Data Analytics in Biomedical Engineering and Healthcare

Data Analytics in Biomedical Engineering and Healthcare
Author: Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar
Publsiher: Academic Press
Total Pages: 292
Release: 2020-10-18
ISBN 10: 0128193158
ISBN 13: 9780128193150
Language: EN, FR, DE, ES & NL

Data Analytics in Biomedical Engineering and Healthcare Book Review:

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks

Handbook of Data Science Approaches for Biomedical Engineering

Handbook of Data Science Approaches for Biomedical Engineering
Author: Valentina Emilia Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari
Publsiher: Academic Press
Total Pages: 318
Release: 2019-11-13
ISBN 10: 0128183195
ISBN 13: 9780128183199
Language: EN, FR, DE, ES & NL

Handbook of Data Science Approaches for Biomedical Engineering Book Review:

Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare
Author: Janmenjoy Nayak,Bighnaraj Naik,Danilo Pelusi,Asit Kumar Das
Publsiher: Academic Press
Total Pages: 396
Release: 2021-04-08
ISBN 10: 0128222611
ISBN 13: 9780128222614
Language: EN, FR, DE, ES & NL

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare Book Review:

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques. Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/unstructured data on experimental analysis. Presents a comprehensive handbook that covers an Introduction to Computational Intelligence in Biomedical Engineering and Healthcare, Computational Intelligence Techniques, and Advanced and Emerging Techniques in Computational Intelligence Helps readers analyze and do advanced research in specialty healthcare applications Includes links to websites, videos, articles and other online content to expand and support primary learning objectives

Computational Intelligence and Data Sciences

Computational Intelligence and Data Sciences
Author: Taylor & Francis Group
Publsiher: CRC Press
Total Pages: 272
Release: 2022-02-23
ISBN 10: 9781032123134
ISBN 13: 1032123133
Language: EN, FR, DE, ES & NL

Computational Intelligence and Data Sciences Book Review:

This book presents futuristic trends in computational intelligence including algorithms used in different application domains in health informatics covering bio-medical, bioinformatics, &biological sciences. It provides conceptual framework with a focus on computational intelligence techniques in biomedical engineering &health informatics.

Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics

Demystifying Big Data  Machine Learning  and Deep Learning for Healthcare Analytics
Author: Pradeep N,Sandeep Kautish,Sheng-Lung Peng
Publsiher: Academic Press
Total Pages: 372
Release: 2021-06-25
ISBN 10: 0128220449
ISBN 13: 9780128220443
Language: EN, FR, DE, ES & NL

Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics Book Review:

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

Healthcare Data Analytics

Healthcare Data Analytics
Author: Chandan K. Reddy,Charu C. Aggarwal
Publsiher: CRC Press
Total Pages: 760
Release: 2015-06-23
ISBN 10: 148223212X
ISBN 13: 9781482232127
Language: EN, FR, DE, ES & NL

Healthcare Data Analytics Book Review:

At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available to solve healthcare problems. The book details novel techniques for acquiring, handling, retrieving, and making best use of healthcare data. It analyzes recent developments in healthcare computing and discusses emerging technologies that can help improve the health and well-being of patients. Written by prominent researchers and experts working in the healthcare domain, the book sheds light on many of the computational challenges in the field of medical informatics. Each chapter in the book is structured as a "survey-style" article discussing the prominent research issues and the advances made on that research topic. The book is divided into three major categories: Healthcare Data Sources and Basic Analytics - details the various healthcare data sources and analytical techniques used in the processing and analysis of such data Advanced Data Analytics for Healthcare - covers advanced analytical methods, including clinical prediction models, temporal pattern mining methods, and visual analytics Applications and Practical Systems for Healthcare - covers the applications of data analytics to pervasive healthcare, fraud detection, and drug discovery along with systems for medical imaging and decision support Computer scientists are usually not trained in domain-specific medical concepts, whereas medical practitioners and researchers have limited exposure to the data analytics area. The contents of this book will help to bring together these diverse communities by carefully and comprehensively discussing the most relevant contributions from each domain.

Strategies in Biomedical Data Science

Strategies in Biomedical Data Science
Author: Jay A. Etchings
Publsiher: John Wiley & Sons
Total Pages: 464
Release: 2017-01-03
ISBN 10: 1119256186
ISBN 13: 9781119256182
Language: EN, FR, DE, ES & NL

Strategies in Biomedical Data Science Book Review:

An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.

