Machine Learning For Healthcare

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Machine Learning and AI for Healthcare

Arjun Panesar
Machine Learning and AI for Healthcare Book PDF

Author : Arjun Panesar
Publisher : Apress
Page : 390 pages
File Size : 53,8 Mb
Release : 2019-02-04
Category : Computers
ISBN : 9781484237991

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Machine Learning and AI for Healthcare by Arjun Panesar Pdf

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.

Introduction to Deep Learning for Healthcare

Cao Xiao,Jimeng Sun
Introduction to Deep Learning for Healthcare Book PDF

Author : Cao Xiao,Jimeng Sun
Publisher : Springer Nature
Page : 236 pages
File Size : 45,9 Mb
Release : 2021-11-11
Category : Medical
ISBN : 9783030821845

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Introduction to Deep Learning for Healthcare by Cao Xiao,Jimeng Sun Pdf

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.

Machine Learning in Healthcare

Bikesh Kumar Singh,G.R. Sinha
Machine Learning in Healthcare Book PDF

Author : Bikesh Kumar Singh,G.R. Sinha
Publisher : CRC Press
Page : 248 pages
File Size : 49,8 Mb
Release : 2022-02-17
Category : Computers
ISBN : 9781000540406

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Machine Learning in Healthcare by Bikesh Kumar Singh,G.R. Sinha Pdf

Artificial intelligence (AI) and machine learning (ML) techniques play an important role in our daily lives by enhancing predictions and decision-making for the public in several fields such as financial services, real estate business, consumer goods, social media, etc. Despite several studies that have proved the efficacy of AI/ML tools in providing improved healthcare solutions, it has not gained the trust of health-care practitioners and medical scientists. This is due to poor reporting of the technology, variability in medical data, small datasets, and lack of standard guidelines for application of AI. Therefore, the development of new AI/ML tools for various domains of medicine is an ongoing field of research. Machine Learning in Healthcare: Fundamentals and Recent Applications discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises. This text is ideal for readers interested in machine learning without any background knowledge and looking to implement machine-learning models for healthcare systems.

Deep Learning in Healthcare

Yen-Wei Chen,Lakhmi C. Jain
Deep Learning in Healthcare Book PDF

Author : Yen-Wei Chen,Lakhmi C. Jain
Publisher : Springer Nature
Page : 225 pages
File Size : 40,9 Mb
Release : 2019-11-18
Category : Technology & Engineering
ISBN : 9783030326067

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Deep Learning in Healthcare by Yen-Wei Chen,Lakhmi C. Jain Pdf

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.

Machine Learning with Health Care Perspective

Vishal Jain,Jyotir Moy Chatterjee
Machine Learning with Health Care Perspective Book PDF

Author : Vishal Jain,Jyotir Moy Chatterjee
Publisher : Springer Nature
Page : 418 pages
File Size : 44,5 Mb
Release : 2020-03-09
Category : Technology & Engineering
ISBN : 9783030408503

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Machine Learning with Health Care Perspective by Vishal Jain,Jyotir Moy Chatterjee Pdf

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.

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

Pradeep N,Sandeep Kautish,Sheng-Lung Peng
Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics Book PDF

Author : Pradeep N,Sandeep Kautish,Sheng-Lung Peng
Publisher : Academic Press
Page : 374 pages
File Size : 48,8 Mb
Release : 2021-06-10
Category : Science
ISBN : 9780128220443

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Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics by Pradeep N,Sandeep Kautish,Sheng-Lung Peng Pdf

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics Unique case study approach provides readers with insights for practical clinical implementation

Machine Learning in Healthcare Informatics

Sumeet Dua,U. Rajendra Acharya,Prerna Dua
Machine Learning in Healthcare Informatics Book PDF

Author : Sumeet Dua,U. Rajendra Acharya,Prerna Dua
Publisher : Springer Science & Business Media
Page : 332 pages
File Size : 51,5 Mb
Release : 2013-12-09
Category : Technology & Engineering
ISBN : 9783642400179

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Machine Learning in Healthcare Informatics by Sumeet Dua,U. Rajendra Acharya,Prerna Dua Pdf

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.

Machine Learning for Healthcare Applications

Sachi Nandan Mohanty,G. Nalinipriya,Om Prakash Jena,Achyuth Sarkar
Machine Learning for Healthcare Applications Book PDF

Author : Sachi Nandan Mohanty,G. Nalinipriya,Om Prakash Jena,Achyuth Sarkar
Publisher : John Wiley & Sons
Page : 418 pages
File Size : 55,8 Mb
Release : 2021-04-13
Category : Computers
ISBN : 9781119791812

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Machine Learning for Healthcare Applications by Sachi Nandan Mohanty,G. Nalinipriya,Om Prakash Jena,Achyuth Sarkar Pdf

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.

