Cases on Health Outcomes and Clinical Data Mining PDF Download

Author: Patricia B. Cerrito
Publisher: IGI Global
ISBN: 9781615207237
Category : Computers
Languages : en
Pages : 0

View

Book Description
"Because so much data is now becoming readily available to investigate health outcomes, it is important to examine just how statistical models are used to do this. This book studies health outcomes research using data mining techniques"--Provided by publisher.

Author: Cerrito, Patricia
Publisher: IGI Global
ISBN: 1615207244
Category : Computers
Languages : en
Pages : 463

View

Book Description
"Because so much data is now becoming readily available to investigate health outcomes, it is important to examine just how statistical models are used to do this. This book studies health outcomes research using data mining techniques"--Provided by publisher.

Author: Irwin Epstein
Publisher: Routledge
ISBN: 0789017083
Category : Business & Economics
Languages : en
Pages : 209

View

Book Description
This groundbreaking book will show you how to use existing patient records to do original research so you can custom-tailor programs to fit the specific needs of your department. Clinical Data-Mining in Practice-Based Research draws from the experiences of members of the Mount Sinai Department of Social Work staff. By analyzing case data, these professionals were able to identify biopsychosocial factors that affected social-health outcomes, and therefore to assess, maintain, and improve the quality of social work services. The detailed discussions in this book will help you apply these techniques toward improving your own service.

Author: Agency for Healthcare Research and Quality/AHRQ
Publisher: Government Printing Office
ISBN: 1587634333
Category : Medical
Languages : en
Pages : 385

View

Book Description
This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.

Author: Ronny S. Mans
Publisher: Springer
ISBN: 3319160710
Category : Computers
Languages : en
Pages : 99

View

Book Description
What are the possibilities for process mining in hospitals? In this book the authors provide an answer to this question by presenting a healthcare reference model that outlines all the different classes of data that are potentially available for process mining in healthcare and the relationships between them. Subsequently, based on this reference model, they explain the application opportunities for process mining in this domain and discuss the various kinds of analyses that can be performed. They focus on organizational healthcare processes rather than medical treatment processes. The combination of event data and process mining techniques allows them to analyze the operational processes within a hospital based on facts, thus providing a solid basis for managing and improving processes within hospitals. To this end, they also explicitly elaborate on data quality issues that are relevant for the data aspects of the healthcare reference model. This book mainly targets advanced professionals involved in areas related to business process management, business intelligence, data mining, and business process redesign for healthcare systems as well as graduate students specializing in healthcare information systems and process analysis.

Author: Arjun Panesar
Publisher: Apress
ISBN: 1484237994
Category : Computers
Languages : en
Pages : 390

View

Book Description
Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.

Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 030911988X
Category : Medical
Languages : en
Pages : 442

View

Book Description
Recent scientific and technological advances have accelerated our understanding of the causes of disease development and progression, and resulted in innovative treatments and therapies. Ongoing work to elucidate the effects of individual genetic variation on patient outcomes suggests the rapid pace of discovery in the biomedical sciences will only accelerate. However, these advances belie an important and increasing shortfall between the expansion in therapy and treatment options and knowledge about how these interventions might be applied appropriately to individual patients. The impressive gains made in Americans' health over the past decades provide only a preview of what might be possible when data on treatment effects and patient outcomes are systematically captured and used to evaluate their effectiveness. Needed for progress are advances as dramatic as those experienced in biomedicine in our approach to assessing clinical effectiveness. In the emerging era of tailored treatments and rapidly evolving practice, ensuring the translation of scientific discovery into improved health outcomes requires a new approach to clinical evaluation. A paradigm that supports a continual learning process about what works best for individual patients will not only take advantage of the rigor of trials, but also incorporate other methods that might bring insights relevant to clinical care and endeavor to match the right method to the question at hand. The Institute of Medicine Roundtable on Value & Science-Driven Health Care's vision for a learning healthcare system, in which evidence is applied and generated as a natural course of care, is premised on the development of a research capacity that is structured to provide timely and accurate evidence relevant to the clinical decisions faced by patients and providers. As part of the Roundtable's Learning Healthcare System series of workshops, clinical researchers, academics, and policy makers gathered for the workshop Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches. Participants explored cutting-edge research designs and methods and discussed strategies for development of a research paradigm to better accommodate the diverse array of emerging data resources, study designs, tools, and techniques. Presentations and discussions are summarized in this volume.

Author: Roger Mazze
Publisher: John Wiley & Sons
ISBN: 0470061715
Category : Medical
Languages : en
Pages : 451

View

Book Description
Using evidence-based medicine, this title addresses theprominent issues of primary care diabetes management. It providespractical solutions to the detection and treatment of diabetes, itscomplications and such new areas as metabolic syndrome,pre-diabetes and diabetes in children. The text reviews thefundamental basis of diabetes management and then addressestreatment of each type of diabetes and the major micro- andmacrovascular complications. This Revised Second Edition uniquely focuses on advancedtechnologies and advanced therapeutics. Key changes include:Integration of incretin hormones in the basic pathophysiologyof type 2 diabetes; Incretin mimetics andpotentiators; Revised clinical decision paths with newmedications and advanced insulin algorithms; New section oncontinuous glucose monitoring. Staged Diabetes Management: A Systematic Approach, SecondEdition, Revised presents a clear set of clinicalalgorithms consistent with the EASD/ADA recommended algorithms. Itprovides a means of applying the principles using a provenmethodology and one that has been applied internationally. Based on the highly successful diabetes programmes for primarycare developed by the world-renowned International Diabetes Centerin Minneapolis, USA Features Decision Paths and Practice Guidelines to facilitateclinical decision making Clearly written and illustrated: each chapter may be read alonebut complements the others to give a broad view of diabetescare This title is an invaluable guide for healthcare professionals,particularly primary care physicians, diabetes specialist nurses,and for all those with an interest in diabetes. It is alsouseful for all Diabetes educators and medical students.

Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 0309133939
Category : Medical
Languages : en
Pages : 374

View

Book Description
As our nation enters a new era of medical science that offers the real prospect of personalized health care, we will be confronted by an increasingly complex array of health care options and decisions. The Learning Healthcare System considers how health care is structured to develop and to apply evidence-from health profession training and infrastructure development to advances in research methodology, patient engagement, payment schemes, and measurement-and highlights opportunities for the creation of a sustainable learning health care system that gets the right care to people when they need it and then captures the results for improvement. This book will be of primary interest to hospital and insurance industry administrators, health care providers, those who train and educate health workers, researchers, and policymakers. The Learning Healthcare System is the first in a series that will focus on issues important to improving the development and application of evidence in health care decision making. The Roundtable on Evidence-Based Medicine serves as a neutral venue for cooperative work among key stakeholders on several dimensions: to help transform the availability and use of the best evidence for the collaborative health care choices of each patient and provider; to drive the process of discovery as a natural outgrowth of patient care; and, ultimately, to ensure innovation, quality, safety, and value in health care.

Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385

View

Book Description
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