Data Analytics In Biomedical Engineering And Healthcare, Lee, Kun Chang
Автор: Olinda Timms Название: Biomedical Ethics. 2 ed ISBN: 813125965X ISBN-13(EAN): 9788131259658 Издательство: Elsevier India Рейтинг: Цена: 838.00 р. Наличие на складе: Есть (2 шт.) Описание: Each chapter focuses on a single area in a simple narrative. Illustrative case reports and case studies of ethical dilemmas are provided with points for reflection/discussion. In step with the curriculum in Medical Ethics already established in several medical colleges.
The chapters can be used to develop modules in a medical ethics program. Additional resources (titles of relevant films, readings, and references) are provided. The chapters have been linked to the AETCOM modules for easy reference, providing content for teaching modules.
This book provides the resource to create teaching modules in medical ethics. In this way, the book compliments the AETCOM modules and can be used to develop teaching-learning sessions.?
Описание: Features statistical and operational research methods and tools being used to improve the healthcare industry With a focus on cutting–edge approaches to the quickly growing field of healthcare, Healthcare Analytics: From Data to Knowledge to Healthcare Improvement provides an integrated and comprehensive treatment on recent research advancements in data–driven healthcare analytics in an effort to provide more personalized and efficient healthcare services. Emphasizing data and healthcare analytics from an operational management and statistical perspective, the book details how analytical methods and tools can be utilized to enhance care quality and operational efficiency. Organized into two main sections, Part One features biomedical and health informatics and specifically addresses the analytics of genomic and proteomic data; physiological signals from patient monitoring systems; data uncertainty in clinical laboratory tests; predictive modeling; disease modeling for sepsis; and the design of cyber infrastructures for early prediction of epidemic events. Part Two focuses on healthcare delivery systems, including system advances for transforming clinic workflow and patient care; macro analysis of patient flow distribution; intensive care units; primary care; demand and resource allocation; mathematical models for predicting patient readmission and postoperative outcome; physician–patient interactions; insurance claims; and the role of social media in healthcare. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement also features: Contributions from well–known international experts who shed light on new approaches in this growing area Discussions on contemporary methods and techniques to address the handling of rich and large–scale healthcare data as well as the overall optimization of healthcare system operations Numerous real–world examples and case studies that emphasize the vast potential of statistical and operational research tools and techniques to address the big data environment within the healthcare industry Plentiful applications that showcase the various analytical methods and tools that can be applied to successful predictive modeling The book is an ideal reference for academics and practitioners in operations research, management science, applied mathematics, statistics, business, industrial and systems engineering, healthcare systems, and economics. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement is also appropriate for graduate–level courses typically offered within operations research, industrial engineering, business, and public health departments. Hui Yang, PhD, is Associate Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University. His research interests include sensor–based modeling and analysis of complex systems for process monitoring/control; system diagnostics/prognostics; quality improvement; and performance optimization with special focus on nonlinear stochastic dynamics and the resulting chaotic, recurrence, self–organizing behaviors. Eva K. Lee, PhD, is Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology, Director of the Center for Operations Research in Medicine and HealthCare, and Distinguished Scholar in Health System, Health Systems Institute at both Emory University School of Medicine and Georgia Institute of Technology. Her research interests include health risk prediction; early disease prediction and diagnosis; optimal treatment strategies and drug delivery; healthcare outcome analysis and treatment prediction; public health and medical preparedness; large–scale healthcare/medical decision analysis and quality improvement; clinical translational science; and business intelligence and organization transformation.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Автор: Wang, Kevin Название: Leveraging Biomedical and Healthcare Data ISBN: 0128095563 ISBN-13(EAN): 9780128095560 Издательство: Elsevier Science Рейтинг: Цена: 15487.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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
Автор: Wang, Li & Perrizo Название: Big Data Analytics In Bioinformatics And Healthcare ISBN: 1466666110 ISBN-13(EAN): 9781466666115 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 37145.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Vijay Gadepally; Timothy Mattson; Michael Stonebra Название: Heterogeneous Data Management, Polystores, and Analytics for Healthcare ISBN: 3030141764 ISBN-13(EAN): 9783030141769 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Improved BigDAWG Architecture.- Multi-Model Database Management Systems - a Look Forward.- Progressive Interactions Between Data Sources.- TDM: A Tensor Data Model for Logical Data Independence in Polystore Systems.- Sketching data structures for massive graph problems.- Managing Structurally Heterogeneous Databases in Software Product Lines.- PDSPTF: Polystore Database System for Scalability and Access to PTF Time-domain Astronomy Data Archives.- API Federation in the BigDAWG Polystore.- Augmented Therapy with Online Support Groups.- RHCS - A Clinical Recommendation System for Geriatric Patients.- Implementation of a Medical Coding Support System by Combining Approaches: NLP and Machine Learning.- Building a Research-Quality Copy Number Variation Data Repository for Translational Research.- Data-Driven Genomic Computing: Making Sense of the Signals from the Genome.- DEAME - Differential Expression Analysis Made Easy.
