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Med-books.by - Библиотека медицинской литературы » Медицинская информатика

GIS and Spatial Analysis in Veterinary Science - Durr P.A., Gatrell A.C. - 2004 год



GIS and Spatial Analysis in Veterinary Science - Durr P.A., Gatrell A.C. - 2004 год

The use of geographical information systems (including remote sensing) and spatial analysis in public health is now widespread. Its importance and potential for the monitoring of animal diseases has never been greater with the recent outbreaks of BSE and Foot-and-Mouth Disease. GIS and Spatial Analysis in Veterinary Science is the first book to review how such practices can be applied to veterinary science. Topics covered include the application of GIS to epidemic disease response, to companion animal epidemiology and to the management of wildlife diseases. It also covers the parallels with human health and spatial statistics in the biomedical sciences.

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Machine Learning in Healthcare Informatics - Dua S., Acharya U.R., Dua P. - 2014 год



Machine Learning in Healthcare Informatics - Dua S., Acharya U.R., Dua P. - 2014 год

The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.

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Ionizing radiation detectors for medical imaging - Del Guerra A. - 2004 год - 524 с.



Ionizing radiation detectors for medical imaging - Del Guerra A. - 2004 год - 524 с.

The book contains ten technical chapters, half of which are devoted to radiology and the other half to nuclear medicine. The last chapter describes the detectors for radiotherapy and portal imaging. Each chapter addresses completely a specific application. The emphasis is always on detector fundamentals and detector properties. Where necessary, software and specific applications are described in depth.
This book is intended for graduate and undergraduate students in physics and engineering who want to study medical imaging. In addition, scientists who are working in a specific sub-field of medical imaging can acquire from the book an up-to-date description of the state of the art in related sub-fields, within the scope of ionizing radiation detectors. Other scientists, as well as physicians, can use the book as a reference for medical imaging.

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GIS in Public Health Practice - Craglia M., Maheswaran R. - 2004 год



GIS in Public Health Practice - Craglia M., Maheswaran R. - 2004 год

Interest among public health practitioners and academics is growing rapidly in the opportunities afforded by GIS, particularly in Europe and the US.Written by a varied group of authors, the primary focus of this book is on real practice in the use of GIS in public health. GIS in Public Health Practice covers disease mapping and spatial analysis, then moves on to GIS applications in communicable disease control and environmental health protection, and applications in health care planning and policy. It also explores issues surrounding the use of GIS in public health, particularly data availability, data protection and e-governance. This book is for practitioners, with its detail of the methods used in the different applications and its identification of opportunities and potential limitations. Researchers and graduate students studying public health and epidemiology should also find it useful.

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Computer Medical Databases: The First Six Decades (1950-2010) - Collen M.F. - 2012 год



Computer Medical Databases: The First Six Decades (1950-2010) - Collen M.F. - 2012 год

Chapter 1 offers an overview of the basic computer technology. Each succeeding chapter, describes the problems in medicine, followed by a review in chronological sequence of why and how computers were applied to try to meet these problems. Only the technical aspects of computer hardware, software, and communications are discussed as they are necessary to explain how the technology was applied. This approach generally led to defining the objectives for applications of medical informatics. At the end of each chapter, the author summarizes his personal views and interpretations of the chapter contents. Although the concurrent evolution of medical informatics in Canada, Europe, and Japan certainly influenced workers in the United States, the scope of this historical review is limited to the development of medical informatics within the United States. Furthermore, this review is limited to electronic digital computers; it excludes mechanical, analog, and hybrid computers.

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Guide to Health Informatics - Coiera E. - 2015 год



Guide to Health Informatics - Coiera E. - 2015 год

This essential text provides a readable yet sophisticated overview of the basic concepts of information technologies as they apply in healthcare. Spanning areas as diverse as the electronic medical record, searching, protocols, and communications as well as the Internet, Enrico Coiera has succeeded in making this vast and complex area accessible and understandable to the non-specialist, while providing everything that students of medical informatics need to know to accompany their course.
Fully revised, the third edition of Guide to Health Informatics remains essential reading for all health science undergraduates, clinical health professionals, and health service managers who need to appreciate and understand the role of informatics and its associated technologies for optimal practice and service delivery.

