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Medical Device Data And Modeling For Clinical Decision Making
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Book Synopsis Medical Device Data and Modeling for Clinical Decision Making by : John R. Zaleski
Download or read book Medical Device Data and Modeling for Clinical Decision Making written by John R. Zaleski and published by Artech House. This book was released on 2011 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: This cutting-edge volume is the first book that provides you with practical guidance on the use of medical device data for bioinformatics modeling purposes. You learn how to develop original methods for communicating with medical devices within healthcare enterprises and assisting with bedside clinical decision making. The book guides in the implementation and use of clinical decision support methods within the context of electronic health records in the hospital environment.This highly valuable reference also teaches budding biomedical engineers and bioinformaticists the practical benefits of using medical device data. Supported with over 100 illustrations, this all-in-one resource discusses key concepts in detail and then presents clear implementation examples to give you a complete understanding of how to use this knowledge in the field.
Book Synopsis Medical Device Data and Modeling for Clinical Decision Making by : John Zaleski
Download or read book Medical Device Data and Modeling for Clinical Decision Making written by John Zaleski and published by . This book was released on 2011 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: This cutting-edge volume is the first book that provides you with practical guidance on the use of medical device data for bioinformatics modeling purposes. You learn how to develop original methods for communicating with medical devices within healthcare enterprises and assisting with bedside clinical decision making. The book guides in the implementation and use of clinical decision support methods within the context of electronic health records in the hospital environment. This highly valuable reference also teaches budding biomedical engineers and bioinformaticists the practical benefits of using medical device data. Supported with over 100 illustrations, this all-in-one resource discusses key concepts in detail and then presents clear implementation examples to give you a complete understanding of how to use this knowledge in the field.
Book Synopsis Fundamentals of Clinical Data Science by : Pieter Kubben
Download or read book Fundamentals of Clinical Data Science written by Pieter Kubben and published by Springer. This book was released on 2018-12-21 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Author :Agency for Healthcare Research and Quality/AHRQ Publisher :Government Printing Office ISBN 13 :1587634333 Total Pages :385 pages Book Rating :4.5/5 (876 download)
Book Synopsis Registries for Evaluating Patient Outcomes by : Agency for Healthcare Research and Quality/AHRQ
Download or read book Registries for Evaluating Patient Outcomes written by Agency for Healthcare Research and Quality/AHRQ and published by Government Printing Office. This book was released on 2014-04-01 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.
Book Synopsis Connected Medical Devices by : John Zaleski
Download or read book Connected Medical Devices written by John Zaleski and published by CRC Press. This book was released on 2015-03-27 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores how medical device integration (MDI) supports quality patient care and better clinical outcomes by reducing clinical documentation transcription errors, improving data accuracy and density within clinical records and ensuring the complete capture of medical device information on patients. It begins with a comprehensive overview of the types of medical devices in use and the ways in which those devices interact, then examines factors such as interoperability standards, patient identification, clinical alerts and regulatory and security considerations.
Book Synopsis Clinical Surveillance by : John R. Zaleski
Download or read book Clinical Surveillance written by John R. Zaleski and published by CRC Press. This book was released on 2020-11-05 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: For more than a decade, the focus of information technology has been on capturing and sharing data from a patient within an all-encompassing record (a.k.a. the electronic health record, EHR), to promote improved longitudinal oversight in the care of the patient. There are both those who agree and those who disagree as to whether this goal has been met, but it is certainly evolving. A key element to improved patient care has been the automated capture of data from durable medical devices that are the source of (mostly) objective data, from imagery to time-series histories of vital signs and spot-assessments of patients. The capture and use of these data to support clinical workflows have been written about and thoroughly debated. Yet, the use of these data for clinical guidance has been the subject of various papers published in respected medical journals, but without a coherent focus on the general subject of the clinically actionable benefits of objective medical device data for clinical decision-making purposes. Hence, the uniqueness of this book is in providing a single point-of-capture for the targeted clinical benefits of medical device data--both electronic- health-record-based and real-time--for improved clinical decision-making at the point of care, and for the use of these data to address and assess specific types of clinical surveillance. Clinical Surveillance: The Actionable Benefits of Objective Medical Device Data for Crucial Decision-Making focuses on the use of objective, continuously collected medical device data for the purpose of identifying patient deterioration, with a primary focus on those data normally obtained from both the higher-acuity care settings in intensive care units and the lower-acuity settings of general care wards. It includes examples of conditions that demonstrate earlier signs of deterioration including systemic inflammatory response syndrome, opioid-induced respiratory depression, shock induced by systemic failure, and more. The book provides education on how to use these data, such as for clinical interventions, in order to identify examples of how to guide care using automated durable medical device data from higher- and lower-acuity care settings. The book also includes real-world examples of applications that are of high value to clinical end-users and health systems.
