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Machine Learning In Cardiovascular Medicine

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Machine Learning in Cardiovascular Medicine

Machine Learning in Cardiovascular Medicine Book
Author : Subhi J. Al'Aref,Gurpreet Singh,Lohendran Baskaran,Dimitri Metaxas
Publisher : Academic Press
Release : 2020-11-20
ISBN : 0128202742
Language : En, Es, Fr & De

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Book Description :

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

Deep Learning for Medical Decision Support Systems

Deep Learning for Medical Decision Support Systems Book
Author : Utku Kose,Omer Deperlioglu,Jafar Alzubi,Bogdan Patrut
Publisher : Springer Nature
Release : 2020-06-17
ISBN : 981156325X
Language : En, Es, Fr & De

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Book Description :

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.

Intelligence Based Medicine

Intelligence Based Medicine Book
Author : Anthony C. Chang
Publisher : Academic Press
Release : 2020-06-27
ISBN : 0128233389
Language : En, Es, Fr & De

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Book Description :

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

Machine Learning for Medical Image Reconstruction

Machine Learning for Medical Image Reconstruction Book
Author : Florian Knoll,Andreas Maier,Daniel Rueckert
Publisher : Springer
Release : 2018-09-11
ISBN : 3030001296
Language : En, Es, Fr & De

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Book Description :

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.

Machine Learning in Medicine

Machine Learning in Medicine Book
Author : Ton J. Cleophas,Aeilko H. Zwinderman
Publisher : Springer Science & Business Media
Release : 2013-02-12
ISBN : 9400758243
Language : En, Es, Fr & De

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Book Description :

Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine Book
Author : Silvana Quaglini,Pedro Barahona,Steen Andreassen
Publisher : Springer Science & Business Media
Release : 2001-06-22
ISBN : 3540422943
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001, held in Cascais, Portugal in July 2001. The 31 revised full papers presented together with 30 posters and two invited papers were carefully reviewed and selected from 79 submissions. Among the topics addressed in their context on medical information processing are knowledge management, machine learning, data mining, decision support systems, temporal reasoning, case-based reasoning, planning and scheduling, natural language processing, computer vision, image and signal interpretation, intelligent agents, telemedicine, careflow systems, and cognitive modeling.

Machine Learning in Medicine Cookbook Two

Machine Learning in Medicine   Cookbook Two Book
Author : Ton J. Cleophas,Aeilko H. Zwinderman
Publisher : Springer
Release : 2014-05-27
ISBN : 331907413X
Language : En, Es, Fr & De

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Book Description :

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. For this reason we produced a 100 page cookbook, entitled "Machine Learning in Medicine - Cookbook One", with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled "Machine Learning in Medicine - Cookbook Two" is complementary to the first and also intended for providing a more balanced view of the field and thus, 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. Similarly to Machine Learning in Medicine - Cookbook One, the current work will describe stepwise analyses of over twenty machine learning methods, that are, likewise, based on the three major machine learning methodologies: Cluster methodologies (Chaps. 1-3) Linear methodologies (Chaps. 4-11) Rules methodologies (Chaps. 12-20) In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8. We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing "general purposes", "main scientific questions" and "conclusions" are given in place. Finally, we will demonstrate that modern 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.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine Book
Author : Werner Horn,Yuval Shahar,Greger Lindberg,Steen Andreassen,Jeremy Wyatt
Publisher : Springer
Release : 2003-05-21
ISBN : 3540487204
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, AIMDM'99, held in Aalborg, Denmark, in June 1999. The 27 full papers and 19 short papers presented in the book together with four invited papers were selected from 90 submissions. The papers are organized in topical sections on guidelines and protocols; decision support systems, knowledge-based systems, and cooperative systems; model-based systems; neural nets and causal probabilistic networks; knowledge representation; temporal reasoning; machine learning; natural language processing; and image processing and computer aided design.

