Artificial Intelligence and Machine Learning in Healthcare

Artificial Intelligence and Machine Learning in Healthcare
Author: Ankur Saxena,Shivani Chandra
Publsiher: Springer Nature
Total Pages: 228
Release: 2021-05-06
ISBN 10: 9811608113
ISBN 13: 9789811608117
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Machine Learning in Healthcare Book Review:

This book reviews the application of artificial intelligence and machine learning in healthcare. It discusses integrating the principles of computer science, life science, and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. The initial chapters of the book cover the practical applications of artificial intelligence for disease prognosis & management. Further, the role of artificial intelligence and machine learning is discussed with reference to specific diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, and Covid-19. The chapters provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. The book also touches upon precision medicine, personalized medicine, and transfer learning, with the real examples. Further, it also discusses the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Covid -19. This book is a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in healthcare.

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 LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.

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.

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 in Healthcare

Machine Learning in Healthcare
Author: Bikesh Kumar Singh,G. R. Sinha
Publsiher: Unknown
Total Pages: 135
Release: 2022
ISBN 10: 9780367564438
ISBN 13: 0367564432
Language: EN, FR, DE, ES & NL

Machine Learning in Healthcare Book Review:

"Machine Learning in Healthcare discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. It covers fundamental concepts including mathematical requisites and traditional machine-learning framework followed by advanced machine-learning methods and their applications in medical fields"--

Big Data and Artificial Intelligence for Healthcare Applications

Big Data and Artificial Intelligence for Healthcare Applications
Author: Ankur Saxena,Nicolas Brault,Shazia Rashid
Publsiher: CRC Press
Total Pages: 286
Release: 2021-06-15
ISBN 10: 1000387313
ISBN 13: 9781000387315
Language: EN, FR, DE, ES & NL

Big Data and Artificial Intelligence for Healthcare Applications Book Review:

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.

Artificial Intelligence and Machine Learning in Public Healthcare

Artificial Intelligence and Machine Learning in Public Healthcare
Author: KC Santosh,Loveleen Gaur
Publsiher: Springer
Total Pages: 74
Release: 2021-11-27
ISBN 10: 9789811667671
ISBN 13: 9811667675
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Machine Learning in Public Healthcare Book Review:

This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health. It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues. Public health requires special attention as it drives economy and education system. COVID-19 is an example—a truly infectious disease outbreak. The vision of WHO is to create public health services that can deal with abovementioned crucial challenges by focusing on the following elements: health protection, disease prevention and health promotion. For these issues, in the big data analytics era, AI and ML tools/techniques have potential to improve public health (e.g., existing healthcare solutions and wellness services). In other words, they have proved to be valuable tools not only to analyze/diagnose pathology but also to accelerate decision-making procedure especially when we consider resource-constrained regions.

Introduction to Deep Learning for Healthcare

Introduction to Deep Learning for Healthcare
Author: Cao Xiao,Jimeng Sun
Publsiher: Springer Nature
Total Pages: 232
Release: 2021-11-11
ISBN 10: 3030821846
ISBN 13: 9783030821845
Language: EN, FR, DE, ES & NL

Introduction to Deep Learning for Healthcare Book Review:

This textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that can effectively model health data. Healthcare data: Among all healthcare technologies, electronic health records (EHRs) had vast adoption and a significant impact on healthcare delivery in recent years. One crucial benefit of EHRs is to capture all the patient encounters with rich multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for diagnoses and procedures, lab results, and medication information. Unstructured data contain 1) clinical notes as text, 2) medical imaging data such as X-rays, echocardiogram, and magnetic resonance imaging (MRI), and 3) time-series data such as the electrocardiogram (ECG) and electroencephalogram (EEG). Beyond the data collected during clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors’ increasing use. The authors present deep learning case studies on all data described. Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep learning models are neural networks of many layers, which can extract multiple levels of features from raw data. Deep learning applied to healthcare is a natural and promising direction with many initial successes. The authors cover deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. It’s presented with concrete healthcare case studies such as clinical predictive modeling, readmission prediction, phenotyping, x-ray classification, ECG diagnosis, sleep monitoring, automatic diagnosis coding from clinical notes, automatic deidentification, medication recommendation, drug discovery (drug property prediction and molecule generation), and clinical trial matching. This textbook targets graduate-level students focused on deep learning methods and their healthcare applications. It can be used for the concepts of deep learning and its applications as well. Researchers working in this field will also find this book to be extremely useful and valuable for their research.

