| Author | : Subhi J. Al'Aref,Gurpreet Singh,Lohendran Baskaran,Dimitri Metaxas |
| Publisher | : Academic Press |
| Release Date | : 2020-11-20 |
| ISBN 10 | : 0128202742 |
| Pages | : 454 pages |
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
| Author | : Subhi J. Al'Aref, M.D.,Gurpreet Singh,Lohendran Baskaran,Dimitri Metaxas |
| Publisher | : Academic Press |
| Release Date | : 2020-12-11 |
| ISBN 10 | : 0128202734 |
| Pages | : 454 pages |
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
| Author | : Lei Xing,Maryellen L. Giger,James K Min |
| Publisher | : Academic Press |
| Release Date | : 2020-09-16 |
| ISBN 10 | : 0128212586 |
| Pages | : 568 pages |
Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI. The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticism. By understanding how AI works, and developing an appreciation for both limitations and strengths, clinicians can harness its computational power to streamline workflow and improve patient care. It also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches. On the other hand, computers scientists and data analysts can provide solutions, but often lack easy access to clinical insight that may help focus their efforts. This book provides vital background knowledge to help bring these two groups together, and to engage in more streamlined dialogue to yield productive collaborative solutions in the field of medicine. Provides history and overview of artificial intelligence, as narrated by pioneers in the field Discusses broad and deep background and updates on recent advances in both medicine and artificial intelligence that enabled the application of artificial intelligence Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach
| Author | : Debmalya Barh |
| Publisher | : Academic Press |
| Release Date | : 2020-03-04 |
| ISBN 10 | : 0128173386 |
| Pages | : 544 pages |
Artificial Intelligence in Precision Health: From Concept to Applications provides a readily available resource to understand artificial intelligence and its real time applications in precision medicine in practice. Written by experts from different countries and with diverse background, the content encompasses accessible knowledge easily understandable for non-specialists in computer sciences. The book discusses topics such as cognitive computing and emotional intelligence, big data analysis, clinical decision support systems, deep learning, personal omics, digital health, predictive models, prediction of epidemics, drug discovery, precision nutrition and fitness. Additionally, there is a section dedicated to discuss and analyze AI products related to precision healthcare already available. This book is a valuable source for clinicians, healthcare workers, and researchers from diverse areas of biomedical field who may or may not have computational background and want to learn more about the innovative field of artificial intelligence for precision health. Provides computational approaches used in artificial intelligence easily understandable for non-computer specialists Gives know-how and real successful cases of artificial intelligence approaches in predictive models, modeling disease physiology, and public health surveillance Discusses the applicability of AI on multiple areas, such as drug discovery, clinical trials, radiology, surgery, patient care and clinical decision support
| Author | : U. Joseph Schoepf |
| Publisher | : Humana Press |
| Release Date | : 2018-05-28 |
| ISBN 10 | : 1603272372 |
| Pages | : 931 pages |
Leading clinicians and researchers from around the world review the full scope of current developments, research, and scientific controversy regarding the principles and applications of cardiac CT. Richly illustrated with numerous black-and-white and color images, the book discusses the interpretation of CT images of the heart in a variety of clinical, physiological, and pathological applications. The authors emphasize current state-of-the-art uses of CT, but also examine developments at the horizon. They also review the technical basis of CT image acquisition, as well as tools for image visualization and analysis.
| Author | : Erik R. Ranschaert,Sergey Morozov,Paul R. Algra |
| Publisher | : Springer |
| Release Date | : 2019-01-29 |
| ISBN 10 | : 3319948784 |
| Pages | : 373 pages |
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
| Author | : Utku Kose,Omer Deperlioglu,Jafar Alzubi,Bogdan Patrut |
| Publisher | : Springer Nature |
| Release Date | : 2020-06-17 |
| ISBN 10 | : 981156325X |
| Pages | : 171 pages |
This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.
