Tracking Medicine PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Tracking Medicine PDF full book. Access full book title Tracking Medicine by John E. Wennberg. Download full books in PDF and EPUB format.
Author: John E. Wennberg
Publisher: Oxford University Press
ISBN: 0199830851
Category : Medical
Languages : en
Pages : 341
Get Book Here
Book Description
Written by a groundbreaking figure of modern medical study, Tracking Medicine is an eye-opening introduction to the science of health care delivery, as well as a powerful argument for its relevance in shaping the future of our country. An indispensable resource for those involved in public health and health policy, this book uses Dr. Wennberg's pioneering research to provide a framework for understanding the health care crisis; and outlines a roadmap for real change in the future. It is also a useful tool for anyone interested in understanding and forming their own opinion on the current debate.
Author: John E. Wennberg
Publisher: Oxford University Press
ISBN: 0199830851
Category : Medical
Languages : en
Pages : 341
Get Book Here
Book Description
Written by a groundbreaking figure of modern medical study, Tracking Medicine is an eye-opening introduction to the science of health care delivery, as well as a powerful argument for its relevance in shaping the future of our country. An indispensable resource for those involved in public health and health policy, this book uses Dr. Wennberg's pioneering research to provide a framework for understanding the health care crisis; and outlines a roadmap for real change in the future. It is also a useful tool for anyone interested in understanding and forming their own opinion on the current debate.
Author: Ewout W. Steyerberg
Publisher: Springer
ISBN: 3030163997
Category : Medical
Languages : en
Pages : 574
Get Book Here
Book Description
The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies
Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 0309102162
Category : Medical
Languages : en
Pages : 273
Get Book Here
Book Description
The third installment in the Pathways to Quality Health Care series, Rewarding Provider Performance: Aligning Incentives in Medicare, continues to address the timely topic of the quality of health care in America. Each volume in the series effectively evaluates specific policy approaches within the context of improving the current operational framework of the health care system. The theme of this particular book is the staged introduction of pay for performance into Medicare. Pay for performance is a strategy that financially rewards health care providers for delivering high-quality care. Building on the findings and recommendations described in the two companion editions, Performance Measurement and Medicare's Quality Improvement Organization Program, this book offers options for implementing payment incentives to provide better value for America's health care investments. This book features conclusions and recommendations that will be useful to all stakeholders concerned with improving the quality and performance of the nation's health care system in both the public and private sectors.
Author: Gørill Haugan
Publisher: Springer Nature
ISBN: 3030631354
Category : Medical
Languages : en
Pages : 382
Get Book Here
Book Description
This open access textbook represents a vital contribution to global health education, offering insights into health promotion as part of patient care for bachelor’s and master’s students in health care (nurses, occupational therapists, physiotherapists, radiotherapists, social care workers etc.) as well as health care professionals, and providing an overview of the field of health science and health promotion for PhD students and researchers. Written by leading experts from seven countries in Europe, America, Africa and Asia, it first discusses the theory of health promotion and vital concepts. It then presents updated evidence-based health promotion approaches in different populations (people with chronic diseases, cancer, heart failure, dementia, mental disorders, long-term ICU patients, elderly individuals, families with newborn babies, palliative care patients) and examines different health promotion approaches integrated into primary care services. This edited scientific anthology provides much-needed knowledge, translating research into guidelines for practice. Today’s medical approaches are highly developed; however, patients are human beings with a wholeness of body-mind-spirit. As such, providing high-quality and effective health care requires a holistic physical-psychological-social-spiritual model of health care is required. A great number of patients, both in hospitals and in primary health care, suffer from the lack of a holistic oriented health approach: Their condition is treated, but they feel scared, helpless and lonely. Health promotion focuses on improving people’s health in spite of illnesses. Accordingly, health care that supports/promotes patients’ health by identifying their health resources will result in better patient outcomes: shorter hospital stays, less re-hospitalization, being better able to cope at home and improved well-being, which in turn lead to lower health-care costs. This scientific anthology is the first of its kind, in that it connects health promotion with the salutogenic theory of health throughout the chapters. the authors here expand the understanding of health promotion beyond health protection and disease prevention. The book focuses on describing and explaining salutogenesis as an umbrella concept, not only as the key concept of sense of coherence.