Book Synopsis Modern Methods of Clinical Investigation by : Institute of Medicine
Download or read book Modern Methods of Clinical Investigation written by Institute of Medicine and published by National Academies Press. This book was released on 1990-02-01 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: The very rapid pace of advances in biomedical research promises us a wide range of new drugs, medical devices, and clinical procedures. The extent to which these discoveries will benefit the public, however, depends in large part on the methods we choose for developing and testing them. Modern Methods of Clinical Investigation focuses on strategies for clinical evaluation and their role in uncovering the actual benefits and risks of medical innovation. Essays explore differences in our current systems for evaluating drugs, medical devices, and clinical procedures; health insurance databases as a tool for assessing treatment outcomes; the role of the medical profession, the Food and Drug Administration, and industry in stimulating the use of evaluative methods; and more. This book will be of special interest to policymakers, regulators, executives in the medical industry, clinical researchers, and physicians.
Book Synopsis Sharing Clinical Trial Data by : Institute of Medicine
Download or read book Sharing Clinical Trial Data written by Institute of Medicine and published by National Academies Press. This book was released on 2015-04-20 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients.
Book Synopsis Machine Learning and AI for Healthcare by : Arjun Panesar
Download or read book Machine Learning and AI for Healthcare written by Arjun Panesar and published by Apress. This book was released on 2019-02-04 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.
Book Synopsis A Systematic Survey of Computer-Aided Diagnosis in Medicine: Past and Present Developments by : Juri Yanase
Download or read book A Systematic Survey of Computer-Aided Diagnosis in Medicine: Past and Present Developments written by Juri Yanase and published by Infinite Study. This book was released on with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diagnostic decision-making process of medical experts, they can be considered as expert systems in medicine.
Book Synopsis Clinical Prediction Models by : Ewout W. Steyerberg
Download or read book Clinical Prediction Models written by Ewout W. Steyerberg and published by Springer. This book was released on 2019-07-22 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies
Book Synopsis Clinical Evaluation of Medical Devices by : Karen M. Becker
Download or read book Clinical Evaluation of Medical Devices written by Karen M. Becker and published by Springer Science & Business Media. This book was released on 2007-11-05 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: The original edition of this text, Clinical Evaluation of Medical Devices: Principles and Case Studies, provided the first overview of key pr- ciples and approaches to medical device clinical trials, illustrated with a series of detailed, real-world case studies. The book is designed as a resource for clinical professionals and regulatory specialists working in the field of new medical device development and marketing. Since the first edition of this text was published in 1997, the rapid pace of inno- tion in health care technologies continues to yield exciting and important new products. The regulatory landscape has also evolved, reflecting some of the changes and needs within the medical device industry. The purpose of Clinical Evaluation of Medical Devices: Principles and Case Studies, Second Edition is to provide an updated and expanded presentation of the scientific methods and regulatory requirements applied to the study of new significant risk medical devices. The text now includes (1) new information on the requirements and process for gaining reimbursement of new products from Medicare and private insurers, with case studies of research specifically designed for this p- pose as well as health care technology assessment methods; (2) infor- tion on new statistical methodologies applied to medical device trials; and (3) all new case studies, including examples of combination pr- ucts, three-phase development models (i. e. , feasibility, FDA approval, Medicare reimbursement), and novel study designs.
Book Synopsis Data Modelling and Analytics for the Internet of Medical Things by : Rajiv Pandey
Download or read book Data Modelling and Analytics for the Internet of Medical Things written by Rajiv Pandey and published by CRC Press. This book was released on 2023-12-22 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: The emergence of the Internet of Medical Things (IoMT) is transforming the management of diseases, improving diseases diagnosis and treatment methods, and reducing healthcare costs and errors. This book covers all the essential aspects of IoMT in one place, providing readers with a comprehensive grasp of IoMT and related technologies. Data Modelling and Analytics for the Internet of Medical Things integrates the architectural, conceptual, and technological aspects of IoMT, discussing in detail the IoMT, connected smart medical devices, and their applications to improve health outcomes. It explores various methodologies and solutions for medical data analytics in healthcare systems using machine learning and deep learning approaches, as well as exploring how technologies such as blockchain and cloud computing can further enhance data analytics in the e-health domain. Prevalent IoMT case studies and applications are also discussed. This book is suitable for scientists, design engineers, system integrators, and researchers in the field of IoMT. It will also be of interest to postgraduate students in computer science focusing on healthcare applications and a supplementary reading for IoMT courses.
