Life Cycle Decisions For Biomedical Data
Download Life Cycle Decisions For Biomedical Data full books in PDF, epub, and Kindle. Read online free Life Cycle Decisions For Biomedical Data ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 185 |
| Release | : 2020-10-04 |
| Genre | : Science |
| ISBN | : 0309670039 |
Biomedical research results in the collection and storage of increasingly large and complex data sets. Preserving those data so that they are discoverable, accessible, and interpretable accelerates scientific discovery and improves health outcomes, but requires that researchers, data curators, and data archivists consider the long-term disposition of data and the costs of preserving, archiving, and promoting access to them. Life Cycle Decisions for Biomedical Data examines and assesses approaches and considerations for forecasting costs for preserving, archiving, and promoting access to biomedical research data. This report provides a comprehensive conceptual framework for cost-effective decision making that encourages data accessibility and reuse for researchers, data managers, data archivists, data scientists, and institutions that support platforms that enable biomedical research data preservation, discoverability, and use.
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 185 |
| Release | : 2020-09-04 |
| Genre | : Science |
| ISBN | : 0309670063 |
Biomedical research results in the collection and storage of increasingly large and complex data sets. Preserving those data so that they are discoverable, accessible, and interpretable accelerates scientific discovery and improves health outcomes, but requires that researchers, data curators, and data archivists consider the long-term disposition of data and the costs of preserving, archiving, and promoting access to them. Life Cycle Decisions for Biomedical Data examines and assesses approaches and considerations for forecasting costs for preserving, archiving, and promoting access to biomedical research data. This report provides a comprehensive conceptual framework for cost-effective decision making that encourages data accessibility and reuse for researchers, data managers, data archivists, data scientists, and institutions that support platforms that enable biomedical research data preservation, discoverability, and use.
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 93 |
| Release | : 2020-07-09 |
| Genre | : Computers |
| ISBN | : 0309672759 |
Biomedical research data sets are becoming larger and more complex, and computing capabilities are expanding to enable transformative scientific results. The National Institutes of Health's (NIH's) National Library of Medicine (NLM) has the unique role of ensuring that biomedical research data are findable, accessible, interoperable, and reusable in an ethical manner. Tools that forecast the costs of long-term data preservation could be useful as the cost to curate and manage these data in meaningful ways continues to increase, as could stewardship to assess and maintain data that have future value. The National Academies of Sciences, Engineering, and Medicine convened a workshop on July 11-12, 2019 to gather insight and information in order to develop and demonstrate a framework for forecasting long-term costs for preserving, archiving, and accessing biomedical data. Presenters and attendees discussed tools and practices that NLM could use to help researchers and funders better integrate risk management practices and considerations into data preservation, archiving, and accessing decisions; methods to encourage NIH-funded researchers to consider, update, and track lifetime data; and burdens on the academic researchers and industry staff to implement these tools, methods, and practices. This publication summarizes the presentations and discussion of the workshop.
| Author | : Institute of Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 236 |
| Release | : 2015-04-20 |
| Genre | : Medical |
| ISBN | : 0309316324 |
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.
| Author | : Ibrahim Emam |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
| Author | : G. Schreier |
| Publisher | : IOS Press |
| Total Pages | : 360 |
| Release | : 2018-05-18 |
| Genre | : Medical |
| ISBN | : 1614998582 |
Biomedical engineering and health informatics are closely related to each other, and it is often difficult to tell where one ends and the other begins, but ICT systems in healthcare and biomedical systems and devices are already becoming increasingly interconnected, and share the common entity of data. This is something which is set to become even more prevalent in future, and will complete the chain and flow of information from the sensor, via processing, to the actuator, which may be anyone or anything from a human healthcare professional to a robot. Methods for automating the processing of information, such as signal processing, machine learning, predictive analytics and decision support, are increasingly important for providing actionable information and supporting personalized and preventive healthcare protocols in both biomedical and digital healthcare systems and applications. This book of proceedings presents 50 papers from the 12th eHealth conference, eHealth2018, held in Vienna, Austria, in May 2018. The theme of this year’s conference is Biomedical Meets eHealth – From Sensors to Decisions, and the papers included here cover a wide range of topics from the field of eHealth. The book will be of interest to all those working to design and implement healthcare today.
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 69 |
| Release | : 2018-03-05 |
| Genre | : Education |
| ISBN | : 0309465052 |
The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation's ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses. The field of data science has emerged to address the proliferation of data and the need to manage and understand it. Data science is a hybrid of multiple disciplines and skill sets, draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on specifics of the domains to which it is applied. Fueled by the explosion of data, jobs that involve data science have proliferated and an array of data science programs at the undergraduate and graduate levels have been established. Nevertheless, data science is still in its infancy, which suggests the importance of envisioning what the field might look like in the future and what key steps can be taken now to move data science education in that direction. This study will set forth a vision for the emerging discipline of data science at the undergraduate level. This interim report lays out some of the information and comments that the committee has gathered and heard during the first half of its study, offers perspectives on the current state of data science education, and poses some questions that may shape the way data science education evolves in the future. The study will conclude in early 2018 with a final report that lays out a vision for future data science education.
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 139 |
| Release | : 2018-11-11 |
| Genre | : Education |
| ISBN | : 0309475597 |
Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 257 |
| Release | : 2019-10-20 |
| Genre | : Science |
| ISBN | : 0309486165 |
One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.
| Author | : Pieter Kubben |
| Publisher | : Springer |
| Total Pages | : 219 |
| Release | : 2018-12-21 |
| Genre | : Medical |
| ISBN | : 3319997130 |
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.