Artificial Intelligence in Healthcare and Medicine

Artificial Intelligence in Healthcare and Medicine
Author: Kayvan Najarian,Delaram Kahrobaei,Enrique Domínguez,Reza Soroushmehr
Publsiher: Unknown
Total Pages: 286
Release: 2022
ISBN 10: 9781000565843
ISBN 13: 100056584X
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Healthcare and Medicine Book Review:

This book provides a comprehensive overview of the recent developments in clinical decision support systems, precision health, and data science in medicine. The book targets clinical researchers and computational scientists seeking to understand the recent advances of artificial intelligence (AI) in health and medicine. Since AI and its applications are believed to have the potential to revolutionize healthcare and medicine, there is a clear need to explore and investigate the state-of-the-art advancements in the field. This book provides a detailed description of the advancements, challenges, and opportunities of using AI in medical and health applications. Over 10 case studies are included in the book that cover topics related to biomedical image processing, machine learning for healthcare, clinical decision support systems, visualization of high dimensional data, data security and privacy, bioinformatics, and biometrics. The book is intended for clinical researchers and computational scientists seeking to understand the recent advances of AI in health and medicine. Many universities may use the book as a secondary training text. Companies in the healthcare sector can greatly benefit from the case studies covered in the book. Moreover, this book also: Provides an overview of the recent developments in clinical decision support systems, precision health, and data science in medicine Examines the advancements, challenges, and opportunities of using AI in medical and health applications Includes 10 cases for practical application and reference Kayvan Najarian is a Professor in the Department of Computational Medicine and Bioinformatics, Department of Electrical Engineering and Computer Science, and Department of Emergency Medicine at the University of Michigan, Ann Arbor. Delaram Kahrobaei is the University Dean for Research at City University of New York (CUNY), a Professor of Computer Science and Mathematics, Queens College CUNY, and the former Chair of Cyber Security, University of York. Enrique Domínguez is a professor in the Department of Computer Science at the University of Malaga and a member of the Biomedical Research Institute of Malaga. Reza Soroushmehr is a Research Assistant Professor in the Department of Computational Medicine and Bioinformatics and a member of the Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor.

Artificial Intelligence and Big Data Analytics for Smart Healthcare

Artificial Intelligence and Big Data Analytics for Smart Healthcare
Author: Miltiadis Lytras,Akila Sarirete,Anna Visvizi,Kwok Tai Chui
Publsiher: Academic Press
Total Pages: 290
Release: 2021-10-22
ISBN 10: 0128220627
ISBN 13: 9780128220627
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Big Data Analytics for Smart Healthcare Book Review:

Artificial Intelligence and Big Data Analytics for Smart Healthcare serves as a key reference for practitioners and experts involved in healthcare as they strive to enhance the value added of healthcare and develop more sustainable healthcare systems. It brings together insights from emerging sophisticated information and communication technologies such as big data analytics, artificial intelligence, machine learning, data science, medical intelligence, and, by dwelling on their current and prospective applications, highlights managerial and policymaking challenges they may generate. The book is split into five sections: big data infrastructure, framework and design for smart healthcare; signal processing techniques for smart healthcare applications; business analytics (descriptive, diagnostic, predictive and prescriptive) for smart healthcare; emerging tools and techniques for smart healthcare; and challenges (security, privacy, and policy) in big data for smart healthcare. The content is carefully developed to be understandable to different members of healthcare chain to leverage collaborations with researchers and industry. Presents a holistic discussion on the new landscape of data driven medical technologies including Big Data, Analytics, Artificial Intelligence, Machine Learning, and Precision Medicine Discusses such technologies with case study driven approach with reference to real world application and systems, to make easier the understanding to the reader not familiar with them Encompasses an international collaboration perspective, providing understandable knowledge to professionals involved with healthcare to leverage productive partnerships with technology developers

Machine Learning and the Internet of Medical Things in Healthcare

Machine Learning and the Internet of Medical Things in Healthcare
Author: Krishna Kant Singh,Mohamed Elhoseny,Akansha Singh,Ahmed A. Elngar
Publsiher: Academic Press
Total Pages: 290
Release: 2021-04-26
ISBN 10: 012823217X
ISBN 13: 9780128232170
Language: EN, FR, DE, ES & NL

Machine Learning and the Internet of Medical Things in Healthcare Book Review:

