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Machine Learning in Cardiovascular Medicine

Machine Learning in Cardiovascular Medicine
A Book

by Subhi J. Al'Aref,Gurpreet Singh,Lohendran Baskaran,Dimitri Metaxas

  • Publisher : Academic Press
  • Release : 2020-11-20
  • Pages : 454
  • ISBN : 0128202742
  • Language : En, Es, Fr & De
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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

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine
Technical Basis and Clinical Applications

by Lei Xing,Maryellen L. Giger,James K Min

  • Publisher : Academic Press
  • Release : 2020-09-16
  • Pages : 568
  • ISBN : 0128212586
  • Language : En, Es, Fr & De
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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 and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting

Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting
First International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings

by Hongen Liao,Simone Balocco,Guijin Wang,Feng Zhang,Yongpan Liu,Zijian Ding,Luc Duong,Renzo Phellan,Guillaume Zahnd,Katharina Breininger,Shadi Albarqouni,Stefano Moriconi,Su-Lin Lee,Stefanie Demirci

  • Publisher : Springer
  • Release : 2019-10-13
  • Pages : 212
  • ISBN : 9783030333263
  • Language : En, Es, Fr & De
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This book constitutes the refereed proceedings of the First International Workshop on Machine Learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For MLMECH 2019, 16 papers were accepted for publication from a total of 21 submissions. They focus on machine learning techniques and analyzing of ECG data in the diagnosis of heart diseases. CVII-STENT 2019 accepted all 8 submissiones for publication. They contain technological and scientific research concerning endovascular procedures.

Machine Learning, Big Data, and IoT for Medical Informatics

Machine Learning, Big Data, and IoT for Medical Informatics
A Book

by Pardeep Kumar,Yugal Kumar,Mohamed A. Tawhid

  • Publisher : Academic Press
  • Release : 2021-06-13
  • Pages : 458
  • ISBN : 0128217812
  • Language : En, Es, Fr & De
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Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT. Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems. Includes several privacy preservation techniques for medical data. Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis. Offers case studies and applications relating to machine learning, big data, and health care analysis.

Precision Medicine in Cardiovascular Disease Prevention

Precision Medicine in Cardiovascular Disease Prevention
A Book

by Seth S. Martin

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 3030750558
  • Language : En, Es, Fr & De
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Deep Learning for Medical Decision Support Systems

Deep Learning for Medical Decision Support Systems
A Book

by Utku Kose,Omer Deperlioglu,Jafar Alzubi,Bogdan Patrut

  • Publisher : Springer Nature
  • Release : 2020-06-17
  • Pages : 171
  • ISBN : 981156325X
  • Language : En, Es, Fr & De
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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.

Advances in Information and Communication

Advances in Information and Communication
Proceedings of the 2021 Future of Information and Communication Conference (FICC), Volume 1

by Kohei Arai

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 3030731006
  • Language : En, Es, Fr & De
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Intelligence-Based Medicine

Intelligence-Based Medicine
Artificial Intelligence and Human Cognition in Clinical Medicine and Healthcare

by Anthony C. Chang

  • Publisher : Academic Press
  • Release : 2020-06-27
  • Pages : 534
  • ISBN : 0128233389
  • Language : En, Es, Fr & De
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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

Industrial Applications of Machine Learning

Industrial Applications of Machine Learning
A Book

by Pedro Larrañaga,David Atienza,Javier Diaz-Rozo,Alberto Ogbechie,Carlos Esteban Puerto-Santana,Concha Bielza

  • Publisher : CRC Press
  • Release : 2018-12-12
  • Pages : 336
  • ISBN : 135112837X
  • Language : En, Es, Fr & De
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Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems. Features Describes the opportunities, challenges, issues, and trends offered by the fourth industrial revolution Provides a user-friendly introduction to machine learning with examples of cutting-edge applications in different industrial sectors Includes four case studies addressing real-world industrial problems solved with machine learning techniques A dedicated website for the book contains the datasets of the case studies for the reader's reproduction, enabling the groundwork for future problem-solving Uses of three of the most widespread software and programming languages within the engineering and data science communities, namely R, Python, and Weka

Proceedings of the 2nd International Conference on Healthcare Science and Engineering

Proceedings of the 2nd International Conference on Healthcare Science and Engineering
A Book

by Chase Q. Wu,Ming-Chien Chyu,Jaime Lloret,Xianxian Li

  • Publisher : Springer
  • Release : 2019-05-09
  • Pages : 306
  • ISBN : 9811368376
  • Language : En, Es, Fr & De
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This book presents a compilation of selected papers from the 2nd International Conference on Healthcare Science and Engineering (Healthcare 2018). The work focuses on novel computing, networking, and data analytics techniques for various issues in healthcare. The book is a valuable resource for academic researchers and practitioners working in the field.

