Machine Learning For Health Informatics

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Machine Learning for Health Informatics

Author : Andreas Holzinger
Publisher : Springer
Page : 481 pages
File Size : 43,5 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 in Healthcare Informatics

Author : Sumeet Dua,U. Rajendra Acharya,Prerna Dua
Publisher : Springer Science & Business Media
Page : 334 pages
File Size : 42,9 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.

Deep Learning Techniques for Biomedical and Health Informatics

Author : Basant Agarwal,Valentina Emilia Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma
Publisher : Academic Press
Page : 367 pages
File Size : 41,7 Mb
Release : 2020-01-14
Category : Science
ISBN : 9780128190623

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Deep Learning Techniques for Biomedical and Health Informatics by Basant Agarwal,Valentina Emilia Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma Pdf

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis

Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics

Author : Sujata Dash,Subhendu Kumar Pani,Joel J. P. C. Rodrigues,Babita Majhi
Publisher : CRC Press
Page : 407 pages
File Size : 53,6 Mb
Release : 2022-02-10
Category : Computers
ISBN : 9781000534054

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Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics by Sujata Dash,Subhendu Kumar Pani,Joel J. P. C. Rodrigues,Babita Majhi Pdf

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others. • Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications • Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches • Discusses various techniques of IoT systems for healthcare data analytics • Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics • Focuses more on the application of algorithms in various real life biomedical and engineering problems

Computational Intelligence and Healthcare Informatics

Author : Om Prakash Jena,Alok Ranjan Tripathy,Ahmed A. Elngar,Zdzislaw Polkowski
Publisher : John Wiley & Sons
Page : 434 pages
File Size : 42,9 Mb
Release : 2021-10-19
Category : Computers
ISBN : 9781119818687

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Computational Intelligence and Healthcare Informatics by Om Prakash Jena,Alok Ranjan Tripathy,Ahmed A. Elngar,Zdzislaw Polkowski Pdf

COMPUTATIONAL INTELLIGENCE and HEALTHCARE INFORMATICS The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis. Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. Audience The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.

Deep Learning in Biomedical and Health Informatics

Author : M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari
Publisher : CRC Press
Page : 224 pages
File Size : 45,6 Mb
Release : 2021-09-26
Category : Computers
ISBN : 9781000429084

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Deep Learning in Biomedical and Health Informatics by M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari Pdf

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques. In short, the volume : Discusses the relationship between AI and healthcare, and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA. Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal. Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.

Machine Learning with Health Care Perspective

Author : Vishal Jain,Jyotir Moy Chatterjee
Publisher : Springer Nature
Page : 418 pages
File Size : 51,8 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.

Biomedical Data Mining for Information Retrieval

Author : Sujata Dash,Subhendu Kumar Pani,S. Balamurugan,Ajith Abraham
Publisher : John Wiley & Sons
Page : 450 pages
File Size : 40,7 Mb
Release : 2021-08-24
Category : Computers
ISBN : 9781119711247

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Biomedical Data Mining for Information Retrieval by Sujata Dash,Subhendu Kumar Pani,S. Balamurugan,Ajith Abraham Pdf

BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.

Big Data, IoT, and Machine Learning

Author : Rashmi Agrawal,Marcin Paprzycki,Neha Gupta
Publisher : CRC Press
Page : 237 pages
File Size : 46,7 Mb
Release : 2020-07-29
Category : Computers
ISBN : 9781000098303

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Big Data, IoT, and Machine Learning by Rashmi Agrawal,Marcin Paprzycki,Neha Gupta Pdf

The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them. Features Addresses the complete data science technologies workflow Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning Covers data processing and security solutions in IoT and Big Data applications Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems Presents security issues and data migration techniques of NoSQL databases

Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems

Author : Om Prakash Jena,Bharat Bhushan,Nitin Rakesh,Parma Nand Astya,Yousef Farhaoui
Publisher : CRC Press
Page : 321 pages
File Size : 41,8 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 Non/Less-Invasive Methods in Health Informatics

Author : Kun Qian,Liang Zhang,Kezhi Li,Juan Liu
Publisher : Frontiers Media SA
Page : 174 pages
File Size : 47,6 Mb
Release : 2021-11-26
Category : Medical
ISBN : 9782889717088

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Machine Learning for Non/Less-Invasive Methods in Health Informatics by Kun Qian,Liang Zhang,Kezhi Li,Juan Liu Pdf

Computational Intelligence for Machine Learning and Healthcare Informatics

Author : Rajshree Srivastava,Pradeep Kumar Mallick,Siddharth Swarup Rautaray,Manjusha Pandey
Publisher : Walter de Gruyter GmbH & Co KG
Page : 346 pages
File Size : 41,9 Mb
Release : 2020-06-22
Category : Computers
ISBN : 9783110648195

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Computational Intelligence for Machine Learning and Healthcare Informatics by Rajshree Srivastava,Pradeep Kumar Mallick,Siddharth Swarup Rautaray,Manjusha Pandey Pdf

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.

Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics

Author : Abhishek Kumar,Ashutosh Kumar Dubey,Sreenatha G. Anavatti,Pramod Singh Rathore
Publisher : CRC Press
Page : 242 pages
File Size : 40,6 Mb
Release : 2022-03-10
Category : Technology & Engineering
ISBN : 9781000539974

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Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics by Abhishek Kumar,Ashutosh Kumar Dubey,Sreenatha G. Anavatti,Pramod Singh Rathore Pdf

In the last two decades, machine learning has developed dramatically and is still experiencing a fast and everlasting change in paradigms, methodology, applications and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on their diversity and complexity. Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques, providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design and interdisciplinary challenges. This book is useful for research scholars and students involved in critical condition analysis and computation models.

Explainable AI in Healthcare

Author : Mehul S Raval,Mohendra Roy,Tolga Kaya,Rupal Kapdi
Publisher : CRC Press
Page : 346 pages
File Size : 53,6 Mb
Release : 2023-07-17
Category : Medical
ISBN : 9781000906400

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Explainable AI in Healthcare by Mehul S Raval,Mohendra Roy,Tolga Kaya,Rupal Kapdi Pdf

This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering. This book will benefit readers in the following ways: Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care Investigates bridges between computer scientists and physicians being built with XAI Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent Initiates discussions on human-AI relationships in health care Unites learning for privacy preservation in health care

Machine Learning for Healthcare

Author : Rashmi Agrawal,Jyotir Moy Chatterjee,Abhishek Kumar,Pramod Singh Rathore,Dac-Nhuong Le
Publisher : CRC Press
Page : 160 pages
File Size : 40,8 Mb
Release : 2020-12-08
Category : Computers
ISBN : 9781000221886

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Machine Learning for Healthcare by Rashmi Agrawal,Jyotir Moy Chatterjee,Abhishek Kumar,Pramod Singh Rathore,Dac-Nhuong Le 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.