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Biomedical Measurement Systems and Data Science by Michael Insana Pdf
Discover the fundamental principles of biomedical measurement design and performance evaluation with this hands-on guide. Whether you develop measurement instruments or use them in novel ways, this practical text will prepare you to be an effective generator and consumer of biomedical data. Designed for both classroom instruction and self-study, it explains how information is encoded into recorded data and can be extracted and displayed in an accessible manner. Describes and integrates experimental design, performance assessment, classification, and system modelling. Combines mathematical concepts with computational models, providing the tools needed to answer advanced biomedical questions. Includes MATLAB® scripts throughout to help readers model all types of biomedical systems, and contains numerous homework problems, with a solutions manual available online. This is an essential text for advanced undergraduate and graduate students in bioengineering, electrical and computer engineering, computer science, medical physics, and anyone preparing for a career in biomedical sciences and engineering.
Handbook of Data Science Approaches for Biomedical Engineering by Valentina Emilia Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari Pdf
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
Introduction to Biomedical Data Science by Robert Hoyt,Robert Muenchen Pdf
Overview of biomedical data science -- Spreadsheet tools and tips -- Biostatistics primer -- Data visualization -- Introduction to databases -- Big data -- Bioinformatics and precision medicine -- Programming languages for data analysis -- Machine learning -- Artificial intelligence -- Biomedical data science resources -- Appendix A: Glossary -- Appendix B: Using data.world -- Appendix C: Chapter exercises.
Transforming Ergonomics with Personalized Health and Intelligent Workplaces by M. Vega-Barbas,F. Seoane Pdf
Life expectancy is increasing, and we are all expected to work for longer as a result. A balance must be found between the demands of work and human capabilities, and this makes the prevention of workplace-related health problems more important than ever. Emerging technologies, such as smart textiles, wearable devices, and the Internet of Things have enabled the development of intelligent biomedical clothing and the integration of pervasive sensitive services into the environment, and together with ambient intelligence technology techniques and big data analytics, have fostered a proliferation of p-Health monitoring solutions. This book presents a collection of the most significant challenges and advances in the field of intelligent workspaces and personalized ergonomics, bringing together the most relevant results of various international research projects. The book is organized into three main sections: Personalized Ergonomics, which explores the need for practical and reliable risk assessment methods for the prevention of musculoskeletal disorders and the enhancement of the workplace; Pervasive Technology for Intelligent Workplaces, which identifies the opportunities and challenges of technology-based interventions and the security and privacy issues of the smart workplace; and Data Warehouse Governance and Analytics. The book concludes with a chapter on lessons learnt. The transformation of the working environment into a healthy and intelligent space will not only support ergonomists, employees and employers, but may also be the solution to the sustainability of our current social welfare systems, and the book will be of interest to all those concerned with workplace health.
Data Analytics in Biomedical Engineering and Healthcare by Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar Pdf
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
Information Technology in Medical Diagnostics III by Waldemar Wójcik,Saygid Uvaysov,Andrzej Smolarz Pdf
The science of biomedical measurements is experiencing a period of rapid development. Biomedical measuring systems are becoming increasingly accurate on the one hand and complex on the other. In order to make progress in this field, metrological problems must be solved using a systemic and formal approach. To this end, it is necessary to define the components of the system and the rules for their interaction, which allows the creation of a mathematical model. In this way, any technology or object can be presented in the form of a structure on which the necessary estimates can be formulated and synthesis, including metrological one, can be made. The authors have observed that despite the significance of the problem, few scientific centres deal with this issue in a generalised manner. Hence the idea of bringing together the achievements of the centres from Russia, Poland and Kazakhstan in one joint publication. The first and second volumes of Information Technology in Medical Diagnostics found readers not only in Poland, Ukraine, and Kazakhstan but also Spain, Russia and the Czech Republic. Following the readers’ suggestions, in the third volume of ITMD we returned to the formula of closed chapters known from volume one. Due to its limited volume, the book deals with the aforementioned issues in only selected areas of biomedical engineering. The book will be of interest not only for academics and engineers but also for professionals involved in biomedical engineering, seeking solutions for the problems that cannot be solved using "traditional" technologies or trying to improve existing measurement systems.
Advances in Biomedical Sensing, Measurements, Instrumentation and Systems by Aimé Lay-Ekuakille Pdf
Advances in technological devices unveil new architectures for instrumentation and improvements in measurement techniques. Sensing technology, related to biomedical aspects, plays a key role in nowadays applications; it promotes different advantages for: healthcare, solving difficulties for elderly persons, clinical analysis, microbiological characterizations, etc.. This book intends to illustrate and to collect recent advances in biomedical measurements and sensing instrumentation, not as an encyclopedia but as clever support for scientists, students and researchers in other to stimulate exchange and discussions for further developments.
Biomedical Sensors and Measurement by Ping Wang,Qingjun Liu Pdf
"Biomedical Sensors and Measurement" is an interdisciplinary book combining electronics with biology and medicine. It gives an overview of the concept and principle of biomedical sensors and measurement. First, the basic theory and technology are explained, followed by details of the physical sensors, chemical sensors, biosensors and their typical applications in biomedicine. Furthermore, the interface technology of the sensors and the typical measurement systems is presented. The large amount of vivid and specific figures and formulas will help to deepen the understanding of the fundamental and new applications involving biomedical sensors and measurement technology. The book is intended for biomedical engineers, medical physicists and other researchers and professionals in biomedicine-related specialties, especially interdisciplinary studies. Prof. Ping Wang and Dr. Qingjun Liu both work at the Biosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, China.
