Books in Life and medical sciences

41-50 of 62 results in All results

Predictive Modeling of Drug Sensitivity

• 1st Edition
• November 15, 2016
• Ranadip Pal
• English
• Paperback 978-0-12-805274-7

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• eBook 978-0-12-805431-4

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Predictive Modeling of Drug Sensitivity gives an overview of drug sensitivity modeling for personalized medicine that includes data characterizations, modeling techniques, applications, and research challenges. It covers the major mathematical techniques used for modeling drug sensitivity, and includes the requisite biological knowledge to guide a user to apply the mathematical tools in different biological scenarios. This book is an ideal reference for computer scientists, engineers, computational biologists, and mathematicians who want to understand and apply multiple approaches and methods to drug sensitivity modeling. The reader will learn a broad range of mathematical and computational techniques applied to the modeling of drug sensitivity, biological concepts, and measurement techniques crucial to drug sensitivity modeling, how to design a combination of drugs under different constraints, and the applications of drug sensitivity prediction methodologies.

Brain-Computer Interfaces: Lab Experiments to Real-World Applications

• 1st Edition
• Volume 228
• August 27, 2016
• Damien Coyle
• English
• Hardback 978-0-12-804216-8

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• eBook 978-0-12-809262-0

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Brain-Computer Interfaces: Lab Experiments to Real-World Applications, the latest volume in the Progress in Brain Research series, focuses on new trends and developments. This established international series examines major areas of basic and clinical research within the neurosciences, as well as popular and emerging subfields.

Biostatistics and Computer-based Analysis of Health Data using Stata

• 1st Edition
• August 24, 2016
• Christophe Lalanne + 1 more
• English
• Hardback 978-1-78548-142-0

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• eBook 978-0-08-101084-6

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This volume of the Biostatistics and Health Sciences Set focuses on statistics applied to clinical research. The use of Stata for data management and statistical modeling is illustrated using various examples. Many aspects of data processing and statistical analysis of cross-sectional and experimental medical data are covered, including regression models commonly found in medical statistics. This practical book is primarily intended for health researchers with basic knowledge of statistical methodology. Assuming basic concepts, the authors focus on the practice of biostatistical methods essential to clinical research, epidemiology and analysis of biomedical data (including comparison of two groups, analysis of categorical data, ANOVA, linear and logistic regression, and survival analysis). The use of examples from clinical trials and epideomological studies provide the basis for a series of practical exercises, which provide instruction and familiarize the reader with essential Stata packages and commands.

Machine Learning and Medical Imaging

• 1st Edition
• August 9, 2016
• Guorong Wu + 2 more
• English
• Hardback 978-0-12-804076-8

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• eBook 978-0-12-804114-7

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Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.

Biostatistics and Computer-based Analysis of Health Data using R

• 1st Edition
• July 11, 2016
• Christophe Lalanne + 1 more
• English
• Hardback 978-1-78548-088-1

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• eBook 978-0-08-101175-1

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Biostatistics and Computer-Based Analysis of Health Data Using the R Software addresses the concept that many of the actions performed by statistical software comes back to the handling, manipulation, or even transformation of digital data. It is therefore of primary importance to understand how statistical data is displayed and how it can be exploited by software such as R. In this book, the authors explore basic and variable commands, sample comparisons, analysis of variance, epidemiological studies, and censored data. With proposed applications and examples of commands following each chapter, this book allows readers to apply advanced statistical concepts to their own data and software.

Protecting Patient Information

• 1st Edition
• April 7, 2016
• Paul Cerrato
• English
• Paperback 978-0-12-804392-9

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• eBook 978-0-12-804411-7

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Protecting Patient Information: A Decision-Maker's Guide to Risk, Prevention, and Damage Control provides the concrete steps needed to tighten the information security of any healthcare IT system and reduce the risk of exposing patient health information (PHI) to the public. The book offers a systematic, 3-pronged approach for addressing the IT security deficits present in healthcare organizations of all sizes. Healthcare decision-makers are shown how to conduct an in-depth analysis of their organization’s information risk level. After this assessment is complete, the book offers specific measures for lowering the risk of a data breach, taking into account federal and state regulations governing the use of patient data. Finally, the book outlines the steps necessary when an organization experiences a data breach, even when it has taken all the right precautions.

Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology

• 1st Edition
• March 22, 2016
• Hamid R Arabnia + 1 more
• English
• Paperback 978-0-12-804203-8

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• eBook 978-0-12-804259-5

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Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology: Systems and Applications covers the latest trends in the field with special emphasis on their applications. The first part covers the major areas of computational biology, development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques for the study of biological and behavioral systems. The second part covers bioinformatics, an interdisciplinary field concerned with methods for storing, retrieving, organizing, and analyzing biological data. The book also explores the software tools used to generate useful biological knowledge. The third part, on systems biology, explores how to obtain, integrate, and analyze complex datasets from multiple experimental sources using interdisciplinary tools and techniques, with the final section focusing on big data and the collection of datasets so large and complex that it becomes difficult to process using conventional database management systems or traditional data processing applications.

Health Information Exchange: Navigating and Managing a Network of Health Information Systems

• 1st Edition
• February 9, 2016
• Brian Dixon
• English
• eBook 978-0-12-803150-6

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Health Information Exchange (HIE): Navigating and Managing a Network of Health Information Systems allows health professionals to appropriately access, and securely share, patients’ vital medical information electronically, thus improving the speed, quality, safety, and cost of patient care. The book presents foundational knowledge on HIE, covering the broad areas of technology, governance, and policy, providing a concise, yet in-depth, look at HIE that can be used as a teaching tool for universities, healthcare organizations with a training component, certification institutions, and as a tool for self-study for independent learners who want to know more about HIE when studying for certification exams. In addition, it not only provides coverage of the technical, policy, and organizational aspects of HIE, but also touches on HIE as a growing profession. In Part One, the book defines HIE, describing it as an emerging profession within HIT/Informatics. In Part Two, the book provides key information on the policy and governance of HIE, including stakeholder engagement, strategic planning, sustainability, etc. Part Three focuses on the technology behind HIE, defining and describing master person indexes, information infrastructure, interfacing, and messaging, etc. In Part Four, the authors discuss the value of HIE, and how to create and measure it. Finally, in Part Five, the book provides perspectives on the future of HIE, including emerging trends, unresolved challenges, etc.

Medical Image Recognition, Segmentation and Parsing

• 1st Edition
• December 2, 2015
• S. Kevin Zhou
• English
• Hardback 978-0-12-802581-9

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• eBook 978-0-12-802676-2

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This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects Methods and theories for medical image recognition, segmentation and parsing of multiple objects Efficient and effective machine learning solutions based on big datasets Selected applications of medical image parsing using proven algorithms

Applied Computing in Medicine and Health

• 1st Edition
• August 21, 2015
• Dhiya Al-Jumeily + 3 more
• English
• Paperback 978-0-12-803468-2

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• eBook 978-0-12-803498-9

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Applied Computing in Medicine and Health is a comprehensive presentation of on-going investigations into current applied computing challenges and advances, with a focus on a particular class of applications, primarily artificial intelligence methods and techniques in medicine and health. Applied computing is the use of practical computer science knowledge to enable use of the latest technology and techniques in a variety of different fields ranging from business to scientific research. One of the most important and relevant areas in applied computing is the use of artificial intelligence (AI) in health and medicine. Artificial intelligence in health and medicine (AIHM) is assuming the challenge of creating and distributing tools that can support medical doctors and specialists in new endeavors. The material included covers a wide variety of interdisciplinary perspectives concerning the theory and practice of applied computing in medicine, human biology, and health care. Particular attention is given to AI-based clinical decision-making, medical knowledge engineering, knowledge-based systems in medical education and research, intelligent medical information systems, intelligent databases, intelligent devices and instruments, medical AI tools, reasoning and metareasoning in medicine, and methodological, philosophical, ethical, and intelligent medical data analysis.