Causation In Population Health Informatics And Data Science

Marketing text: This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference in epidemiology and population health research. Key concepts covered include how data are generated and interpreted, and how and why concepts in health informatics and the philosophy of science should be integrated in a systems-thinking approach. Furthermore, a formal epistemology for the health sciences and public health is suggested. Causation in Population Health Informatics and Data Science provides a detailed guide of the latest thinking on causal inference in population health informatics. It is therefore a critical resource for all informaticians and epidemiologists interested in the potential benefits of utilising a systems-based approach to causal inference in health informatics.

Marketing text: This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference in epidemiology and population health research. Key concepts covered include how data are generated and interpreted, and how and why concepts in health informatics and the philosophy of science should be integrated in a systems-thinking approach. Furthermore, a formal epistemology for the health sciences and public health is suggested. Causation in Population Health Informatics and Data Science provides a detailed guide of the latest thinking on causal inference in population health informatics. It is therefore a critical resource for all informaticians and epidemiologists interested in the potential benefits of utilising a systems-based approach to causal inference in health informatics.

This edited volume aims to better understand the multifaceted phenomenon we call health. Going beyond simple views of health as the absence of disease or as complete well-being, this book unites scientists and philosophers. The contributions clarify the links between health and adaptation, robustness, resilience, or dynamic homeostasis, and discuss how to achieve health and healthy aging through practices such as hormesis. The book is divided into three parts and a conclusion: the first part explains health from within specific disciplines, the second part explores health from the perspective of a bodily part, system, function, or even the environment in which organisms live, and the final part looks at more clinical or practical perspectives. It thereby gathers, across 30 chapters, diverse perspectives from the broad fields of evolutionary and systems biology, immunology, and biogerontology, more specific areas such as odontology, cardiology, neurology, and public health, as well as philosophical reflections on mental health, sexuality, authenticity and medical theories. The overarching aim is to inform, inspire and encourage intellectuals from various disciplines to assess whether explanations in these disparate fields and across biological levels can be sufficiently systematized and unified to clarify the complexity of health. It will be particularly useful for medical graduates, philosophy graduates and research professionals in the life sciences and general medicine, as well as for upper-level graduate philosophy of science students.

Population Health Informatics addresses the growing opportunity to utilize technology to put into practice evidence-based solutions to improve population health outcomes across diverse settings. The book focuses on how to operationalize population informatics solutions to address important public health challenges impacting individuals, families, communities, and the environment in which they live. The book uniquely uses a practical, step-by-step approach to implement evidence-based, data- driven population informatics solutions.

Discover best practices for real world data research with SAS code and examples Real world health care data is common and growing in use with sources such as observational studies, patient registries, electronic medical record databases, insurance healthcare claims databases, as well as data from pragmatic trials. This data serves as the basis for the growing use of real world evidence in medical decision-making. However, the data itself is not evidence. Analytical methods must be used to turn real world data into valid and meaningful evidence. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS brings together best practices for causal comparative effectiveness analyses based on real world data in a single location and provides SAS code and examples to make the analyses relatively easy and efficient. The book focuses on analytic methods adjusted for time-independent confounding, which are useful when comparing the effect of different potential interventions on some outcome of interest when there is no randomization. These methods include: propensity score matching, stratification methods, weighting methods, regression methods, and approaches that combine and average across these methods methods for comparing two interventions as well as comparisons between three or more interventions algorithms for personalized medicine sensitivity analyses for unmeasured confounding

What one can know depends on one’s evidence. Good scientific theories are supported by evidence. Our experiences provide us with evidence. Any sort of inquiry involves the seeking of evidence. It is irrational to believe contrary to your evidence. For these reasons and more, evidence is one of the most fundamental notions in the field of epistemology and is emerging as a crucial topic across academic disciplines. The Routledge Handbook of the Philosophy of Evidence is an outstanding reference source to the key topics, problems, and debates in this exciting subject and is the first major volume of its kind. Comprising forty chapters by an international team of contributors the handbook is divided into six clear parts: The Nature of Evidence Evidence and Probability The Social Epistemology of Evidence Sources of Evidence Evidence and Justification Evidence in the Disciplines The Routledge Handbook of the Philosophy of Evidence is essential reading for students and researchers in philosophy of science and epistemology, and will also be of interest to those in related disciplines across the humanities and social sciences, such as law, religion, and history.

What Is Scientific Knowledge? is a much-needed collection of introductory-level chapters on the epistemology of science. Renowned historians, philosophers, science educators, and cognitive scientists have authored 19 original contributions specifically for this volume. The chapters, accessible for students in both philosophy and the sciences, serve as helpful introductions to the primary debates surrounding scientific knowledge. First-year undergraduates can readily understand the variety of discussions in the volume, and yet advanced students and scholars will encounter chapters rich enough to engage their many interests. The variety and coverage in this volume make it the perfect choice for the primary text in courses on scientific knowledge. It can also be used as a supplemental book in classes in epistemology, philosophy of science, and other related areas. Key features: * an accessible and comprehensive introduction to the epistemology of science for a wide variety of students (both undergraduate- and graduate-level) and researchers * written by an international team of senior researchers and the most promising junior scholars * addresses several questions that students and lay people interested in science may already have, including questions about how scientific knowledge is gained, its nature, and the challenges it faces.

Real world health care data from observational studies, pragmatic trials, patient registries, and databases is common and growing in use. Real World Health Care Data Analysis: Causal Methods and Implementation in SAS® brings together best practices for causal-based comparative effectiveness analyses based on real world data in a single location. Example SAS code is provided to make the analyses relatively easy and efficient.The book also presents several emerging topics of interest, including algorithms for personalized medicine, methods that address the complexities of time varying confounding, extensions of propensity scoring to comparisons between more than two interventions, sensitivity analyses for unmeasured confounding, and implementation of model averaging.