Modern Inference Based On Health Related Markers

Modern Inference Based on Health Related Markers: Biomarkers and Statistical Decision Making provides a compendium of biomarkers based methodologies for respective health related fields and health related marker-specific biostatistical techniques. These methodologies may be applied to various problems encountered in medical and epidemiological studies. This book introduces correct and efficient testing mechanisms including procedures based on bootstrap and permutation methods with the aim of making these techniques assessable to practical researchers. In the biostatistical aspect, it describes how to correctly state testing problems, but it also includes novel results, which have appeared in current statistical publications. The book discusses also modern applied statistical developments that consider data-driven techniques, including empirical likelihood methods and other simple and efficient methods to derive statistical tools for use in health related studies. The title is a valuable source for biostaticians, practitioners, theoretical and applied investigators, and several members of the biomedical field who are interested in learning more about efficient evidence-based inference incorporating several forms of markers measurements. Combines modern epidemiological and public health discoveries with cutting-edge biostatistical tools, including relevant software codes, offering one full package to meet the demand of practical investigators Includes the emerging topics from real health fields in order to display recent advances and trends in Biomarkers and associated Decision Making areas Written by researchers who are leaders of Epidemiological and Biostatistical fields, presenting up-to-date investigations related to the measuring health issues, emerging fields of biomarkers, designing health studies and their implementations, clinical trials and their practices and applications, different aspects of genetic markers

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

This comprehensive text focuses on reasoning, critical thinking and pragmatic decision making in medicine. Based on the author’s extensive experience and filled with definitions, formulae, flowcharts and checklists, this fully revised second edition continues to provide invaluable guidance to the crucial role that clinical epidemiology plays in the expanding field of evidence-based medicine. Key Features: • Considers evidence-based medicine as a universal initiative common to all health sciences and professions, and all specialties within those disciplines • Demonstrates how effective practice is reliant on proper foundations, such as clinical and fundamental epidemiology, and biostatistics • Introduces the reader to basic epidemiological methods, meta-analysis and decision analysis • Shows that structured, modern, argumentative reasoning is required to build the best possible evidence and use it in practice and research • Outlines how to make the most appropriate decisions in clinical care, disease prevention and health promotion Presenting a range of topics seldom seen in a single resource, the innovative blend of informal logic and structured evidence-based reasoning makes this book invaluable for anyone seeking broad, in-depth and readable coverage of this complex and sometimes controversial field.

"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.

The German version of this work has a long tradition, and this fourth edition is the first to see an English version. Its main feature is the international approach regarding both authors and topics. The four internationally renowned editors were able to acquire the leading specialists for each field as contributors to the book. No less than 120 authors, half of them from non-German speaking countries, ensure an extremely high standard and that cross-cultural aspects are considered. Another major feature is that the book presents the evidence such that it may be examined from at least four different entry points -- via basic disciplines of psychiatric knowledge about groups defined by demographic criteria. Detailed linkages to other chapters allow the inclusion of neighbouring disciplines, such as the neurosciences and molecular biology. Contemporary Psychiatry is also unique in including chapters on psychiatric disorders caused by catastrophes, disasters etc. -- aspects totally neglected by normal textbooks. While this book gives an overall view of the state of the art of psychiatric knowledge, it even goes so far as to suggest future perspectives.

We live in a wireless society, one where convenience and accessibility determine the efficacy of the latest electronic gadgets and mobile devices. Making the most of these technologies—and ensuring their security against potential attackers—requires increased diligence in mobile technology research and development. Mobile Computing and Wireless Networks: Concepts, Methodologies, Tools, and Applications brings together a comprehensive range of voices and research in the area of mobile and wireless technologies, exploring the successes and failures, advantages and drawbacks, and benefits and limitations of the technology. With applications in a plethora of different research and topic areas, this multi-volume reference work benefits researchers, service providers, end-users, and information technology professionals. This four-volume reference work includes a diverse array of chapters and authors covering topics such as m-commerce, network ethics, mobile agent systems, mobile learning, communications infrastructure, and applications in fields such as business, healthcare, government, tourism, and more.

It is generally conceded that there are significant disparities in health and health care based on a person's racial and ethnic background. Infant mortality rates for black babies remain nearly two-and-one-half times higher than for whites. The life expectancy for black men and women remains at nearly one decade fewer years of life compared with their white counterparts. Rates of death attributable to heart disease, stroke, prostate and breast cancer remain much higher in black populations. Diabetes disease rates are more than 30 percent higher among Native Americans and Hispanics than among whites. Minorities remain grossly under-represented in the health professions workforce relative to their proportions in the population. In addition, despite a large and growing body of scientific evidence, many patients and providers remain unaware that racial and ethnic health care disparities are a problem and perceptions about health care inequalities vary between minorities and whites. closing the disparity gap. This book presents new research focusing on these disparities.