach that is needed to tackle national problems. It demonstrates that other endemic tropical diseases and conditions can be eliminated or controlled if a similar approach is adopted.

Whether you call them work-related upper limb disorders (WRULDs), cumulative trauma disorders (CTDS), or occupational overuse syndromes (OOSs), these conditions are a cause of pain, disability and suffering to workers worldwide. These designations often imply that their causes are related to work, but the supporting evidence can be unclear. Transparency is important, especially when it is necessary to form a connection with work factors to obtain treatment or compensation. This book addresses the dilemma. Written by a professional ergonomist with almost 40 years of experience in workplace ergonomics, this book combines a critical summary and assessment of the epidemiological literature with an exploration of the scientific and medical evidence for possible causal mechanisms to develop well-informed conclusions on causation of a number of common musculoskeletal disorders of the upper limb and intervertebral disc injury. Although much of the book focuses on physical factors, the role of psychosocial factors is increasingly being recognized and an additional chapter reviews a number of the current theories relating to this important issue. Features Focuses on a clear and authoritative account of the evidence for the role of work in the causation of commonly occurring ULDs and disc injury Provides an up-to-date compilation of the scientific evidence, devoid of views based on assumptions or prejudice Presents a clear explanation of the most likely causal mechanisms for common ULDs and disc injuries Includes a summary of theories concerning the role played by psychosocial factors Outlines the statistical evidence in a clear and understandable manner Bridges the gap between the evidence-base in the scientific and medical research literature and the practitioner

Theory of Drug Development presents a formal quantitative framework for understanding drug development that goes beyond simply describing the properties of the statistics in individual studies. It examines the drug development process from the perspectives of drug companies and regulatory agencies. By quantifying various ideas underlying drug development, the book shows how to systematically address problems, such as: Sizing a phase 2 trial and choosing the range of p-values that will trigger a follow-up phase 3 trial Deciding whether a drug should receive marketing approval based on its phase 2/3 development program and recent experience with other drugs in the same clinical area Determining the impact of adaptive designs on the quality of drugs that receive marketing approval Designing a phase 3 pivotal study that permits the data-driven adjustment of the treatment effect estimate Knowing when enough information has been gathered to show that a drug improves the survival time for the whole patient population Drawing on his extensive work as a statistician in the pharmaceutical industry, the author focuses on the efficient development of drugs and the quantification of evidence in drug development. He provides a rationale for underpowered phase 2 trials based on the notion of efficiency, which leads to the identification of an admissible family of phase 2 designs. He also develops a framework for evaluating the strength of evidence generated by clinical trials. This approach is based on the ratio of power to type 1 error and transcends typical Bayesian and frequentist statistical analyses.

A Practical Guide with Step-by-Step Explanations, Numerous Worked Examples, and R CodeThe A-Z of Error-Free Research describes the design, analysis, modeling, and reporting of experiments, clinical trials, and surveys. The book shows you when to use statistics, the best ways to cope with variation, and how to design an experiment, determine optimal sample size, and collect useable data. It also helps you choose the best statistical procedures for your application and takes you step by step through model development and reporting results for publication. Transition from Student to ResearcherHelping you become a confident researcher, the book begins with an overview of when-and when not-to use statistics. It guides you through the planning and data collection phases and presents various data analysis techniques, including methods for sample size determination. The author then covers techniques for developing models that provide a basis for future research. He also discusses reporting techniques to ensure your research efforts get the proper credit. The book concludes with case-control and cohort studies.

In Cancer Screening: A Practical Guide for Physicians, a panel of highly experienced clinicians and researchers from around the world present their up-to-date screening techniques for a wide variety of cancers. The techniques range from screening for breast, gynecological, and gastrointestinal cancers, to testing for urogenital, dermatological, and respiratory cancers. In addition to providing the busy practitioner with quick access to guidelines for particular cancers, the epidemiology and biology of the various cancers, as well as the sensitivity and specificity of the methods, are discussed in detail. Authoritative and physician-friendly, Cancer Screening: A Practical Guide for Physicians offers to all internists, oncologists, various subspecialists, and primary care physicians a concise practical review of cancer screening designed specifically for daily use in the consulting room.