Machine Learning and AI for Healthcare

Machine Learning and AI for Healthcare
Author: Arjun Panesar
Publsiher: Apress
Total Pages: 368
Release: 2019-02-04
ISBN 10: 1484237994
ISBN 13: 9781484237991
Language: EN, FR, DE, ES & NL

Machine Learning and AI for Healthcare Book Review:

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 Learn Gain 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 agents Who 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.

Applications of Artificial Intelligence and Machine Learning in Healthcare

Applications of Artificial Intelligence and Machine Learning in Healthcare
Author: Hassan Alamoudi
Publsiher: Anonim
Total Pages: 329
Release: 2019
ISBN 10:
ISBN 13: OCLC:1139617986
Language: EN, FR, DE, ES & NL

Applications of Artificial Intelligence and Machine Learning in Healthcare Book Review:

Artificial Intelligence (AI) is becoming a ubiquitous term that is used in many fields of research or the popular culture. Among these fields that was affected by this hype is the healthcare sector. Along with its subdomain, Machine Learning (ML), they established an environment of interest in the promises of machines versus humans capabilities. Though artificial intelligence applications in healthcare such as interpreting ECGs could date back to the mid of the twentieth century, the promises of AI still at its beginning when it comes to new breakthroughs. This is due to the transformation into a digital world and new advancements in the processing capabilities. Computer vision has contributed the most to the healthcare sector where it can leverage doctors and practitioners with automated classification and annotations as a preparing step. This kind of mechanism is the best suited for applications of AI in healthcare. However, the amount of data in other forms such as textual or lab results is exceeding the force power. While a solution could be to use machines to learn and propose solutions, the results could be catastrophic and human lives are on stake. So, explainable AI could be beneficial where it analyzes and makes predictions that can be trusted by the users. The study here is conducted on cardiovascular patients dataset to predict the presence or absence of the disease. Classifications techniques used include Nave Bayes, Logistic Regression, Decision Trees, Support Vector Machines, and Artificial Neural Networks. The Logistic regression model achieved the best Area under the curve. Moreover, an extension of the previous studies discussed is conducted to explain the model and to show how models of AI can be trusted and not used as black-boxes.

Artificial Intelligence in Healthcare

Artificial Intelligence in Healthcare
Author: Adam Bohr,Kaveh Memarzadeh
Publsiher: Academic Press
Total Pages: 378
Release: 2020-06-21
ISBN 10: 0128184396
ISBN 13: 9780128184394
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Healthcare Book Review:

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

Machine Learning with Health Care Perspective

Machine Learning with Health Care Perspective
Author: Vishal Jain,Jyotir Moy Chatterjee
Publsiher: Springer Nature
Total Pages: 415
Release: 2020-03-09
ISBN 10: 3030408507
ISBN 13: 9783030408503
Language: EN, FR, DE, ES & NL

Machine Learning with Health Care Perspective Book Review:

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.

Machine Learning for Healthcare

Machine Learning for Healthcare
Author: Rashmi Agrawal,Jyotir Moy Chatterjee,Abhishek Kumar,Pramod Singh Rathore,Dac-Nhuong Le
Publsiher: CRC Press
Total Pages: 204
Release: 2020-12-09
ISBN 10: 1000221881
ISBN 13: 9781000221886
Language: EN, FR, DE, ES & NL

Machine Learning for Healthcare Book Review:

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.

Machine Intelligence for Healthcare

Machine Intelligence for Healthcare
Author: Francis X. Campion,Gunnar Carlsson,Francis , FACP
Publsiher: Anonim
Total Pages: 346
Release: 2017-02-02
ISBN 10: 9781542924948
ISBN 13: 1542924944
Language: EN, FR, DE, ES & NL

Machine Intelligence for Healthcare Book Review:

Machine Intelligence for Healthcare is a must read for physician leaders, health insurance executives, clinical researchers, public health officials, data scientists and software engineers seeking to understand this pivotal innovation in the information revolution in healthcare. MI for Healthcare provides a detailed introduction of Machine Intelligence, then takes the reader on a journey through the basics of machine learning, topological data analysis and applications of machine intelligence software for healthcare and life sciences. Over 20 case studies cover topics related to clinical variation analysis, hospital clinical pathways, population health management, genetic analysis, precision medicine, healthcare revenue cycle, and payment integrity. The book includes a detailed introduction of the mathematics of topology and concepts of machine learning algorithms. This provides an understanding for the central role which machine intelligence software is now playing in the emergence of the "learning healthcare system" and success in the new world of value-based healthcare delivery.

