A Systematic Survey of Computer-Aided Diagnosis in Medicine: Past and Present Developments PDF Download
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Author: Juri Yanase
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 51
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Book Description
Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diagnostic decision-making process of medical experts, they can be considered as expert systems in medicine.
Author: Juri Yanase
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 51
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Book Description
Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diagnostic decision-making process of medical experts, they can be considered as expert systems in medicine.
Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385
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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
Author: Joy Higgs
Publisher: Elsevier Health Sciences
ISBN: 0750688858
Category : Medical
Languages : en
Pages : 518
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Book Description
Clinical reasoning is the foundation of professional clinical practice. Totally revised and updated, this book continues to provide the essential text on the theoretical basis of clinical reasoning in the health professions and examines strategies for assisting learners, scholars and clinicians develop their reasoning expertise. key chapters revised and updated nature of clinical reasoning sections have been expanded increase in emphasis on collaborative reasoning core model of clinical reasoning has been revised and updated
Author: Mihaela Pop
Publisher: Springer
ISBN: 3319594486
Category : Computers
Languages : en
Pages : 524
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Book Description
This book constitutes the refereed proceedings of the 9th International Conference on Functional Imaging and Modeling of the Heart, held in Toronto, ON, Canada, in June 2017. The 48 revised full papers were carefully reviewed and selected from 63 submissions. The focus of the papers is on following topics: novel imaging and analysis methods for myocardial tissue characterization and remodeling; advanced cardiac image analysis tools for diagnostic and interventions; electrophysiology: mapping and biophysical modeling; biomechanics and flow: modeling and tissue property measurements.
Author: Arjun Panesar
Publisher: Apress
ISBN: 1484237994
Category : Computers
Languages : en
Pages : 390
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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: Rani, Geeta
Publisher: IGI Global
ISBN: 1799827437
Category : Medical
Languages : en
Pages : 586
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Book Description
By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.
Author: Pieter Kubben
Publisher: Springer
ISBN: 3319997130
Category : Medical
Languages : en
Pages : 219
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Book Description
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Author: Leo Anthony Celi
Publisher: Springer Nature
ISBN: 3030479943
Category : Medical
Languages : en
Pages : 471
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Book Description
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
Author: Vincent Francois-Lavet
Publisher: Foundations and Trends (R) in Machine Learning
ISBN: 9781680835380
Category :
Languages : en
Pages : 156
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Book Description
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. Written by recognized experts, this book is an important introduction to Deep Reinforcement Learning for practitioners, researchers and students alike.
Author: Filippo Crea
Publisher: Springer Science & Business Media
ISBN: 8847053676
Category : Medical
Languages : en
Pages : 263
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Book Description
In the past two decades a number of studies have shown that abnormalities in the function and structure of coronary microcirculation can be detected in several cardiovascular diseases. On the basis of the clinical setting in which it occurs, coronary microvascular dysfunction (CMD) can be classified into four types: CMD in the absence of any other cardiac disease; CMD in myocardial diseases; CMD in obstructive epicardial coronary artery disease; and iatrogenic CMD. In some instances CMD represents an epiphenomenon, whereas in others it represents an important marker of risk or may contribute to the pathogenesis of myocardial ischemia, thus becoming a possible therapeutic target. This book provides an update on coronary physiology and a systematic assessment of microvascular abnormalities in cardiovascular diseases, in the hope that it will assist clinicians in prevention, detection and management of CMD in their everyday activity.