Internet of Things in Biomedical Engineering

Internet of Things in Biomedical Engineering
Author: Valentina E. Balas,Le Hoang Son,Sudan Jha,Manju Khari,Raghvendra Kumar
Publsiher: Academic Press
Total Pages: 379
Release: 2019-06-14
ISBN 10: 0128173572
ISBN 13: 9780128173572
Language: EN, FR, DE, ES & NL

Internet of Things in Biomedical Engineering Book Review:

Internet of Things in Biomedical Engineering presents the most current research in Internet of Things (IoT) applications for clinical patient monitoring and treatment. The book takes a systems-level approach for both human-factors and the technical aspects of networking, databases and privacy. Sections delve into the latest advances and cutting-edge technologies, starting with an overview of the Internet of Things and biomedical engineering, as well as a focus on ‘daily life.’ Contributors from various experts then discuss ‘computer assisted anthropology,’ CLOUDFALL, and image guided surgery, as well as bio-informatics and data mining. This comprehensive coverage of the industry and technology is a perfect resource for students and researchers interested in the topic. Presents recent advances in IoT for biomedical engineering, covering biometrics, bioinformatics, artificial intelligence, computer vision and various network applications Discusses big data and data mining in healthcare and other IoT based biomedical data analysis Includes discussions on a variety of IoT applications and medical information systems Includes case studies and applications, as well as examples on how to automate data analysis with Perl R in IoT

Predictive Intelligence in Biomedical and Health Informatics

Predictive Intelligence in Biomedical and Health Informatics
Author: Rajshree Srivastava,Nhu Gia Nguyen,Ashish Khanna,Siddhartha Bhattacharyya
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 180
Release: 2020-10-12
ISBN 10: 3110676125
ISBN 13: 9783110676129
Language: EN, FR, DE, ES & NL

Predictive Intelligence in Biomedical and Health Informatics Book Review:

Predictive Intelligence in Biomedical and Health Informatics focuses on imaging, computer-aided diagnosis and therapy as well as intelligent biomedical image processing and analysis. It develops computational models, methods and tools for biomedical engineering related to computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy and bioinformatics. Large volumes of complex data are often a key feature of biomedical and engineering problems and computational intelligence helps to address such problems. Practical and validated solutions to hard biomedical and engineering problems can be developed by the applications of neural networks, support vector machines, reservoir computing, evolutionary optimization, biosignal processing, pattern recognition methods and other techniques to address complex problems of the real world.

Exploratory Data Analytics for Healthcare

Exploratory Data Analytics for Healthcare
Author: R. Lakshmana Kumar,R. Indrakumari,B. Balamurugan,Achyut Shankar
Publsiher: CRC Press
Total Pages: 312
Release: 2021-12-24
ISBN 10: 1000527018
ISBN 13: 9781000527018
Language: EN, FR, DE, ES & NL

Exploratory Data Analytics for Healthcare Book Review:

Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.

Knowledge Modelling and Big Data Analytics in Healthcare

Knowledge Modelling and Big Data Analytics in Healthcare
Author: Mayuri Mehta,Kalpdrum Passi,Indranath Chatterjee,Rajan Patel
Publsiher: CRC Press
Total Pages: 362
Release: 2021-12-09
ISBN 10: 1000477762
ISBN 13: 9781000477764
Language: EN, FR, DE, ES & NL

Knowledge Modelling and Big Data Analytics in Healthcare Book Review:

Knowledge Modelling and Big Data Analytics in Healthcare: Advances and Applications focuses on automated analytical techniques for healthcare applications used to extract knowledge from a vast amount of data. It brings together a variety of different aspects of the healthcare system and aids in the decision-making processes for healthcare professionals. The editors connect four contemporary areas of research rarely brought together in one book: artificial intelligence, big data analytics, knowledge modelling, and healthcare. They present state-of-the-art research from the healthcare sector, including research on medical imaging, healthcare analysis, and the applications of artificial intelligence in drug discovery. This book is intended for data scientists, academicians, and industry professionals in the healthcare sector.

Deep Learning Techniques for Biomedical and Health Informatics

Deep Learning Techniques for Biomedical and Health Informatics
Author: Sujata Dash,Biswa Ranjan Acharya,Mamta Mittal,Ajith Abraham,Arpad Kelemen
Publsiher: Springer Nature
Total Pages: 383
Release: 2019-11-14
ISBN 10: 3030339661
ISBN 13: 9783030339661
Language: EN, FR, DE, ES & NL

Deep Learning Techniques for Biomedical and Health Informatics Book Review:

This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim of healthcare informatics is to ensure high-quality, efficient health care, and better treatment and quality of life by efficiently analyzing abundant biomedical and healthcare data, including patient data and electronic health records (EHRs), as well as lifestyle problems. In the past, it was common to have a domain expert to develop a model for biomedical or health care applications; however, recent advances in the representation of learning algorithms (deep learning techniques) make it possible to automatically recognize the patterns and represent the given data for the development of such model. This book allows new researchers and practitioners working in the field to quickly understand the best-performing methods. It also enables them to compare different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health. It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in the fields of machine learning, deep learning, biomedical engineering, health informatics, and related fields.