Advancement of Artificial Intelligence in Healthcare Engineering

Advancement of Artificial Intelligence in Healthcare Engineering
Author: Dilip Singh Sisodia,Ram Bilas Pachori,Lalit Garg
Publsiher: Medical Information Science Reference
Total Pages: 300
Release: 2020
ISBN 10: 9781799821205
ISBN 13: 179982120X
Language: EN, FR, DE, ES & NL

Advancement of Artificial Intelligence in Healthcare Engineering Book Review:

"This book explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in the design, implementation, and optimization of challenging healthcare engineering solutions"--

eHealth

eHealth
Author: Thomas F. Heston
Publsiher: BoD – Books on Demand
Total Pages: 184
Release: 2018-08-01
ISBN 10: 1789235227
ISBN 13: 9781789235227
Language: EN, FR, DE, ES & NL

eHealth Book Review:

eHealth has revolutionized health care and the practice of medicine. Internet technologies have given the most rural communities access to healthcare services, and automated computer algorithms are improving medical diagnoses and speeding up the delivery of care. Handheld apps, wearable devices, and artificial intelligence lead the way, creating a global healthcare solution that is smarter and more accessible. Read what leaders in the field are doing to advance the use of electronic technology to improve global health.

Machine Learning for Healthcare Analytics Projects

Machine Learning for Healthcare Analytics Projects
Author: Eduonix Learning Solutions
Publsiher: Packt Publishing Ltd
Total Pages: 134
Release: 2018-10-30
ISBN 10: 1789532523
ISBN 13: 9781789532524
Language: EN, FR, DE, ES & NL

Machine Learning for Healthcare Analytics Projects Book Review:

Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn Key Features Develop a range of healthcare analytics projects using real-world datasets Implement key machine learning algorithms using a range of libraries from the Python ecosystem Accomplish intermediate-to-complex tasks by building smart AI applications using neural network methodologies Book Description Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics. This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks. By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain. What you will learn Explore super imaging and natural language processing (NLP) to classify DNA sequencing Detect cancer based on the cell information provided to the SVM Apply supervised learning techniques to diagnose autism spectrum disorder (ASD) Implement a deep learning grid and deep neural networks for detecting diabetes Analyze data from blood pressure, heart rate, and cholesterol level tests using neural networks Use ML algorithms to detect autistic disorders Who this book is for Machine Learning for Healthcare Analytics Projects is for data scientists, machine learning engineers, and healthcare professionals who want to implement machine learning algorithms to build smart AI applications. Basic knowledge of Python or any programming language is expected to get the most from this book.

Deep Learning in Healthcare

Deep Learning in Healthcare
Author: Yen-Wei Chen,Lakhmi C. Jain
Publsiher: Springer Nature
Total Pages: 218
Release: 2019-11-18
ISBN 10: 3030326063
ISBN 13: 9783030326067
Language: EN, FR, DE, ES & NL

Deep Learning in Healthcare Book Review:

This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.

Healthcare and Artificial Intelligence

Healthcare and Artificial Intelligence
Author: Bernard Nordlinger,Cédric Villani,Daniela Rus
Publsiher: Springer Nature
Total Pages: 279
Release: 2020-03-17
ISBN 10: 3030321614
ISBN 13: 9783030321611
Language: EN, FR, DE, ES & NL

Healthcare and Artificial Intelligence Book Review:

This book provides an overview of the role of AI in medicine and, more generally, of issues at the intersection of mathematics, informatics, and medicine. It is intended for AI experts, offering them a valuable retrospective and a global vision for the future, as well as for non-experts who are curious about this timely and important subject. Its goal is to provide clear, objective, and reasonable information on the issues covered, avoiding any fantasies that the topic “AI” might evoke. In addition, the book seeks to provide a broad kaleidoscopic perspective, rather than deep technical details.

Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare

Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare
Author: Mark Chang
Publsiher: CRC Press
Total Pages: 352
Release: 2020-05-12
ISBN 10: 1000767302
ISBN 13: 9781000767308
Language: EN, FR, DE, ES & NL

Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare Book Review:

Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language processing rely on computer science’s use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI research, the book: · Covers broad AI topics in drug development, precision medicine, and healthcare. · Elaborates on supervised, unsupervised, reinforcement, and evolutionary learning methods. · Introduces the similarity principle and related AI methods for both big and small data problems. · Offers a balance of statistical and algorithm-based approaches to AI. · Provides examples and real-world applications with hands-on R code. · Suggests the path forward for AI in medicine and artificial general intelligence. As well as covering the history of AI and the innovative ideas, methodologies and software implementation of the field, the book offers a comprehensive review of AI applications in medical sciences. In addition, readers will benefit from hands on exercises, with included R code.

Demystifying Big Data and Machine Learning for Healthcare

Demystifying Big Data and Machine Learning for Healthcare
Author: Prashant Natarajan,John C. Frenzel,Detlev H. Smaltz
Publsiher: CRC Press
Total Pages: 210
Release: 2017-02-15
ISBN 10: 1315389312
ISBN 13: 9781315389318
Language: EN, FR, DE, ES & NL

Demystifying Big Data and Machine Learning for Healthcare Book Review:

Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.

AI and Machine Learning for Healthcare

AI and Machine Learning for Healthcare
Author: Aileen Nielsen
Publsiher: Anonim
Total Pages: 329
Release: 2017
ISBN 10:
ISBN 13: OCLC:1137155909
Language: EN, FR, DE, ES & NL

AI and Machine Learning for Healthcare Book Review:

"Artificial intelligence (AI) and machine learning (ML) in healthcare comprise a rapidly expanding and very promising field. The marriage of technology and health has the potential to empower medical professionals and patients alike, while drastically cutting the cost of healthcare. Using a measured no-hype approach, this video will help technology entrepreneurs, product managers, and health care executives understand what can be done with AI and ML in healthcare today and what concepts are most crucial to producing valuable applications in the near future."--Resource description page.