Artificial Intelligence and Machine Learning in Healthcare

Ankur Saxena,Shivani Chandra
Artificial Intelligence and Machine Learning in Healthcare Book PDF

Author : Ankur Saxena,Shivani Chandra
Publisher : Springer Nature
Page : 228 pages
File Size : 46,5 Mb
Release : 2021-05-06
Category : Science
ISBN : 9789811608117

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Artificial Intelligence and Machine Learning in Healthcare by Ankur Saxena,Shivani Chandra Pdf

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 Deep Learning in Efficacy Improvement of Healthcare Systems

Om Prakash Jena,Bharat Bhushan,Nitin Rakesh,Parma Nand Astya,Yousef Farhaoui
Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems Book PDF

Author : Om Prakash Jena,Bharat Bhushan,Nitin Rakesh,Parma Nand Astya,Yousef Farhaoui
Publisher : CRC Press
Page : 321 pages
File Size : 43,7 Mb
Release : 2022-05-18
Category : Computers
ISBN : 9781000486827

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Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems by Om Prakash Jena,Bharat Bhushan,Nitin Rakesh,Parma Nand Astya,Yousef Farhaoui Pdf

The goal of medical informatics is to improve life expectancy, disease diagnosis and quality of life. Medical devices have revolutionized healthcare and have led to the modern age of machine learning, deep learning and Internet of Medical Things (IoMT) with their proliferation, mobility and agility. This book exposes different dimensions of applications for computational intelligence and explains its use in solving various biomedical and healthcare problems in the real world. This book describes the fundamental concepts of machine learning and deep learning techniques in a healthcare system. The aim of this book is to describe how deep learning methods are used to ensure high-quality data processing, medical image and signal analysis and improved healthcare applications. This book also explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems. Furthermore, it provides the healthcare sector with innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modelling, advanced deployment, case studies, analytical results, computational structuring and significant progress in the field of machine learning and deep learning in healthcare applications. FEATURES Explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems Provides guidance in developing intelligence-based diagnostic systems, efficient models and cost-effective machines Provides the latest research findings, solutions to the concerning issues and relevant theoretical frameworks in the area of machine learning and deep learning for healthcare systems Describes experiences and findings relating to protocol design, prototyping, experimental evaluation, real testbeds and empirical characterization of security and privacy interoperability issues in healthcare applications Explores and illustrates the current and future impacts of pandemics and mitigates risk in healthcare with advanced analytics This book is intended for students, researchers, professionals and policy makers working in the fields of public health and in the healthcare sector. Scientists and IT specialists will also find this book beneficial for research exposure and new ideas in the field of machine learning and deep learning.

Machine Learning for Health Informatics

Andreas Holzinger
Machine Learning for Health Informatics Book PDF

Author : Andreas Holzinger
Publisher : Springer
Page : 481 pages
File Size : 44,5 Mb
Release : 2016-12-09
Category : Computers
ISBN : 9783319504780

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Machine Learning for Health Informatics by Andreas Holzinger Pdf

Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.

Machine Learning for Healthcare Technologies

David A. Clifton
Machine Learning for Healthcare Technologies Book PDF

Author : David A. Clifton
Publisher : IET
Page : 316 pages
File Size : 50,6 Mb
Release : 2016-10-28
Category : Computers
ISBN : 9781849199780

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Machine Learning for Healthcare Technologies by David A. Clifton Pdf

This book provides a snapshot of the state of current research at the interface between machine learning and healthcare with special emphasis on machine learning projects that are (or are close to) achieving improvement in patient outcomes. The book provides overviews on a range of technologies including detecting artefactual events in vital signs monitoring data; patient physiological monitoring; tracking infectious disease; predicting antibiotic resistance from genomic data; and managing chronic disease.

Demystifying Big Data and Machine Learning for Healthcare

Prashant Natarajan,John C. Frenzel,Detlev H. Smaltz
Demystifying Big Data and Machine Learning for Healthcare Book PDF

Author : Prashant Natarajan,John C. Frenzel,Detlev H. Smaltz
Publisher : CRC Press
Page : 233 pages
File Size : 49,5 Mb
Release : 2017-02-15
Category : Medical
ISBN : 9781315389301

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Demystifying Big Data and Machine Learning for Healthcare by Prashant Natarajan,John C. Frenzel,Detlev H. Smaltz Pdf

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.

Machine Learning for Healthcare Analytics Projects

Eduonix Learning Solutions
Machine Learning for Healthcare Analytics Projects Book PDF

Author : Eduonix Learning Solutions
Publisher : Packt Publishing Ltd
Page : 131 pages
File Size : 51,7 Mb
Release : 2018-10-30
Category : Computers
ISBN : 9781789532524

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Machine Learning for Healthcare Analytics Projects by Eduonix Learning Solutions Pdf

Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn Key FeaturesDevelop a range of healthcare analytics projects using real-world datasetsImplement key machine learning algorithms using a range of libraries from the Python ecosystemAccomplish intermediate-to-complex tasks by building smart AI applications using neural network methodologiesBook 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 learnExplore super imaging and natural language processing (NLP) to classify DNA sequencingDetect cancer based on the cell information provided to the SVMApply supervised learning techniques to diagnose autism spectrum disorder (ASD)Implement a deep learning grid and deep neural networks for detecting diabetesAnalyze data from blood pressure, heart rate, and cholesterol level tests using neural networksUse ML algorithms to detect autistic disordersWho 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.

Machine Learning for Healthcare

Rashmi Agrawal,Jyotir Moy Chatterjee,Dac-nhuong Le,Abhishek Kumar
Machine Learning for Healthcare Book PDF

Author : Rashmi Agrawal,Jyotir Moy Chatterjee,Dac-nhuong Le,Abhishek Kumar
Publisher : CRC Press
Page : 212 pages
File Size : 41,5 Mb
Release : 2020-12-09
Category : Artificial intelligence
ISBN : 0367352338

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Machine Learning for Healthcare by Rashmi Agrawal,Jyotir Moy Chatterjee,Dac-nhuong Le,Abhishek Kumar Pdf

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.