Healthcare Data Analytics and Management

Healthcare Data Analytics and Management
Author: Nilanjan Dey,Amira S. Ashour,Simon James Fong,Chintan Bhatt
Publsiher: Academic Press
Total Pages: 340
Release: 2018-11-15
ISBN 10: 0128156368
ISBN 13: 9780128156360
Language: EN, FR, DE, ES & NL

Healthcare Data Analytics and Management Book Review:

Healthcare Data Analytics and Management help readers disseminate cutting-edge research that delivers insights into the analytic tools, opportunities, novel strategies, techniques and challenges for handling big data, data analytics and management in healthcare. As the rapidly expanding and heterogeneous nature of healthcare data poses challenges for big data analytics, this book targets researchers and bioengineers from areas of machine learning, data mining, data management, and healthcare providers, along with clinical researchers and physicians who are interested in the management and analysis of healthcare data. Covers data analysis, management and security concepts and tools in the healthcare domain Highlights electronic medical health records and patient information records Discusses the different techniques to integrate Big data and Internet-of-Things in healthcare, including machine learning and data mining Includes multidisciplinary contributions in relation to healthcare applications and challenges

Biomedical Information Technology

Biomedical Information Technology
Author: David Dagan Feng
Publsiher: Academic Press
Total Pages: 820
Release: 2019-10-22
ISBN 10: 0128160357
ISBN 13: 9780128160350
Language: EN, FR, DE, ES & NL

Biomedical Information Technology Book Review:

Biomedical Information Technology, Second Edition, contains practical, integrated clinical applications for disease detection, diagnosis, surgery, therapy and biomedical knowledge discovery, including the latest advances in the field, such as biomedical sensors, machine intelligence, artificial intelligence, deep learning in medical imaging, neural networks, natural language processing, large-scale histopathological image analysis, virtual, augmented and mixed reality, neural interfaces, and data analytics and behavioral informatics in modern medicine. The enormous growth in the field of biotechnology necessitates the utilization of information technology for the management, flow and organization of data. All biomedical professionals can benefit from a greater understanding of how data can be efficiently managed and utilized through data compression, modeling, processing, registration, visualization, communication and large-scale biological computing. Presents the world's most recognized authorities who give their "best practices" Provides professionals with the most up-to-date and mission critical tools to evaluate the latest advances in the field Gives new staff the technological fundamentals and updates experienced professionals with the latest practical integrated clinical applications

Leveraging Biomedical and Healthcare Data

Leveraging Biomedical and Healthcare Data
Author: Firas Kobeissy,Kevin Wang,Fadi A. Zaraket,Ali Alawieh
Publsiher: Academic Press
Total Pages: 225
Release: 2018-11-23
ISBN 10: 012809561X
ISBN 13: 9780128095614
Language: EN, FR, DE, ES & NL

Leveraging Biomedical and Healthcare Data Book Review:

Leveraging Biomedical and Healthcare Data: Semantics, Analytics and Knowledge provides an overview of the approaches used in semantic systems biology, introduces novel areas of its application, and describes step-wise protocols for transforming heterogeneous data into useful knowledge that can influence healthcare and biomedical research. Given the astronomical increase in the number of published reports, papers, and datasets over the last few decades, the ability to curate this data has become a new field of biomedical and healthcare research. This book discusses big data text-based mining to better understand the molecular architecture of diseases and to guide health care decision. It will be a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in understanding more about how to process and apply great amounts of data to improve their research. Includes at each section resource pages containing a list of available curated raw and processed data that can be used by researchers in the field Provides demonstrative and relevant examples that serve as a general tutorial Presents a list of algorithm names and computational tools available for basic and clinical researchers

Machine Learning Used in Biomedical Computing and Intelligence Healthcare Volume I

Machine Learning Used in Biomedical Computing and Intelligence Healthcare  Volume I
Author: Honghao Gao,Ying Li,Zijian Zhang,Wenbing Zhao
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2021-06-17
ISBN 10: 2889669327
ISBN 13: 9782889669325
Language: EN, FR, DE, ES & NL

Machine Learning Used in Biomedical Computing and Intelligence Healthcare Volume I Book Review:

Big Data Analytics in Bioinformatics and Healthcare

Big Data Analytics in Bioinformatics and Healthcare
Author: Wang, Baoying
Publsiher: IGI Global
Total Pages: 528
Release: 2014-10-31
ISBN 10: 1466666129
ISBN 13: 9781466666122
Language: EN, FR, DE, ES & NL

Big Data Analytics in Bioinformatics and Healthcare Book Review:

As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.