Author: Pradeep Singh
Publisher: John Wiley & Sons
ISBN: 1119821886
Category : Computers
Languages : en
Pages : 480
Get Book Here
Book Description
FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.
Author: Pat Langley
Publisher: Morgan Kaufmann
ISBN: 9781558603011
Category : Computers
Languages : en
Pages : 436
Get Book Here
Book Description
Machine learning is the computational study of algorithms that improve performance based on experience, and this book covers the basic issues of artificial intelligence. Individual sections introduce the basic concepts and problems in machine learning, describe algorithms, discuss adaptions of the learning methods to more complex problem-solving tasks and much more.
Author: Jude Hemanth
Publisher: Springer Nature
ISBN: 9811595097
Category : Technology & Engineering
Languages : en
Pages : 879
Get Book Here
Book Description
This book solicits the innovative research ideas and solutions for almost all the intelligent data intensive theories and application domains. The proliferation of various mobile and wireless communication networks has paved way to foster a high demand for intelligent data processing and communication technologies. The potential of data in wireless mobile networks is enormous, and it constitutes to improve the communication capabilities profoundly. As the networking and communication applications are becoming more intensive, the management of data resources and its flow between various storage and computing resources are posing significant research challenges to both ICT and data science community. The general scope of this book covers the design, architecture, modeling, software, infrastructure and applications of intelligent communication architectures and systems for big data or data-intensive applications. In particular, this book reports the novel and recent research works on big data, mobile and wireless networks, artificial intelligence, machine learning, social network mining, intelligent computing technologies, image analysis, robotics and autonomous systems, data security and privacy.
Author: Kishor Kumar Sadasivuni
Publisher: John Wiley & Sons
ISBN: 1119813034
Category : Medical
Languages : en
Pages : 356
Get Book Here
Book Description
PREDICTING HEART FAILURE Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods focuses on the mechanics and symptoms of heart failure and various approaches, including conventional and modern techniques to diagnose it. This book also provides a comprehensive but concise guide to all modern cardiological practice, emphasizing practical clinical management in many different contexts. Predicting Heart Failure supplies readers with trustworthy insights into all aspects of heart failure, including essential background information on clinical practice guidelines, in-depth, peer-reviewed articles, and broad coverage of this fast-moving field. Readers will also find: Discussion of the main characteristics of cardiovascular biosensors, along with their open issues for development and application Summary of the difficulties of wireless sensor communication and power transfer, and the utility of artificial intelligence in cardiology Coverage of data mining classification techniques, applied machine learning and advanced methods for estimating HF severity and diagnosing and predicting heart failure Discussion of the risks and issues associated with the remote monitoring system Assessment of the potential applications and future of implantable and wearable devices in heart failure prediction and detection Artificial intelligence in mobile monitoring technologies to provide clinicians with improved treatment options, ultimately easing access to healthcare by all patient populations. Providing the latest research data for the diagnosis and treatment of heart failure, Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods is an excellent resource for nurses, nurse practitioners, physician assistants, medical students, and general practitioners to gain a better understanding of bedside cardiology.
Author: Donald B. Rubin
Publisher: John Wiley & Sons
ISBN: 0470317361
Category : Mathematics
Languages : en
Pages : 258
Get Book Here
Book Description
Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing to such handle surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. After establishing that only standard complete-data methods are needed to analyze a multiply-imputed set, the text evaluates procedures in general circumstances, outlining specific procedures for creating imputations in both the ignorable and nonignorable cases. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modern statistics.
Author: A. Brahmananda Reddy
Publisher: Springer Nature
ISBN: 9811673896
Category : Technology & Engineering
Languages : en
Pages : 602
Get Book Here
Book Description
This book includes original, peer-reviewed research articles from International Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2021), held in VNR Vignana Jyoythi Institute of Engineering and Technology (VNR VJIET), Hyderabad, Telangana, India, during 13–14 August 2021. The book focuses on “Smart Innovations in Mezzanine Technologies, Data Analytics, Networks and Communication Systems” enlargements and reviews on the advanced topics in artificial intelligence, machine learning, data mining and big data computing, knowledge engineering, semantic Web, cloud computing, Internet on Things, cybersecurity, communication systems, and distributed computing and smart systems.