Artificial Intelligence for Computational Modeling of the Heart

Artificial Intelligence for Computational Modeling of the Heart Book
Author : Tommaso Mansi,Tiziano Passerini,Dorin Comaniciu
Publisher : Academic Press
Release : 2019-12
ISBN : 012817594X
Language : En, Es, Fr & De

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Book Description :

Artificial Intelligence for Computational Modeling of the Heart presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient's heart by combining computational modeling of heart physiology and artificial intelligence. The book first introduces the major aspects of multi-scale modeling of the heart, along with the compromises needed to achieve subject-specific simulations. Reader will then learn how AI technologies can unlock robust estimations of cardiac anatomy, obtain meta-models for real-time biophysical computations, and estimate model parameters from routine clinical data. Concepts are all illustrated through concrete clinical applications. Presents recent advances in computational modeling of heart function and artificial intelligence technologies for subject-specific applications Discusses AI-based technologies for robust anatomical modeling from medical images, data-driven reduction of multi-scale cardiac models, and estimations of physiological parameters from clinical data Illustrates the technology through concrete clinical applications and discusses potential impacts and next steps needed for clinical translation

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Deep Learning and Convolutional Neural Networks for Medical Image Computing Book
Author : Le Lu,Yefeng Zheng,Gustavo Carneiro,Lin Yang
Publisher : Springer
Release : 2017-07-12
ISBN : 331942999X
Language : En, Es, Fr & De

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Book Description :

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

Data Mining and Medical Knowledge Management Cases and Applications

Data Mining and Medical Knowledge Management  Cases and Applications Book
Author : Berka, Petr,Rauch, Jan,Zighed, Djamel Abdelkader
Publisher : IGI Global
Release : 2009-02-28
ISBN : 1605662194
Language : En, Es, Fr & De

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Book Description :

The healthcare industry produces a constant flow of data, creating a need for deep analysis of databases through data mining tools and techniques resulting in expanded medical research, diagnosis, and treatment. Data Mining and Medical Knowledge Management: Cases and Applications presents case studies on applications of various modern data mining methods in several important areas of medicine, covering classical data mining methods, elaborated approaches related to mining in electroencephalogram and electrocardiogram data, and methods related to mining in genetic data. A premier resource for those involved in data mining and medical knowledge management, this book tackles ethical issues related to cost-sensitive learning in medicine and produces theoretical contributions concerning general problems of data, information, knowledge, and ontologies.

Biomedical Image Analysis and Machine Learning Technologies Applications and Techniques

Biomedical Image Analysis and Machine Learning Technologies  Applications and Techniques Book
Author : Gonzalez, Fabio A.,Romero, Eduardo
Publisher : IGI Global
Release : 2009-12-31
ISBN : 1605669571
Language : En, Es, Fr & De

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Book Description :

Medical images are at the base of many routine clinical decisions and their influence continues to increase in many fields of medicine. Since the last decade, computers have become an invaluable tool for supporting medical image acquisition, processing, organization and analysis. Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques provides a panorama of the current boundary between biomedical complexity coming from the medical image context and the multiple techniques which have been used for solving many of these problems. This innovative publication serves as a leading industry reference as well as a source of creative ideas for applications of medical issues.

KARDIO

KARDIO Book
Author : Ivan Bratko,Igor Mozetič,Nada Lavrač
Publisher : Mit Press
Release : 1989
ISBN :
Language : En, Es, Fr & De

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Book Description :

This book is the first detailed account of the development of a complex and successful expert system based on deep and qualitative knowledge. It shows how the qualitative modeling approach, using logic based representations and machine learning techniques, can be used to construct knowledge bases whose complexity is far beyond the capability of traditional, dialogue based techniques of knowledge acquisition.The relevant techniques are demonstrated in full detail in the building of Kardio, a medical expert system model of the human heart designed for the diagnosis of cardiac arrhythmias. Kardio's performance is estimated by cardiologists to be equivalent to that of a specialist of internal medicine (not a cardiologist) who is highly skilled in the reading of ECG recordings, and it can be used as a diagnostic tool in ECG interpretation. It may also be used for instruction in electrocardiography.The authors show how the model was compiled, by means of qualitative simulation and machine learning tools, into various representations that are suited for particular expert tasks. They investigate a hierarchical organization of a qualitative model and outline an experiment whereby the construction of a deep model is automated by means of machine learning techniques. The book contains a complete model of the electrical system of the heart that can be used to further development in this area of applications.Ivan Bratko, author of Prolog Programming for Artificial Intelligence, is a professor of computer science at E. Kardelj University and leads the AI laboratory at the Jozef Stefan Institute in Ljubljana, Yugoslavia. Igor Mozetic and Nada Lavrac are researchers at the institute.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine Book
Author : David Riaño,Szymon Wilk,Annette ten Teije
Publisher : Springer
Release : 2019-06-19
ISBN : 303021642X
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine Book
Author : Riccardo Bellazzi,Ameen Abu-Hanna,Jim Hunter
Publisher : Springer Science & Business Media
Release : 2007-06-29
ISBN : 3540735984
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 11th Conference on Artificial Intelligence in Medicine in Europe, AIME 2007, held in Amsterdam, The Netherlands in July 2007. The 28 revised full papers and 38 revised short papers presented were carefully reviewed and selected from 137 submissions. The papers are organized in topical sections on agent-based systems, temporal data mining, machine learning and knowledge discovery, text mining, natural language processing and generation, ontologies, decision support systems, applications of AI-based image processing techniques, protocols and guidelines, as well as workflow systems.

Web Based Applications in Healthcare and Biomedicine

Web Based Applications in Healthcare and Biomedicine Book
Author : Athina A. Lazakidou
Publisher : Springer Science & Business Media
Release : 2009-12-18
ISBN : 9781441912749
Language : En, Es, Fr & De

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Book Description :

Web-based applications provide the power of desktop and server applications with the exibility and accessibility of the web. Using web browsers, users can securely access applications from anywhere within the reach of the company intranet or extranet. The special issue strives to explore the advanced web-based information systems and database applications in healthcare area. Healthcare organizations are undergoing major reorganizations and adjustments to meet the increasing demands of improved healthcare access and quality, as well as lowered costs. As the use of information technology to process medical data increases, much of the critical information necessary to meet these challenges is being stored in digital format. Web-enabled information technologies can provide the means for greater access and more effective integration of healthcare information from disparate computer applications and other information resources. This book presents studies from leading researchers and practitioners focusing on the current challenges, directions, trends, and opportunities associated with heal- care organizations and their strategic use of web-enabled technologies. Managing healthcare information systems with web-enabled technologies is an excellent ve- cle for understanding current and potential uses of Internet technology in the broad areas of healthcare and medical applications.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine Book
Author : Lei Xing,Maryellen L. Giger,James K Min
Publisher : Academic Press
Release : 2020-09-16
ISBN : 0128212586
Language : En, Es, Fr & De

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Book Description :

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

Machine Learning

Machine Learning Book
Author : Claude Sammut,Achim Hoffmann
Publisher : Morgan Kaufmann
Release : 2002
ISBN :
Language : En, Es, Fr & De

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Book Description :

Proceedings of the annual International Conferences on Machine Learning, 1988-present. Current volume: ICML 2002: 19th International Conference on Machine Learning. Submissions are expected that describe empirical, theoretical, and cognitive-modeling research in all areas of machine learning. Submissions that present algorithms for novel learning tasks, interdisciplinary research involving machine learning, or innovative applications of machine learning techniques to challenging, real-world problems are especially encouraged.

Signal Processing and Machine Learning for Biomedical Big Data

Signal Processing and Machine Learning for Biomedical Big Data Book
Author : Ervin Sejdic,Tiago H. Falk
Publisher : CRC Press
Release : 2018-07-04
ISBN : 1351061216
Language : En, Es, Fr & De

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Book Description :

This will be a comprehensive, multi-contributed reference work that will detail the latest research and developments in biomedical signal processing related to big data medical analysis. It will describe signal processing, machine learning, and parallel computing strategies to revolutionize the world of medical analytics and diagnosis as presented by world class researchers and experts in this important field. The chapters will desribe tools that can be used by biomedical and clinical practitioners as well as industry professionals. It will give signal processing researchers a glimpse into the issues faced with Big Medical Data.