Data Analytics in Medicine Concepts Methodologies Tools and Applications

Data Analytics in Medicine  Concepts  Methodologies  Tools  and Applications
Author: Management Association, Information Resources
Publsiher: IGI Global
Total Pages: 2071
Release: 2019-12-06
ISBN 10: 1799812057
ISBN 13: 9781799812050
Language: EN, FR, DE, ES & NL

Data Analytics in Medicine Concepts Methodologies Tools and Applications Book Review:

Advancements in data science have created opportunities to sort, manage, and analyze large amounts of data more effectively and efficiently. Applying these new technologies to the healthcare industry, which has vast quantities of patient and medical data and is increasingly becoming more data-reliant, is crucial for refining medical practices and patient care. Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications is a vital reference source that examines practical applications of healthcare analytics for improved patient care, resource allocation, and medical performance, as well as for diagnosing, predicting, and identifying at-risk populations. Highlighting a range of topics such as data security and privacy, health informatics, and predictive analytics, this multi-volume book is ideally designed for doctors, hospital administrators, nurses, medical professionals, IT specialists, computer engineers, information technologists, biomedical engineers, data-processing specialists, healthcare practitioners, academicians, and researchers interested in current research on the connections between data analytics in the field of medicine.

Medical Big Data and Internet of Medical Things

Medical Big Data and Internet of Medical Things
Author: Aboul Ella Hassanien,Nilanjan Dey,Surekha Borra
Publsiher: CRC Press
Total Pages: 340
Release: 2018-10-25
ISBN 10: 1351030361
ISBN 13: 9781351030366
Language: EN, FR, DE, ES & NL

Medical Big Data and Internet of Medical Things Book Review:

Big data and the Internet of Things (IoT) play a vital role in prediction systems used in biological and medical applications, particularly for resolving issues related to disease biology at different scales. Modelling and integrating medical big data with the IoT helps in building effective prediction systems for automatic recommendations of diagnosis and treatment. The ability to mine, process, analyse, characterize, classify and cluster a variety and wide volume of medical data is a challenging task. There is a great demand for the design and development of methods dealing with capturing and automatically analysing medical data from imaging systems and IoT sensors. Addressing analytical and legal issues, and research on integration of big data analytics with respect to clinical practice and clinical utility, architectures and clustering techniques for IoT data processing, effective frameworks for removal of misclassified instances, practicality of big data analytics, methodological and technical issues, potential of Hadoop in managing healthcare data is the need of the hour. This book integrates different aspects used in the field of healthcare such as big data, IoT, soft computing, machine learning, augmented reality, organs on chip, personalized drugs, implantable electronics, integration of bio-interfaces, and wearable sensors, devices, practical body area network (BAN) and architectures of web systems. Key Features: Addresses various applications of Medical Big Data and Internet of Medical Things in real time environment Highlights recent innovations, designs, developments and topics of interest in machine learning techniques for classification of medical data Provides background and solutions to existing challenges in Medical Big Data and Internet of Medical Things Provides optimization techniques and programming models to parallelize the computationally intensive tasks in data mining of medical data Discusses interactions, advantages, limitations, challenges and future perspectives of IoT based remote healthcare monitoring systems. Includes data privacy and security analysis of cryptography methods for the Web of Medical Things (WoMT) Presents case studies on the next generation medical chair, electronic nose and pill cam are also presented.

Smart Computational Intelligence in Biomedical and Health Informatics

Smart Computational Intelligence in Biomedical and Health Informatics
Author: Amit Kumar Manocha,Mandeep Singh,Shruti Jain,Vishal Jain
Publsiher: CRC Press
Total Pages: 202
Release: 2021-09-27
ISBN 10: 1000434370
ISBN 13: 9781000434378
Language: EN, FR, DE, ES & NL

Smart Computational Intelligence in Biomedical and Health Informatics Book Review:

Smart Computational Intelligence in Biomedical and Health Informatics presents state-of-the-art innovations; research, design, and implementation of methodological and algorithmic solutions to data processing problems, including analysis of evolving trends in health informatics and computer-aided diagnosis. This book describes practical, applications-led research regarding the use of methods and devices in clinical diagnosis, disease prevention, and patient monitoring and management. It also covers simulation and modeling, measurement and control, analysis, information extraction and monitoring of physiological data in clinical medicine and the biological sciences. FEATURES Covers evolutionary approaches to solve optimization problems in biomedical engineering Discusses IoT, Cloud computing, and data analytics in healthcare informatics Provides computational intelligence-based solution for diagnosis of diseases Reviews modelling and simulations in designing of biomedical equipment Promotes machine learning-based approaches to improvements in biomedical engineering problems This book is for researchers, graduate students in healthcare, biomedical engineers, and those interested in health informatics, computational intelligence, and machine learning.