Machine Learning for Medical Image Reconstruction

Machine Learning for Medical Image Reconstruction
First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings

by Florian Knoll,Andreas Maier,Daniel Rueckert

  • Publisher : Springer
  • Release : 2018-09-11
  • Pages : 158
  • ISBN : 3030001296
  • Language : En, Es, Fr & De
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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.

Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications

Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications
ICMISC 2020

by Vinit Kumar Gunjan,Jacek M. Zurada

  • Publisher : Springer Nature
  • Release : 2020-10-17
  • Pages : 998
  • ISBN : 9811572348
  • Language : En, Es, Fr & De
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This book gathers selected research papers presented at the International Conference on Recent Trends in Machine Learning, IOT, Smart Cities & Applications (ICMISC 2020), held on 29–30 March 2020 at CMR Institute of Technology, Hyderabad, Telangana, India. Discussing current trends in machine learning, Internet of things, and smart cities applications, with a focus on multi-disciplinary research in the area of artificial intelligence and cyber-physical systems, this book is a valuable resource for scientists, research scholars and PG students wanting formulate their research ideas and find the future directions in these areas. Further, it serves as a reference work anyone wishing to understand the latest technologies used by practicing engineers around the globe.

Process Improvement in Heart Failure, An Issue of Heart Failure Clinics EBK

Process Improvement in Heart Failure, An Issue of Heart Failure Clinics EBK
A Book

by Clyde Yancy,Kannan Mutharasan

  • Publisher : Elsevier Health Sciences
  • Release : 2020-09-25
  • Pages : 352
  • ISBN : 0323755534
  • Language : En, Es, Fr & De
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This issue of Heart Failure Clinics, guest edited by Drs. Clyde W. Yancy and R. Kannan Mutharasan, will cover Process Improvement in Heart Failure. This issue is one of four issues selected each year by our series consulting editor, Dr. Eduardo Bossone. Topics discussed in this issue will include: Approaching Process Improvement, Identifying Heart Failure Patients, Predicting High-risk Patients and High-Risk Outcomes in Heart Failure, Selecting the Correct Target for Improvement in Heart Failure Care and Improving Adherence, Empowering Patients Living with Heart Failure with Social Media and Technology, Transitioning Heart Failure Patients to Outpatient Care, Innovating Outpatient Processes of Care and Anticipating Complex Care Algorithms, Addressing Co-morbidities in Heart Failure, Systematizing Heart Failure Population Health, Defragmenting Heart Failure Care, and Adapting to the Payment Landscape.

Big Data and Artificial Intelligence for Healthcare Applications

Big Data and Artificial Intelligence for Healthcare Applications
A Book

by Ankur Saxena,Nicolas Brault,Shazia Rashid

  • Publisher : CRC Press
  • Release : 2021-06-15
  • Pages : 286
  • ISBN : 1000387313
  • Language : En, Es, Fr & De
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This book covers a wide range of topics on the role of Artificial Intelligence, Machine Learning, and Big Data for healthcare applications and deals with the ethical issues and concerns associated with it. This book explores the applications in different areas of healthcare and highlights the current research. "Big Data and Artificial Intelligence for Healthcare Applications" covers healthcare big data analytics, mobile health and personalized medicine, clinical trial data management and presents how Artificial Intelligence can be used for early disease diagnosis prediction and prognosis. It also offers some case studies that describes the application of Artificial Intelligence and Machine Learning in healthcare. Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

