Systems Science and Population Health
Systems Science and Population Health

Author: Abdulrahman M. El-Sayed

Publisher: Oxford University Press

Published: 2017-02-01

Total Pages: 241

ISBN-13: 0190492406

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Population health is complex and multileveled, encompassing dynamic interactions between cells, societies, and everything in between. Our typical approach to studying population health, however, remains oriented around a reductionist approach to conceptualizing, empirically analyzing, and intervening to improve population health. The trouble is that interventions founded on simplifying a complex world often do not work, sometimes yielding failure or, even worse, harm. The difficult truth is that "silver bullet" health science often fails, and understanding these failures can help us improve our approach to health science, and, ultimately, population health. SYSTEMS SCIENCE AND POPULATION HEALTH employs principles from across a range of sciences to refine the way we understand population health. By augmenting traditional analytic approaches with new tools like machine learning, microsimulation, and social network analysis, population health can be studied as a dynamic and complex system. This allows us to understand population health as a complex whole, offering new insights and perspectives that stand to improve the health of the public. This text offers the first educational and practical guide to this forward-thinking approach. Comprising 17 chapters from the vanguard of population health, epidemiology, computer science, and medicine, this book offers a three-part introduction to the subject: · An intellectual and conceptual history of systems science as it intersects with population health · Concise, introductory overviews of important and emerging methodological tools in systems science, including systems dynamics, agent-based modeling, microsimulation, social network analysis, and machine-learning-all with relevant examples drawn from population health literature · An exploration of future implications for systems science and its applications to our understanding of population health issues For researchers, students, and practitioners, SYSTEMS SCIENCE AND POPULATION HEALTH redefines many of the foundational elements of how we understand population health. It should not be missed.

Intelligence-Based Cardiology and Cardiac Surgery
Intelligence-Based Cardiology and Cardiac Surgery

Author: Anthony C Chang

Publisher: Elsevier

Published: 2023-09-06

Total Pages: 542

ISBN-13: 032390629X

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Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine provides a comprehensive survey of artificial intelligence concepts and methodologies with real-life applications in cardiovascular medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and data science domains. The book's content consists of basic concepts of artificial intelligence and human cognition applications in cardiology and cardiac surgery. This portfolio ranges from big data, machine and deep learning, cognitive computing and natural language processing in cardiac disease states such as heart failure, hypertension and pediatric heart care. The book narrows the knowledge and expertise chasm between the data scientists, cardiologists and cardiac surgeons, inspiring clinicians to embrace artificial intelligence methodologies, educate data scientists about the medical ecosystem, and create a transformational paradigm for healthcare and medicine. - Covers a wide range of relevant topics from real-world data, large language models, and supervised machine learning to deep reinforcement and federated learning - Presents artificial intelligence concepts and their applications in many areas in an easy-to-understand format accessible to clinicians and data scientists - Discusses using artificial intelligence and related technologies with cardiology and cardiac surgery in a myriad of venues and situations - Delineates the necessary elements for successfully implementing artificial intelligence in cardiovascular medicine for improved patient outcomes - Presents the regulatory, ethical, legal, and financial issues embedded in artificial intelligence applications in cardiology

Population Health Science
Population Health Science

Author: Katherine M. Keyes

Publisher: Oxford University Press

Published: 2016-07-07

Total Pages: 225

ISBN-13: 0190459387

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POPULATION HEALTH SCIENCE formalizes an emerging discipline at the crossroads of social and medical sciences, demography, and economics--an emerging approach to population studies that represents a seismic shift in how traditional health sciences measure and observe health events. Bringing together theories and methods from diverse fields, this text provides grounding in the factors that shape population health. The overall approach is one of consequentialist science: designing creative studies that identify causal factors in health with multidisciplinary rigor. Distilled into nine foundational principles, this book guides readers through population science studies that strategically incorporate: · macrosocial factors · multilevel, lifecourse, and systems theories · prevention science fundamentals · return on investment · equity and efficiency Harnessing the power of scientific inquiry and codifying the knowledge base for a burgeoning field, POPULATION HEALTH SCIENCE arms readers with tools to shift the curve of population health.

A Nationwide Framework for Surveillance of Cardiovascular and Chronic Lung Diseases
A Nationwide Framework for Surveillance of Cardiovascular and Chronic Lung Diseases

Author: Institute of Medicine

Publisher: National Academies Press

Published: 2011-08-26

Total Pages: 200

ISBN-13: 0309212197

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Chronic diseases are common and costly, yet they are also among the most preventable health problems. Comprehensive and accurate disease surveillance systems are needed to implement successful efforts which will reduce the burden of chronic diseases on the U.S. population. A number of sources of surveillance data-including population surveys, cohort studies, disease registries, administrative health data, and vital statistics-contribute critical information about chronic disease. But no central surveillance system provides the information needed to analyze how chronic disease impacts the U.S. population, to identify public health priorities, or to track the progress of preventive efforts. A Nationwide Framework for Surveillance of Cardiovascular and Chronic Lung Diseases outlines a conceptual framework for building a national chronic disease surveillance system focused primarily on cardiovascular and chronic lung diseases. This system should be capable of providing data on disparities in incidence and prevalence of the diseases by race, ethnicity, socioeconomic status, and geographic region, along with data on disease risk factors, clinical care delivery, and functional health outcomes. This coordinated surveillance system is needed to integrate and expand existing information across the multiple levels of decision making in order to generate actionable, timely knowledge for a range of stakeholders at the local, state or regional, and national levels. The recommendations presented in A Nationwide Framework for Surveillance of Cardiovascular and Chronic Lung Diseases focus on data collection, resource allocation, monitoring activities, and implementation. The report also recommends that systems evolve along with new knowledge about emerging risk factors, advancing technologies, and new understanding of the basis for disease. This report will inform decision-making among federal health agencies, especially the Department of Health and Human Services; public health and clinical practitioners; non-governmental organizations; and policy makers, among others.

Machine Learning in Cardiovascular Medicine
Machine Learning in Cardiovascular Medicine

Author: Subhi J. Al'Aref, M.D.

Publisher: Academic Press

Published: 2020-12-11

Total Pages: 454

ISBN-13: 0128202734

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Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), specifically machine learning (ML), in healthcare and within cardiovascular medicine. The book focuses on emphasizing ML for biomedical applications and provides a comprehensive summary of the past and present of AI, basics of ML, and clinical applications of ML within cardiovascular medicine for predictive analytics and precision medicine. It helps readers understand how ML works along with its limitations and strengths, such that they can could harness its computational power to streamline workflow and improve patient care. It is suitable for both clinicians and engineers; providing a template for clinicians to understand areas of application of machine learning within cardiovascular research; and assist computer scientists and engineers in evaluating current and future impact of machine learning on cardiovascular medicine. Provides an overview of machine learning, both for a clinical and engineering audience Summarize recent advances in both cardiovascular medicine and artificial intelligence Discusses the advantages of using machine learning for outcomes research and image processing Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach

Causation in Population Health Informatics and Data Science
Causation in Population Health Informatics and Data Science

Author: Olaf Dammann

Publisher: Springer

Published: 2018-10-29

Total Pages: 139

ISBN-13: 3319963074

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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.