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Machine Learning in Medicine - Cookbook Two - Cleophas T.J., Zwinderman A.H. - 2014 год



Machine Learning in Medicine - Cookbook Two - Cleophas T.J., Zwinderman A.H. - 2014 год

The amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible.
Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the main incentive for the authors to complete a series of three textbooks entitled “Machine Learning in Medicine Part One, Two and Three, Springer Heidelberg Germany, 2012-2013", describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details.

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Machine Learning in Medicine - Cookbook Three - Cleophas T.J., Zwinderman A.H. - 2014 год



Machine Learning in Medicine - Cookbook Three - Cleophas T.J., Zwinderman A.H. - 2014 год

Unique features of the book involve the following.
This book is the third volume of a three volume series of cookbooks entitled "Machine Learning in Medicine - Cookbooks One, Two, and Three". No other self-assessment works for the medical and health care community covering the field of machine learning have been published to date.
Each chapter of the book can be studied without the need to consult other chapters, and can, for the readership's convenience, be downloaded from the internet. Self-assessment examples are available at extras.springer.com.
An adequate command of machine learning methodologies is a requirement for physicians and other health workers, particularly now, because the amount of medical computer data files currently doubles every 20 months, and, because, soon, it will be impossible for them to take proper data-based health decisions without the help of machine learning.
Given the importance of knowledge of machine learning in the medical and health care community, and the current lack of knowledge of it, the readership will consist of any physician and health worker.
The book was written in a simple language in order to enhance readability not only for the advanced but also for the novices.

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Machine Learning in Medicine - Cookbook - Cleophas T.J., Zwinderman A.H. - 2014 год



Machine Learning in Medicine - Cookbook - Cleophas T.J., Zwinderman A.H. - 2014 год

The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing.
Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning and the current 100 page cookbook should be helpful to that aim. It covers in a condensed form the subjects reviewed in the 750 page three volume textbook by the same authors, entitled “Machine Learning in Medicine I-III” (ed. by Springer, Heidelberg, Germany, 2013) and was written as a hand-hold presentation and must-read publication. It was written not only to investigators and students in the fields, but also to jaded clinicians new to the methods and lacking time to read the entire textbooks.
General purposes and scientific questions of the methods are only briefly mentioned, but full attention is given to the technical details. The two authors, a statistician and current president of the International Association of Biostatistics and a clinician and past-president of the American College of Angiology, provide plenty of step-by-step analyses from their own research and data files for self-assessment are available at extras.springer.com.
From their experience the authors demonstrate that machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.

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Machine Learning in Medicine - a Complete Overview - Cleophas T.J., Zwinderman A.H. - 2015 год



Machine Learning in Medicine - a Complete Overview - Cleophas T.J., Zwinderman A.H. - 2015 год

The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector. It was written as a training companion and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care. In eighty chapters eighty different machine learning methodologies are reviewed, in combination with data examples for self-assessment. Each chapter can be studied without the need to consult other chapters.
The amount of data stored in the world's databases doubles every 20 months, and clinicians, familiar with traditional statistical methods, are at a loss to analyze them. Traditional methods have, indeed, difficulty to identify outliers in large datasets, and to find patterns in big data and data with multiple exposure / outcome variables. In addition, analysis-rules for surveys and questionnaires, which are currently common methods of data collection, are, essentially, missing. Fortunately, the new discipline, machine learning, is able to cover all of these limitations.
So far medical professionals have been rather reluctant to use machine learning. Also, in the field of diagnosis making, few doctors may want a computer checking them, are interested in collaboration with a computer or with computer engineers. Adequate health and health care will, however, soon be impossible without proper data supervision from modern machine learning methodologies like cluster models, neural networks and other data mining methodologies.
Each chapter starts with purposes and scientific questions. Then, step-by-step analyses, using data examples, are given. Finally, a paragraph with conclusion, and references to the corresponding sites of three introductory textbooks, previously written by the same authors, is given.

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