Book Synopsis Secondary Analysis of Electronic Health Records by : MIT Critical Data
Download or read book Secondary Analysis of Electronic Health Records written by MIT Critical Data and published by Springer. This book was released on 2016-09-09 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.
Book Synopsis Advancing Temporal Modeling and Heterogeneous Data Analysis for Digital Health by : Yiwen Meng
Download or read book Advancing Temporal Modeling and Heterogeneous Data Analysis for Digital Health written by Yiwen Meng and published by . This book was released on 2020 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent development in electronic medical devices or systems has realized the effective collection and documentation of patients' health in real time. To date, the potential clinical impact of this healthcare data has not been fully realized. Specifically, patients' health data is heterogenous and sparse in nature, as it is composed of various modalities and is collected on different scales. In addition, processing this data efficiently in a temporal manner to take advantage of its sequential structure remains a barrier for medical records. This dissertation attempts to overcome these challenges by developing machine learning models to classify patient reported outcome (PRO) scores from activity tracker data and predict depression diagnoses based on data from patients' historical electronic health records (EHR). A temporal model based on hidden Markov models (HMM) is first proposed to classify PRO scores in various categories from human vital signs collected from Fitbit activity trackers. This approach is able to combine various vital signs on difference scales in a single model that tracks changes in PRO scores over time. Second, several end-to-end machine learning models were built to aggregate multimodal EHR data in a single model. A novel hierarchical embedding method achieved superior performance for predicting depression diagnosis, which lays a foundation for addressing the heterogeneity and sparsity of EHR data. Third, an innovative bidirectional sequence learning model with a transformer architecture was developed for representation learning on high dimensional EHR data, demonstrating significantly improved performance over the traditional forward-only method. Finally, methods to improve the interpretability of the aforementioned models have been developed, which is a critical step before clinical deployment. Relative feature importance factors are determined for each vital sign collected from the Fitbit and attention weights are found for each data modality in the sequential EHR data. Extensive experiments and results have demonstrated the effectiveness of these proposed methods. This dissertation provides methodologies that advance modeling and understanding of digital health datasets, which lays the foundation to construct clinical decision support systems in this domain which could potentially lead to early disease detection and intervention.
Author :Vincent Francois-Lavet Publisher :Foundations and Trends (R) in Machine Learning ISBN 13 :9781680835380 Total Pages :156 pages Book Rating :4.8/5 (353 download)
Book Synopsis An Introduction to Deep Reinforcement Learning by : Vincent Francois-Lavet
Download or read book An Introduction to Deep Reinforcement Learning written by Vincent Francois-Lavet and published by Foundations and Trends (R) in Machine Learning. This book was released on 2018-12-20 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. Written by recognized experts, this book is an important introduction to Deep Reinforcement Learning for practitioners, researchers and students alike.
Book Synopsis Trends and Innovations in Information Systems and Technologies by : Álvaro Rocha
Download or read book Trends and Innovations in Information Systems and Technologies written by Álvaro Rocha and published by Springer Nature. This book was released on 2020-05-17 with total page 619 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected papers presented at the 2020 World Conference on Information Systems and Technologies (WorldCIST’20), held in Budva, Montenegro, from April 7 to 10, 2020. WorldCIST provides a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences with and challenges regarding various aspects of modern information systems and technologies. The main topics covered are A) Information and Knowledge Management; B) Organizational Models and Information Systems; C) Software and Systems Modeling; D) Software Systems, Architectures, Applications and Tools; E) Multimedia Systems and Applications; F) Computer Networks, Mobility and Pervasive Systems; G) Intelligent and Decision Support Systems; H) Big Data Analytics and Applications; I) Human–Computer Interaction; J) Ethics, Computers & Security; K) Health Informatics; L) Information Technologies in Education; M) Information Technologies in Radiocommunications; and N) Technologies for Biomedical Applications.
