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Our health care system is crippled by desperate efforts to prevent the inevitable. A third of the national Medicare budget -- nearly 175 billion -- is spent on the final year of life, and a third of that amount on the final month, often on expensive (and futile) treatments. Such efforts betray a fundamental flaw in how we think about healthcare: we squander resources on hopeless situations, instead of using them to actually improve health. In Predictive Health, distinguished doctors Kenneth Brigham and Michael M.E. Johns propose a solution: invest earlier -- and use science and technology to make healthcare more available and affordable. Every child would begin life with a post-natal genetic screen, when potential risk -- say for type II diabetes or heart disease -- would be found. More data on biology, behavior, and environment would be captured throughout her life. Using this information, health-care workers and the people they care for could forge personal strategies for healthier living long before a small glitch blows up into major disease. This real health care wouldn't just replace much of modern disease care -- it would make it obsolete. The result, according to Brigham and Johns, will be a life defined by a long stay at top physical and mental form, rather than an early peak and long decline. Accomplishing this goal will require new tools, new clinics, fewer doctors and more mentors, smarter companies, and engaged patients. In short, it will require a revolution. Thanks to a decade-long collaboration between Brigham, Johns and others, it is already underway. An optimistic plan for reducing or eliminating many chronic diseases as well as reforming our faltering medical system, Predictive Health is a deeply knowledgeable, deeply humane proposal for how we can reallocate expenses and resources to prolong the best years of life, rather than extending the worst.
Our health care system is crippled by desperate efforts to prevent the inevitable. A third of the national Medicare budget -- nearly 175 billion -- is spent on the final year of life, and a third of that amount on the final month, often on expensive (and futile) treatments. Such efforts betray a fundamental flaw in how we think about healthcare: we squander resources on hopeless situations, instead of using them to actually improve health. In Predictive Health, distinguished doctors Kenneth Brigham and Michael M.E. Johns propose a solution: invest earlier -- and use science and technology to make healthcare more available and affordable. Every child would begin life with a post-natal genetic screen, when potential risk -- say for type II diabetes or heart disease -- would be found. More data on biology, behavior, and environment would be captured throughout her life. Using this information, health-care workers and the people they care for could forge personal strategies for healthier living long before a small glitch blows up into major disease. This real health care wouldn't just replace much of modern disease care -- it would make it obsolete. The result, according to Brigham and Johns, will be a life defined by a long stay at top physical and mental form, rather than an early peak and long decline. Accomplishing this goal will require new tools, new clinics, fewer doctors and more mentors, smarter companies, and engaged patients. In short, it will require a revolution. Thanks to a decade-long collaboration between Brigham, Johns and others, it is already underway. An optimistic plan for reducing or eliminating many chronic diseases as well as reforming our faltering medical system, Predictive Health is a deeply knowledgeable, deeply humane proposal for how we can reallocate expenses and resources to prolong the best years of life, rather than extending the worst.
Predictive Intelligence in Biomedical and Health Informatics focuses on imaging, computer-aided diagnosis and therapy as well as intelligent biomedical image processing and analysis. It develops computational models, methods and tools for biomedical engineering related to computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy and bioinformatics. Large volumes of complex data are often a key feature of biomedical and engineering problems and computational intelligence helps to address such problems. Practical and validated solutions to hard biomedical and engineering problems can be developed by the applications of neural networks, support vector machines, reservoir computing, evolutionary optimization, biosignal processing, pattern recognition methods and other techniques to address complex problems of the real world.
This text is listed on the Course of Reading for SOA Fellowship study in the Group & Health specialty track. Healthcare Risk Adjustment and Predictive Modeling provides a comprehensive guide to healthcare actuaries and other professionals interested in healthcare data analytics, risk adjustment and predictive modeling. The book first introduces the topic with discussions of health risk, available data, clinical identification algorithms for diagnostic grouping and the use of grouper models. The second part of the book presents the concept of data mining and some of the common approaches used by modelers. The third and final section covers a number of predictive modeling and risk adjustment case-studies, with examples from Medicaid, Medicare, disability, depression diagnosis and provider reimbursement, as well as the use of predictive modeling and risk adjustment outside the U.S. For readers who wish to experiment with their own models, the book also provides access to a test dataset.
