June 19, 2021

Download Ebook Free Machine Learning In Cardiovascular Medicine

Machine Learning in Cardiovascular Medicine

Machine Learning in Cardiovascular Medicine
Author : Subhi J. Al'Aref,Gurpreet Singh,Lohendran Baskaran,Dimitri Metaxas
Publisher : Academic Press
Release Date : 2020-11-20
Category : Medical
Total pages :454
GET BOOK

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

Machine Learning, Big Data, and IoT for Medical Informatics

Machine Learning, Big Data, and IoT for Medical Informatics
Author : Pardeep Kumar,Yugal Kumar,Mohamed A. Tawhid
Publisher : Academic Press
Release Date : 2021-06-13
Category : Computers
Total pages :458
GET BOOK

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.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine
Author : Lei Xing,Maryellen L. Giger,James K Min
Publisher : Academic Press
Release Date : 2020-09-16
Category : Business & Economics
Total pages :568
GET BOOK

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

Library & Information Science Abstracts

Library & Information Science Abstracts
Author : Anonim
Publisher : Unknown
Release Date : 2006
Category : Information science
Total pages :129
GET BOOK

Lightweight Deep Learning for Biomedical Image Segmentation

Lightweight Deep Learning for Biomedical Image Segmentation
Author : Dylan Uys
Publisher : Unknown
Release Date : 2019
Category :
Total pages :66
GET BOOK

Many techniques for analyzing cardiovascular health rely on cardiac magnetic resonance images that have been segmented to identify various components of the heart. Manually segmenting these images is cumbersome and prone to variability, which calls for the development of accessible automation tools for cardiac researchers. In order to benefit the developing symbiosis between machine learning and medicine, such tools must be accurate, efficient and inferentially transparent. This paper introduces a U-Net-based pipeline for left ventricular segmentation of short-axis CMRs. The U-Net, a Fully Convolutional Network known for its success in biomedical image segmentation, is a natural candidate for our task. This work constitutes the core of a larger-scale project focused on improving human disease models through the acceleration of animal cardiac research. Accordingly, experiments discussed here leverage both human and animal data to explore the efficacy of image processing and model training strategies. This paper focuses on optimizing our U-Nets for resource constrained environments, and demonstrates that these models require only a fraction of their typical convolutional filters. This reduction affords efficiency with the added benefit of explainability by improving the practicality of visualizing learned features. Inference can also be further optimized by pruning trained models without any loss of accuracy or the need for retraining. Specifically, we show that U-Nets with less than 2% of their original parameters train in minutes on a single GPU and achieve Dice scores above 0.95 on multiple CMR datasets. Furthermore, inference on hundreds of images can be performed in seconds on a laptop.

Intelligence-Based Medicine

Intelligence-Based Medicine
Author : Anthony C. Chang
Publisher : Academic Press
Release Date : 2020-06-27
Category : Medical
Total pages :534
GET BOOK

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

KARDIO

KARDIO
Author : Ivan Bratko,Igor Mozetič,Nada Lavrač
Publisher : Mit Press
Release Date : 1989
Category : Computers
Total pages :260
GET BOOK

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.

Nuclear Cardiology

Nuclear Cardiology
Author : Cláudio Tinoco Mesquita,Maria Fernanda Rezende
Publisher : Springer Nature
Release Date : 2021-03-22
Category : Medical
Total pages :800
GET BOOK

This book covers relevant concepts in nuclear cardiology, combining imaging techniques and clinical data to do so. Today, nuclear cardiology is a worldwide discipline connected to the broader field of cardiovascular imaging. The combination of clinical aspects (symptoms, medications, previous cardiac procedures), ancillary exams and nuclear images is key to decision-making in clinical practice. Thus, a book on this topic is essential to provide better outcomes for cardiology patients. The chapters cover a comprehensive range of topics in current cardiology practice, such as ambulatory patients, patients in emergency settings, patients after complex cardiac procedures, and patients during and after the use of cancer therapies that are potentially toxic for the heart (cardio-oncology). As such, multiple clinical scenarios are also presented: patients with suspected coronary disease, patients with heart failure of unknown origin, patients with acute chest pain in the emergency department, patients with suspected pulmonary embolism, patients with complications of the left ventricular assist device, etc. Furthermore, the book describes nuclear cardiology procedures and techniques, discusses the main clinical indications and scenarios for each procedure, presents new technological advances in the field (machine learning and artificial intelligence tools), and mentions the coronavirus disease 2019 (COVID-19) pandemic. Given its scope, the book offers a valuable guide and videos for various medical professionals, especially cardiologists and nuclear physicians.

Proceedings of the ... International Workshop on Machine Learning

Proceedings of the ... International Workshop on Machine Learning
Author : Anonim
Publisher : Unknown
Release Date : 1985
Category : Artificial intelligence
Total pages :129
GET BOOK

Digital Health in Focus of Predictive, Preventive and Personalised Medicine

Digital Health in Focus of Predictive, Preventive and Personalised Medicine
Author : Lotfi Chaari
Publisher : Springer Nature
Release Date : 2021
Category :
Total pages :129
GET BOOK

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Deep Learning and Convolutional Neural Networks for Medical Image Computing
Author : Le Lu,Yefeng Zheng,Gustavo Carneiro,Lin Yang
Publisher : Springer
Release Date : 2017-07-12
Category : Computers
Total pages :326
GET BOOK

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.

Aerospace Medicine and Biology

Aerospace Medicine and Biology
Author : Anonim
Publisher : Unknown
Release Date : 1991
Category : Aviation medicine
Total pages :129
GET BOOK

A selection of annotated references to unclassified reports and journal articles that were introduced into the NASA scientific and technical information system and announced in Scientific and technical aerospace reports (STAR) and International aerospace abstracts (IAA).

Machine Learning

Machine Learning
Author : Claude Sammut,Achim Hoffmann
Publisher : Morgan Kaufmann Pub
Release Date : 2002
Category : Computers
Total pages :706
GET BOOK

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.

Neural Networks in Healthcare

Neural Networks in Healthcare
Author : Rezaul Begg,Joarder Kamruzzaman,Ruhul Sarkar
Publisher : IGI Global
Release Date : 2006
Category : Computers
Total pages :332
GET BOOK

"This book covers state-of-the-art applications in many areas of medicine and healthcare"--Provided by publisher.

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
Author : S. Kevin Zhou,Dinggang Shen,Hayit Greenspan
Publisher : Academic Press
Release Date : 2017-01-30
Category :
Total pages :458
GET BOOK

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