| Author | : Peter Szolovits |
| Publisher | : Routledge |
| Release Date | : 2019-03-13 |
| ISBN 10 | : 0429728468 |
| Pages | : 226 pages |
This book introduces the field of artificial intelligence in medicine, a new research area that combines sophisticated representational and computing techniques with the insights of expert physicians to produce tools for improving health care. An introductory chapter describes the historical and technical foundations of the work and provides an overview of the current state of the art and research directions. The authors then describe four prototype computer programs that tackle difficult clinical problems in a manner similar to that of an expert physician. The programs presented are internist, a diagnostic aid that combines a large database of disease/manifestation associations with techniques for problem formulation; expert and the Glaucoma Program which use physiological models for the diagnosis and treatment of eye disease; mycin, a rule-based program for diagnosis and therapy selection for infectious diseases; and the Digitalis Therapy Advisor, which aids the physician in prescribing the right dose of the drug digitalis and also explains its actions.
| Author | : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen |
| Publisher | : Academic Press |
| Release Date | : 2017-01-18 |
| ISBN 10 | : 0128104090 |
| Pages | : 458 pages |
Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache
| Author | : James Stirrup |
| Publisher | : Oxford University Press, USA |
| Release Date | : 2020-01-02 |
| ISBN 10 | : 0198809271 |
| Pages | : 576 pages |
Recent years have seen a marked increase in cardiovascular computed tomography (CT) imaging, with the technique now integrated into many imaging guidelines, such as those published by ESC and NICE. Rapid clinical and technological progress has created a need for guidance on the practical aspects of CT image acquisition, analysis and interpretation. The Oxford Specialist Handbook of Cardiovascular CT, now revised for the second edition by practising international experts with many years of hands-on experience, is designed to fulfil this need. The Handbook is a practical guide on performing, analysing and interpreting cardiovascular CT scans, covering all aspects from patient safety to optimal image acquisition to differential diagnoses of tricky images. It takes an international approach to both accreditation and certification, highlighting British, European, and American examinations and courses. The format is designed to be accessible and is laid out in easy to navigate sections. It is meant as a quick-reference guide, to live near the CT scanner, workstation, or on the office shelf. The Handbook is aimed at all cardiovascular CT users (Cardiologists, Radiologists and Radiographers), particularly those new to cardiovascular CT, although even the advanced user should find useful tips and tricks within.
| Author | : Anthony C. Chang |
| Publisher | : Academic Press |
| Release Date | : 2020-06-27 |
| ISBN 10 | : 0128233389 |
| Pages | : 534 pages |
Intelligence-Based Medicine: Data Science, Artificial Intelligence, and Human Cognition in Clinical Medicine and Healthcare provides a multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies with real life applications in healthcare and medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and the data science domains that is symmetric and balanced. The content consists of basic concepts of artificial intelligence and its real-life applications in a myriad of medical areas as well as medical and surgical subspecialties. It brings section summaries to emphasize key concepts delineated in each section; mini-topics authored by world-renowned experts in the respective key areas for their personal perspective; and a compendium of practical resources, such as glossary, references, best articles, and top companies. The goal of the book is to inspire clinicians to embrace the artificial intelligence methodologies as well as to educate data scientists about the medical ecosystem, in order to create a transformational paradigm for healthcare and medicine by using this emerging new technology. Covers a wide range of relevant topics from cloud computing, intelligent agents, to deep reinforcement learning and internet of everything Presents the concepts of artificial intelligence and its applications in an easy-to-understand format accessible to clinicians and data scientists Discusses how artificial intelligence can be utilized in a myriad of subspecialties and imagined of the future Delineates the necessary elements for successful implementation of artificial intelligence in medicine and healthcare
| Author | : Eric Topol |
| Publisher | : Basic Books |
| Release Date | : 2019-03-12 |
| ISBN 10 | : 1541644646 |
| Pages | : 400 pages |
One of America's top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard. Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved.
| Author | : Florian Knoll,Andreas Maier,Daniel Rueckert |
| Publisher | : Springer |
| Release Date | : 2018-09-11 |
| ISBN 10 | : 3030001296 |
| Pages | : 158 pages |
This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography, and deep learning for general image reconstruction.