Advances in Communication and Computational Technology

Advances in Communication and Computational Technology
Select Proceedings of ICACCT 2019

by Gurdeep Singh Hura,Ashutosh Kumar Singh,Lau Siong Hoe

  • Publisher : Springer Nature
  • Release : 2020-08-13
  • Pages : 1537
  • ISBN : 9811553416
  • Language : En, Es, Fr & De
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This book presents high-quality peer-reviewed papers from the International Conference on Advanced Communication and Computational Technology (ICACCT) 2019 held at the National Institute of Technology, Kurukshetra, India. The contents are broadly divided into four parts: (i) Advanced Computing, (ii) Communication and Networking, (iii) VLSI and Embedded Systems, and (iv) Optimization Techniques.The major focus is on emerging computing technologies and their applications in the domain of communication and networking. The book will prove useful for engineers and researchers working on physical, data link and transport layers of communication protocols. Also, this will be useful for industry professionals interested in manufacturing of communication devices, modems, routers etc. with enhanced computational and data handling capacities.

Machine Learning in Medicine

Machine Learning in Medicine
A Book

by Ton J. Cleophas,Aeilko H. Zwinderman

  • Publisher : Springer Science & Business Media
  • Release : 2013-02-12
  • Pages : 265
  • ISBN : 9400758243
  • Language : En, Es, Fr & De
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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.

Machine Learning in Medicine - a Complete Overview

Machine Learning in Medicine - a Complete Overview
A Book

by Ton J. Cleophas,Aeilko H. Zwinderman

  • Publisher : Springer
  • Release : 2016-10-09
  • Pages : 516
  • ISBN : 9783319386386
  • Language : En, Es, Fr & De
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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.

Current and Future Role of Artificial Intelligence in Cardiac Imaging

Current and Future Role of Artificial Intelligence in Cardiac Imaging
A Book

by Steffen Erhard Petersen,Karim Lekadir,Alistair A. Young,Tim Leiner

  • Publisher : Frontiers Media SA
  • Release : 2020-10-09
  • Pages : 329
  • ISBN : 2889660583
  • Language : En, Es, Fr & De
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Recent Trends in Communication and Intelligent Systems

Recent Trends in Communication and Intelligent Systems
Proceedings of ICRTCIS 2020

by Aditya Kumar Singh Pundir,Anupam Yadav,Swagatam Das

  • Publisher : Springer Nature
  • Release : 2021-04-16
  • Pages : 220
  • ISBN : 9811601674
  • Language : En, Es, Fr & De
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This book presents best selected research papers presented at the International Conference on Recent Trends in Communication and Intelligent Systems (ICRTCIS 2020), organized by Arya College of Engineering and IT, Jaipur, on 20-21 November 2020. It discusses the latest technologies in communication and intelligent systems, covering various areas of communication engineering, such as signal processing, VLSI design, embedded systems, wireless communications, and electronics and communications in general. Featuring work by leading researchers and technocrats, the book serves as a valuable reference resource for young researchers and academics as well as practitioners in industry.

Machine Learning in Medicine – A Complete Overview

Machine Learning in Medicine – A Complete Overview
A Book

by Ton J. Cleophas,Aeilko H. Zwinderman

  • Publisher : Springer Nature
  • Release : 2020-03-03
  • Pages : 667
  • ISBN : 303033970X
  • Language : En, Es, Fr & De
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Adequate health and health care is no longer possible without proper data supervision from modern machine learning methodologies like cluster models, neural networks, and other data mining methodologies. The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector, and 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 this second edition the authors have removed the textual errors from the first edition. Also, the improved tables from the first edition, have been replaced with the original tables from the software programs as applied. This is, because, unlike the former, the latter were without error, and readers were better familiar with them. The main purpose of the first edition was, to provide stepwise analyses of the novel methods from data examples, but background information and clinical relevance information may have been somewhat lacking. Therefore, each chapter now contains a section entitled "Background Information". Machine learning may be more informative, and may provide better sensitivity of testing than traditional analytic methods may do. In the second edition a place has been given for the use of machine learning not only to the analysis of observational clinical data, but also to that of controlled clinical trials. Unlike the first edition, the second edition has drawings in full color providing a helpful extra dimension to the data analysis. Several machine learning methodologies not yet covered in the first edition, but increasingly important today, have been included in this updated edition, for example, negative binomial and Poisson regressions, sparse canonical analysis, Firth's bias adjusted logistic analysis, omics research, eigenvalues and eigenvectors.