Автор: Vijay Gadepally; Timothy Mattson; Michael Stonebra Название: Heterogeneous Data Management, Polystores, and Analytics for Healthcare ISBN: 3030337510 ISBN-13(EAN): 9783030337513 Издательство: Springer Рейтинг: Цена: 8104.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed post-conference proceedings for the VLBD conference workshops entitled: Towards Polystores That Manage Multiple Databases, Privacy, Security and/or Policy Issues for Heterogenous Data (Poly 2019) and the Fifth International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH 2019), held in Los Angeles, CA, USA, in August 2019, in conjunction with the 45th International Conference on Very Large Data Bases, VLDB 2019. The 20 regular papers presented together with 2 keynote papers were carefully reviewed and selected from 31 initial submissions. The papers are organized in topical sections named:Poly 2019: Privacy, Security and/or Policy Issues for Heterogenous Data; Building Polystore Systems.DMAH 2019: Database Enabled Biomedical Research; AI for Healthcare; Knowledge Discovery from Unstructured Biomedical Data; Blockchain and Privacy Preserving Data Management.
Автор: Nagarajan, Radhakrishnan Название: Big Data Healthcare Analytics ISBN: 1138575801 ISBN-13(EAN): 9781138575806 Издательство: Taylor&Francis Рейтинг: Цена: 9492.00 р. Наличие на складе: Нет в наличии.
Описание: There has been a recent explosion and continued growth of digital data across a spectrum of areas. Data explosion is especially true in healthcare with increasing adoption of electronic health records as well as growth in disparate data sources (claims data, demographic data, registries) as well as data from other sources (e.g. imaging data from PACS, text data from clinical notes, molecular/genetic data). Data explosion is also accompanied by unprecedented access to healthcare data by providers as well as patients. This in turn has facilitated evidence-based or data-driven approaches where data from the patient(s) pas can be used to guide treatment decisions in targeted and timely manner. While significant progress is underway with regards to storage and retrieval of these Big Data, there is increasing emphasis on analytics that include querying and deciphering actionable knowledge from these large data sources that can assist in knowledge discovery and decision making. In stark contrast to traditional hypothesis testing, Big Data healthcare analytics is expected to facilitate discovery as well as hypothesis generation with significant impact of contemporary areas such as precision medicine, clinical decision support and learning healthcare system. The focus of this book is availing benchmark open-source distributed and programming environments for big data healthcare analytics. Open-source implementation is especially helpful in facilitating enhance transparency and reproducibility of the material presented. Subscribing to open-source can also be attributed to the increasing emphasis on open science within the big data realm and the authors background with open-source environment. While the present book introduces the necessary material about distributing computing environment and open-source programming environments, a key and major part of the book is devoted to the implementation of routinely used approaches to answer questions of interest
Автор: Singh, Sanjay Kumar Название: Iot Based Data Analytics For The Healthcare Industry ISBN: 0128214724 ISBN-13(EAN): 9780128214725 Издательство: Elsevier Science Рейтинг: Цена: 16505.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
IoT Based Data Analytics for the Healthcare Industry: Techniques and Applications explores recent advances in the analysis of healthcare industry data through IoT data analytics. The book covers the analysis of ubiquitous data generated by the healthcare industry, from a wide range of sources, including patients, doctors, hospitals, and health insurance companies. The book provides AI solutions and support for healthcare industry end-users who need to analyze and manipulate this vast amount of data. These solutions feature deep learning and a wide range of intelligent methods, including simulated annealing, tabu search, genetic algorithm, ant colony optimization, and particle swarm optimization.
The book also explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages, challenges and issues in data collection, data handling, and data collection set-up. Healthcare industry data or streaming data generated by ubiquitous sensors cocooned into the IoT requires advanced analytics to transform data into information. With advances in computing power, communications, and techniques for data acquisition, the need for advanced data analytics is in high demand.
Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained.
Автор: Edmon Begoli; Fusheng Wang; Gang Luo Название: Data Management and Analytics for Medicine and Healthcare ISBN: 3319671855 ISBN-13(EAN): 9783319671857 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the thoroughly refereed conference proceedings of the Third International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2017, in Munich, Germany, in September 2017, held in conjunction with the 43rd International Conference on Very Large Data Bases, VLDB 2017.
Автор: Alfano, R. R. Название: Neurophotonics and Biomedical Spectroscopy ISBN: 0323480675 ISBN-13(EAN): 9780323480673 Издательство: Elsevier Science Рейтинг: Цена: 26107.00 р. Наличие на складе: Нет в наличии.
Описание:
Neurophotonics and Biomedical Spectroscopy addresses the novel state-of-the-art work in non-invasive optical spectroscopic methods that detect the onset and progression of diseases and other conditions, including pre-malignancy, cancer, Alzheimer's disease, tissue and cell response to therapeutic intervention, unintended injury and laser energy deposition. The book then highlights research in neurophotonics that investigates single and multi-photon excitation optical signatures of normal/diseased nerve tissues and in the brain, providing a better understanding of the underlying biochemical and structural changes of tissues and cells that are responsible for the observed spectroscopic signatures.
Topics cover a wide array of well-established UV, visible, NIR and IR optical and spectroscopic techniques and novel approaches to diagnose tissue changes, including: label free in vivo and ex vivo fluorescence spectroscopy, Stoke shift spectroscopy, spectral imaging, Resonance Raman spectroscopy, multiphoton two Photon excitation, and more.
Provides an overview of the spectroscopic properties of tissue and tissue-light interaction, describing techniques to exploit these properties in imaging
Explores the potential and significance of molecule-specific imaging and its capacity to reveal vital new information on nanoscale structures
Offers a concise overview of different spectroscopic methods and their potential benefits for solving diagnostic and therapeutic problems
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