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
Using Predictive Analytics to Improve Healthcare Outcomes Winner of the American Journal of Nursing (AJN) Informatics Book of the Year Award 2021! Discover a comprehensive overview, from established leaders in the field, of how to use predictive analytics and other analytic methods for healthcare quality improvement. Using Predictive Analytics to Improve Healthcare Outcomes delivers a 16-step process to use predictive analytics to improve operations in the complex industry of healthcare. The book includes numerous case studies that make use of predictive analytics and other mathematical methodologies to save money and improve patient outcomes. The book is organized as a “how-to” manual, showing how to use existing theory and tools to achieve desired positive outcomes. You will learn how your organization can use predictive analytics to identify the most impactful operational interventions before changing operations. This includes: A thorough introduction to data, caring theory, Relationship-Based Care®, the Caring Behaviors Assurance System©, and healthcare operations, including how to build a measurement model and improve organizational outcomes. An exploration of analytics in action, including comprehensive case studies on patient falls, palliative care, infection reduction, reducing rates of readmission for heart failure, and more—all resulting in action plans allowing clinicians to make changes that have been proven in advance to result in positive outcomes. Discussions of how to refine quality improvement initiatives, including the use of “comfort” as a construct to illustrate the importance of solid theory and good measurement in adequate pain management. An examination of international organizations using analytics to improve operations within cultural context. Using Predictive Analytics to Improve Healthcare Outcomes is perfect for executives, researchers, and quality improvement staff at healthcare organizations, as well as educators teaching mathematics, data science, or quality improvement. Employ this valuable resource that walks you through the steps of managing and optimizing outcomes in your clinical care operations.
Our health care system is crippled by desperate efforts to prevent the inevitable. A third of the national Medicare budget—nearly 175 billion—is spent on the final year of life, and a third of that amount on the final month, often on expensive (and futile) treatments. Such efforts betray a fundamental flaw in how we think about healthcare: we squander resources on hopeless situations, instead of using them to actually improve health. In Predictive Health, distinguished doctors Kenneth Brigham and Michael M.E. Johns propose a solution: invest earlier—and use science and technology to make healthcare more available and affordable. Every child would begin life with a post-natal genetic screen, when potential risk—say for type II diabetes or heart disease—would be found. More data on biology, behavior, and environment would be captured throughout her life. Using this information, health-care workers and the people they care for could forge personal strategies for healthier living long before a small glitch blows up into major disease. This real health care wouldn’t just replace much of modern disease care—it would make it obsolete. The result, according to Brigham and Johns, will be a life defined by a long stay at top physical and mental form, rather than an early peak and long decline. Accomplishing this goal will require new tools, new clinics, fewer doctors and more mentors, smarter companies, and engaged patients. In short, it will require a revolution. Thanks to a decade-long collaboration between Brigham, Johns and others, it is already underway. An optimistic plan for reducing or eliminating many chronic diseases as well as reforming our faltering medical system, Predictive Health is a deeply knowledgeable, deeply humane proposal for how we can reallocate expenses and resources to prolong the best years of life, rather than extending the worst.
This book addresses the steps needed to monitor health assessment systems and the anticipation of their failures: choice and location of sensors, data acquisition and processing, health assessment and prediction of the duration of residual useful life. The digital revolution and mechatronics foreshadowed the advent of the 4.0 industry where equipment has the ability to communicate. The ubiquity of sensors (300,000 sensors in the new generations of aircraft) produces a flood of data requiring us to give meaning to information and leads to the need for efficient processing and a relevant interpretation. The process of traceability and capitalization of data is a key element in the context of the evolution of the maintenance towards predictive strategies.
This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings. Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book’s fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.
Healthcare delivery is progressing into a format wherein analysis of a combination of disease data and patient data using predictive analytics provides additional information for physicians and healthcare providers to make more accurate detection, diagnosis, and treatment decisions. This is a unique book offering a novel course on Predictive Analytics in Healthcare. In this book, the focus in chapters is placed on reviewing and analysing the current and future applications of analytics in several health care disciplines, which can, later on, contribute to technical implementation. This book aims to provide comprehensive information to guide physicians, medical students, hospital administrators, biomedical engineering students, data scientists, and the industry in the proper identification of analytics applications in healthcare. Key Features Presents an overview of Predictive Analytics for physicians, medical students, biomedical engineers, and data scientists in the health care domain. Identifies and presents the several existing applications of analytics in healthcare domains such as public health, women's health, telemedicine, and neurology, so that readers specializing in the particular field can have a comprehensive overview of all methodologies already in place. Enables readers to identify what new applications are needed to advance the use of analytics in their field. Presents case studies for the reader to understand how to us predictive analytics to bring their ideas to fruition.