Deep Medicine

Deep Medicine
Author: Eric Topol
Publsiher: Basic Books
Total Pages: 400
Release: 2019-03-12
ISBN 10: 1541644646
ISBN 13: 9781541644649
Language: EN, FR, DE, ES & NL

Deep Medicine Book Review:

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.

Computational Intelligence for Machine Learning and Healthcare Informatics

Computational Intelligence for Machine Learning and Healthcare Informatics
Author: Rajshree Srivastava,Pradeep Kumar Mallick,Siddharth Swarup Rautaray,Manjusha Pandey
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 346
Release: 2020-06-22
ISBN 10: 3110648199
ISBN 13: 9783110648195
Language: EN, FR, DE, ES & NL

Computational Intelligence for Machine Learning and Healthcare Informatics Book Review:

This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.

Artificial Intelligence in Precision Health

Artificial Intelligence in Precision Health
Author: Debmalya Barh
Publsiher: Academic Press
Total Pages: 544
Release: 2020-03-04
ISBN 10: 0128173386
ISBN 13: 9780128173381
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Precision Health Book Review:

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

Biomedical Signal Processing and Artificial Intelligence in Healthcare

Biomedical Signal Processing and Artificial Intelligence in Healthcare
Author: Walid A. Zgallai
Publsiher: Academic Press
Total Pages: 268
Release: 2020-07-29
ISBN 10: 0128189479
ISBN 13: 9780128189474
Language: EN, FR, DE, ES & NL

Biomedical Signal Processing and Artificial Intelligence in Healthcare Book Review:

Biomedical Signal Processing and Artificial Intelligence in Healthcare is a new volume in the Developments in Biomedical Engineering and Bioelectronics series. This volume covers the basics of biomedical signal processing and artificial intelligence. It explains the role of machine learning in relation to processing biomedical signals and the applications in medicine and healthcare. The book provides background to statistical analysis in biomedical systems. Several types of biomedical signals are introduced and analyzed, including ECG and EEG signals. The role of Deep Learning, Neural Networks, and the implications of the expansion of artificial intelligence is covered. Biomedical Images are also introduced and processed, including segmentation, classification, and detection. This book covers different aspects of signals, from the use of hardware and software, and making use of artificial intelligence in problem solving. Dr Zgallai’s book has up to date coverage where readers can find the latest information, easily explained, with clear examples and illustrations. The book includes examples on the application of signal and image processing employing artificial intelligence to Alzheimer, Parkinson, ADHD, autism, and sleep disorders, as well as ECG and EEG signals. Developments in Biomedical Engineering and Bioelectronics is a 10-volume series which covers recent developments, trends and advances in this field. Edited by leading academics in the field, and taking a multidisciplinary approach, this series is a forum for cutting-edge, contemporary review articles and contributions from key ‘up-and-coming’ academics across the full subject area. The series serves a wide audience of university faculty, researchers and students, as well as industry practitioners. Coverage of the subject area and the latest advances and applications in biomedical signal processing and Artificial Intelligence. Contributions by recognized researchers and field leaders. On-line presentations, tutorials, application and algorithm examples.

The AI Advantage

The AI Advantage
Author: Thomas H. Davenport
Publsiher: Anonim
Total Pages: 248
Release: 2019-06-23
ISBN 10: 0262538008
ISBN 13: 9780262538008
Language: EN, FR, DE, ES & NL

The AI Advantage Book Review:

In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze--remember when it seemed plausible that IBM's Watson could cure cancer?--to explain how businesses can put artificial intelligence to work now, in the real world. His key recommendation: don't go for the "moonshot" (curing cancer, or synthesizing all investment knowledge); look for the "low-hanging fruit" to make your company more efficient. Davenport describes the major AI technologies and explains how they are being used, reports on the AI work done by large commercial enterprises like Amazon and Google, and outlines strategies and steps to becoming a cognitive corporation. This book provides an invaluable guide to the real-world future of business AI.

Medical Applications of Artificial Intelligence

Medical Applications of Artificial Intelligence
Author: Arvin Agah
Publsiher: CRC Press
Total Pages: 526
Release: 2013-11-06
ISBN 10: 143988434X
ISBN 13: 9781439884348
Language: EN, FR, DE, ES & NL

Medical Applications of Artificial Intelligence Book Review:

Enhanced, more reliable, and better understood than in the past, artificial intelligence (AI) systems can make providing healthcare more accurate, affordable, accessible, consistent, and efficient. However, AI technologies have not been as well integrated into medicine as predicted. In order to succeed, medical and computational scientists must develop hybrid systems that can effectively and efficiently integrate the experience of medical care professionals with capabilities of AI systems. After providing a general overview of artificial intelligence concepts, tools, and techniques, Medical Applications of Artificial Intelligence reviews the research, focusing on state-of-the-art projects in the field. The book captures the breadth and depth of the medical applications of artificial intelligence, exploring new developments and persistent challenges.