| Author | : Guorong Wu,Dinggang Shen,Mert Sabuncu |
| Publisher | : Academic Press |
| Release Date | : 2016-08-11 |
| ISBN 10 | : 0128041145 |
| Pages | : 512 pages |
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
| Author | : Zhi-Hua Zhou |
| Publisher | : CRC Press |
| Release Date | : 2012-06-06 |
| ISBN 10 | : 1439830037 |
| Pages | : 236 pages |
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.
| Author | : Jason Brownlee |
| Publisher | : Machine Learning Mastery |
| Release Date | : 2016-03-04 |
| ISBN 10 | : |
| Pages | : 163 pages |
You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.
| Author | : Vinit Kumar Gunjan |
| Publisher | : Springer Nature |
| Release Date | : |
| ISBN 10 | : 9811572348 |
| Pages | : 329 pages |
| Author | : Robert W. Biederman, M.D.,Robert W. W. Biederman,Mark Doyle,June Yamrozik |
| Publisher | : Lippincott Williams & Wilkins |
| Release Date | : 2008 |
| ISBN 10 | : 9780781772167 |
| Pages | : 378 pages |
This text/DVD package is ideally suited for training courses for cardiologists and radiologists seeking certification to perform and interpret cardiovascular MRI (CMR) examinations. The authors present 37 lectures that systematically explain all key aspects of CMR. Coverage begins with an overview of principles, equipment, and imaging methods and proceeds to imaging protocols and clinical applications. An Advanced Training section includes details of imaging techniques, vascular imaging techniques, specialized cardiac imaging, and artifacts. The text and the PowerPoint lectures on the DVD complement each other in a unique way. The book mirrors the content of the lectures and provides full explanations of concepts that are well illustrated in the slides. DVD for Windows (PC only; Mac is available upon request).
| Author | : Georgios Paliouras,Vangelis Karkaletsis,Constantine D. Spyropoulos |
| Publisher | : Springer |
| Release Date | : 2003-06-29 |
| ISBN 10 | : 3540446737 |
| Pages | : 324 pages |
In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.
| Author | : Gordon Guyatt,Drummond Rennie,Maureen O. Meade,Deborah J. Cook |
| Publisher | : McGraw Hill Professional |
| Release Date | : 2008-03-01 |
| ISBN 10 | : 0071590358 |
| Pages | : 860 pages |
The #1 guide to the principles and clinical applications of evidence-based medicine has just gotten better! A Doody's Core Title ESSENTIAL PURCHASE for 2011! No other resource helps you to put key evidence-based medicine protocols into daily clinical practice better than Users' Guides to the Medical Literature. An instant classic in its first edition, this detailed, yet highly readable reference demystifies the statistical, analytical, and clinical principles of evidence-based medicine, giving you a hands-on, practical resource that no other text can match. Here, you'll learn how to distinguish solid medical evidence from poor medical evidence, devise the best search strategies for each clinical question, critically appraise the medical literature, and optimally tailor evidence-based medicine for each patient. The new second edition of this landmark resource is now completely revised and refreshed throughout, with expanded coverage of both basic and advanced issues in using evidence-based medicine in clinical practice. FEATURES: Completely revised and updated to reflect the enormous expansion in medical research and evidence-based resources since the first edition Innovative organization guides you from the fundamentals of using the medical literature to the more advanced strategies and skills for use in every day patient care situations Abundant and current real-world examples drawn from the medical literature are woven throughout, and include important related principles and pitfalls in using medical literature in patient care decisions Practical focus on the key issues in evidence-based practice: What are the results? Are the results valid? How to I apply to results to the care of my patients? More than 60 internationally recognized editors and contributors from the U.S., Canada, South America, Europe, and Asia -- the best of the best in the discipline NEW coverage on how to: --Avoid being misled by biased presentations of research findings --Interpret the significance of clinical trials that are discontinued early --Influence clinician behavior to improve patient care --Apply key strategies for teaching evidence-based medicine Also look for JAMAevidence.com, a new interactive database for the best practice of evidence based medicine.