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks. Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies

Artificial Intelligence

Artificial Intelligence
Author: Sandeep Reddy
Publsiher: CRC Press
Total Pages: 336
Release: 2020-12-03
ISBN 10: 1000216861
ISBN 13: 9781000216868
Language: EN, FR, DE, ES & NL

Artificial Intelligence Book Review:

The rediscovery of the potential of artificial intelligence (AI) to improve healthcare delivery and patient outcomes has led to an increasing application of AI techniques such as deep learning, computer vision, natural language processing, and robotics in the healthcare domain. Many governments and health authorities have prioritized the application of AI in the delivery of healthcare. Also, technological giants and leading universities have established teams dedicated to the application of AI in medicine. These trends will mean an expanded role for AI in the provision of healthcare. Yet, there is an incomplete understanding of what AI is and its potential for use in healthcare. This book discusses the different types of AI applicable to healthcare and their application in medicine, population health, genomics, healthcare administration, and delivery. Readers, especially healthcare professionals and managers, will find the book useful to understand the different types of AI and how they are relevant to healthcare delivery. The book provides examples of AI being applied in medicine, population health, genomics, healthcare administration, and delivery and how they can commence applying AI in their health services. Researchers and technology professionals will also find the book useful to note current trends in the application of AI in healthcare and initiate their own projects to enable the application of AI in healthcare/medical domains.

Machine Learning for Healthcare Applications

Machine Learning for Healthcare Applications
Author: Sachi Nandan Mohanty,G. Nalinipriya,Om Prakash Jena,Achyuth Sarkar
Publsiher: John Wiley & Sons
Total Pages: 416
Release: 2021-04-13
ISBN 10: 1119791812
ISBN 13: 9781119791812
Language: EN, FR, DE, ES & NL

Machine Learning for Healthcare Applications Book Review:

When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.

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.

Data Analytics in Bioinformatics

Data Analytics in Bioinformatics
Author: Rabinarayan Satpathy,Tanupriya Choudhury,Suneeta Satpathy,Sachi Nandan Mohanty,Xiaobo Zhang
Publsiher: John Wiley & Sons
Total Pages: 544
Release: 2021-01-20
ISBN 10: 111978560X
ISBN 13: 9781119785606
Language: EN, FR, DE, ES & NL

Data Analytics in Bioinformatics Book Review:

Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine
Author: David Riaño,Szymon Wilk,Annette ten Teije
Publsiher: Springer
Total Pages: 429
Release: 2019-06-19
ISBN 10: 303021642X
ISBN 13: 9783030216429
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Medicine Book Review:

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.

Machine Learning in Healthcare Informatics

Machine Learning in Healthcare Informatics
Author: Sumeet Dua,U. Rajendra Acharya,Prerna Dua
Publsiher: Springer Science & Business Media
Total Pages: 332
Release: 2013-12-09
ISBN 10: 3642400175
ISBN 13: 9783642400179
Language: EN, FR, DE, ES & NL

Machine Learning in Healthcare Informatics Book Review:

The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.

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.

Artificial Intelligence in Healthcare

Artificial Intelligence in Healthcare
Author: Parag Mahajan
Publsiher: Medmantra, LLC
Total Pages: 628
Release: 2021-02
ISBN 10: 9781954612020
ISBN 13: 1954612028
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Healthcare Book Review:

① Do you know what AI is doing to improve our health and wellbeing? ② Does this new technology concern you, or impress you? ③ Do you want to know more about the future of AI in healthcare? Technology continues to advance at a pace that can seem bewildering. Nowhere else is it moving faster than in the health sector, where ♥AI is now being used to improve millions of lives♥. In this book, ◆ Artificial Intelligence in Healthcare: AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone ◆, you can discover the great improvements that AI is making, with chapters covering: The current applications and future of AI in healthcare and all major medical specialties ✓ The benefits and risks weighed up ✓ The ethics involved ✓ Machine learning and data science simplified ✓ AI's role in medical research and education, health insurance, drug discovery, electronic health records, and the fight against COVID-19 ✓ The roles that major corporations and start-up companies are playing ✓ The implementation of AI in clinical practice ✓ And lots more... Quite simply the most authoritative text on the subject, Artificial Intelligence in Healthcare - 3rd Edition, is an absorbing and compelling read for anyone who wants to know more. It is packed with more updated information than any other book currently available, written in easy-to-understand language, and accessible to all.

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.