Data Science and Predictive Analytics by Ivo D. Dinov Pdf
Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall. First, the volume of data is increasing much faster than the corresponding rise of our computational processing power (Kryder’s law > Moore’s law). Second, traditional discipline-bounds inhibit expeditious progress. Third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. There are very few rigorous instructional resources, interactive learning materials, and dynamic training environments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pillars for the urgently needed bridge to close that supply and demand predictive analytic skills gap. Exposing the enormous opportunities presented by the tsunami of Big data, this textbook aims to identify specific knowledge gaps, educational barriers, and workforce readiness deficiencies. Specifically, it focuses on the development of a transdisciplinary curriculum integrating modern computational methods, advanced data science techniques, innovative biomedical applications, and impactful health analytics. The content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. Big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies. The two examples below demonstrate the powerful need for scientific knowledge, computational abilities, interdisciplinary expertise, and modern technologies necessary to achieve desired outcomes (improving human health and optimizing future return on investment). This can only be achieved by appropriately trained teams of researchers who can develop robust decision support systems using modern techniques and effective end-to-end protocols, like the ones described in this textbook. • A geriatric neurologist is examining a patient complaining of gait imbalance and posture instability. To determine if the patient may suffer from Parkinson’s disease, the physician acquires clinical, cognitive, phenotypic, imaging, and genetics data (Big Data). Most clinics and healthcare centers are not equipped with skilled data analytic teams that can wrangle, harmonize and interpret such complex datasets. A learner that completes a course of study using this textbook will have the competency and ability to manage the data, generate a protocol for deriving biomarkers, and provide an actionable decision support system. The results of this protocol will help the physician understand the entire patient dataset and assist in making a holistic evidence-based, data-driven, clinical diagnosis. • To improve the return on investment for their shareholders, a healthcare manufacturer needs to forecast the demand for their product subject to environmental, demographic, economic, and bio-social sentiment data (Big Data). The organization’s data-analytics team is tasked with developing a protocol that identifies, aggregates, harmonizes, models and analyzes these heterogeneous data elements to generate a trend forecast. This system needs to provide an automated, adaptive, scalable, and reliable prediction of the optimal investment, e.g., R&D allocation, that maximizes the company’s bottom line. A reader that complete a course of study using this textbook will be able to ingest the observed structured and unstructured data, mathematically represent the data as a computable object, apply appropriate model-based and model-free prediction techniques. The results of these techniques may be used to forecast the expected relation between the company’s investment, product supply, general demand of healthcare (providers and patients), and estimate the return on initial investments.
Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics by Sunil Kumar Dhal,Subhendu Kumar Pani,Srinivas Prasad,Sudhir Kumar Mohapatra Pdf
BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.
Biomedical Engineering Tools for Management for Patients with COVID-19 by Valentina Emilia Balas,Oana Geman,Guojun Wang,Muhammad Arif,Octavian Adrian Postolache Pdf
Biomedical Engineering Tools for Management of Patients with COVID-19 presents biomedical engineering tools under research (and in development) that can be used for the management of COVID-19 patients, along with BME tools in the global environment that curtail and prevent the spread of the virus. BME tools covered in the book include new disinfectants and sterilization equipment, testing devices for rapid and accurate COVID-19 diagnosis, Internet of Things applications in COVID-19 hospitals, analytics, Data Science and statistical modeling applied to COVID-19 tracking, Smart City instruments and applications, and more. Later sections discuss smart tools in telemedicine and e-health. Biomedical engineering tools can provide engineers, computer scientists, clinicians and other policymakers with solutions for managing patient treatment, applying data analysis techniques, and applying tools to help the general population curtail spread of the virus. Provides leading-edge biomedical engineering tools and techniques for the treatment of patients with the COVID-19 virus Integrates a variety of case studies as a resource for COVID-19 researchers and clinicians around the world, including both positive and negative research findings Provides insights into innovative Biomedical Engineering techniques and devices from COVID-19 researchers around the world
Recent Trends in Computational Sciences by Gururaj H L,Pooja M R,Francesco Flammini Pdf
This book is a compilation of research papers and presentations from the Fourth Annual International Conference on Data Science, Machine Learning and Blockchain Technology (AICDMB 2023, Mysuru, India, 16-17 March 2023). The book covers a wide range of topics, including data mining, natural language processing, deep learning, computer vision, big data analytics, cryptography, smart contracts, decentralized applications, and blockchain-based solutions for various industries such as healthcare, finance, and supply chain management. The research papers presented in this book highlight the latest advancements and practical applications in data science, machine learning, and blockchain technology, and provide insights into the future direction of these fields. The book serves as a valuable resource for researchers, students, and professionals in the areas of data science, machine learning, and blockchain technology.
Data Science for Healthcare by Sergio Consoli,Diego Reforgiato Recupero,Milan Petković Pdf
This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.