Machine Learning in Medicine a Complete Overview

Author: Ton J. Cleophas
Publisher: Springer
ISBN: 3319151959
Format: PDF, ePub, Mobi
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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.

Regression Analysis in Medical Research

Author: Ton J. Cleophas
Publisher: Springer
ISBN: 3319719378
Format: PDF, ePub, Mobi
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This edition is a pretty complete textbook and tutorial for medical and health care students, as well as a recollection/update bench, and help desk for professionals. Novel approaches already applied in published clinical research will be addressed: matrix analyses, alpha spending, gate keeping, kriging, interval censored regressions, causality regressions, canonical regressions, quasi-likelihood regressions, novel non-parametric regressions. Each chapter can be studied as a stand-alone, and covers one field in the fast growing world of regression analyses. The authors, as professors in statistics and machine learning at European universities, are worried, that their students find regression-analyses harder than any other methodology in statistics. This is serious, because almost all of the novel methodologies in current data mining and data analysis include elements of regression-analysis. It is the main incentive for writing this 28 chapter edition, consistent of - 28 major fields of regression analysis, - their condensed maths, - their applications in medical and health research as published so far, - step by step analyses for self-assessment, - conclusion and reference sections. Traditional regression analysis is adequate for epidemiology, but lacks the precision required for clinical investigations. However, in the past two decades modern regression methods have proven to be much more precise. And so it is time, that a book described regression analyses for clinicians. The current edition is the first to do so. It is written for a non-mathematical readership. Self-assessment data-files are provided through Springer' s "Extras Online".

Machine Learning in Radiation Oncology

Author: Issam El Naqa
Publisher: Springer
ISBN: 3319183052
Format: PDF, ePub, Docs
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​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Machine Learning and Medical Imaging

Author: Guorong Wu
Publisher: Academic Press
ISBN: 0128041145
Format: PDF, Docs
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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. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques

Medical Applications of Artificial Intelligence

Author: Arvin Agah
Publisher: CRC Press
ISBN: 143988434X
Format: PDF, ePub, Mobi
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Enhanced, more reliable, and better understood than in the past, artificial intelligence (AI) systems can make providing healthcare more accurate, affordable, accessible, consistent, and efficient. However, AI technologies have not been as well integrated into medicine as predicted. In order to succeed, medical and computational scientists must develop hybrid systems that can effectively and efficiently integrate the experience of medical care professionals with capabilities of AI systems. After providing a general overview of artificial intelligence concepts, tools, and techniques, Medical Applications of Artificial Intelligence reviews the research, focusing on state-of-the-art projects in the field. The book captures the breadth and depth of the medical applications of artificial intelligence, exploring new developments and persistent challenges.

Understanding Clinical Data Analysis

Author: Ton J. Cleophas
Publisher: Springer
ISBN: 3319395866
Format: PDF, Mobi
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This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings. In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? The book will cover the WHY-SOs.

MATLAB Primer for Speech Language Pathology and Audiology

Author: Frank R. Boutsen
Publisher: Plural Publishing
ISBN: 1597569496
Format: PDF, ePub, Mobi
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MATLAB® Primer for Speech-Language Pathology and Audiology provides training and access to MATLAB®, the computational language developed by MathWorks®. While there are MATLAB® textbooks and manuals written for the field of engineering, there are no textbooks targeting allied heath disciplines, particularly speech-language pathology and audiology. Research and practice in this field can greatly benefit from quantification and automation in data management, a domain that is increasingly labor-intensive. The text anticipates and promotes increased reliance on quantification and automation in the fields of speech-language pathology and audiology. This book is intended for students, practitioners, and researchers in speech-language pathology and audiology who wish to increase their productivity by incorporating and automating common research procedures and data-analysis calculations, or who wish to develop new tools and methods for their own paradigms and data processing. It assumes no prior knowledge of programming, but requires the reader to have a grasp of basic computer skills, such as managing folders, moving files, and navigating file paths and folder structures. Content and style are chosen so as to lower the threshold for an audience who has limited training in computer science. Concepts are presented in a personalized writing style (almost a dialogue with the reader), along with a didactic format similar to programmed instruction, using applications and work assignments that are concrete and manageable. Key features include: * A comprehensive introduction for the user in an effort to limit background knowledge needed to understand the content * Several mathematical review appendices * Exercises for the student to apply skills learned in laboratory and clinical applications Disclaimer: Please note that ancillary content (such documents, audio, and video) may not be included as published in the original print version of this book.

Information and Software Technologies

Author: Robertas Damaševičius
Publisher: Springer
ISBN: 3319676423
Format: PDF, ePub, Docs
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This book constitutes the refereed proceedings of the 23nd International Conference on Information and Software Technologies, ICIST 2017, held in Druskininkai, Lithuania, in October 2017. The 51 papers presented were carefully reviewed and selected from 135 submissions. The papers are organized in topical sections on information systems; business intelligence for information and software systems; software engineering; information technology applications.

Healthcare Analytics Made Simple

Author: Vikas (Vik) Kumar
Publisher: Packt Publishing Ltd
ISBN: 1787283224
Format: PDF
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Add a touch of data analytics to your healthcare systems and get insightful outcomes Key Features Perform healthcare analytics with Python and SQL Build predictive models on real healthcare data with pandas and scikit-learn Use analytics to improve healthcare performance Book Description In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. What you will learn Gain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processes Use SQL and Python to analyze data Measure healthcare quality and provider performance Identify features and attributes to build successful healthcare models Build predictive models using real-world healthcare data Become an expert in predictive modeling with structured clinical data See what lies ahead for healthcare analytics Who this book is for Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare.