Big data analytics to advance stroke and cerebrovascular disease: A tool to bridge translational and clinical research PDF Download

Author: Alexis Netis Simpkins
Publisher: Frontiers Media SA
ISBN: 2832539084
Category : Medical
Languages : en
Pages : 320

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Author:
Publisher:
ISBN:
Category : Cerebrovascular disease
Languages : en
Pages : 36

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Author: Firas Kobeissy
Publisher: Academic Press
ISBN: 012809561X
Category : Medical
Languages : en
Pages : 225

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Book Description
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

Author: Pak H. Chan
Publisher: Cambridge University Press
ISBN: 9781139439657
Category : Medical
Languages : en
Pages : 492

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Book Description
Prevention, diagnosis and treatment are the watchwords in stroke research, for basic neuroscientists and clinicians alike. This 2002 book, from the 22nd Princeton Conference on Cerebrovascular Disease, contains contributions from outstanding investigators on numerous topics in stroke research. The contents cover the status and future directions of stroke pathophysiology, diagnosis and treatment, with special emphasis on the molecular and cellular mechanisms of ischaemic cell death and repair, and clinical issues including imaging, risk factors and therapeutic strategies in stroke. Available in both print and online formats, this survey of the basic and clinical science of stroke is an essential resource for all involved in advancing knowledge of cerebrovascular disease.

Author: Leo Anthony Celi
Publisher: Springer Nature
ISBN: 3030479943
Category : Medical
Languages : en
Pages : 471

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Book Description
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

Author: Forum on Drug Discovery Development and Translation
Publisher:
ISBN: 9780309384841
Category :
Languages : en
Pages : 156

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Book Description
Pharmaceutical companies, academic researchers, and government agencies such as the Food and Drug Administration and the National Institutes of Health all possess large quantities of clinical research data. If these data were shared more widely within and across sectors, the resulting research advances derived from data pooling and analysis could improve public health, enhance patient safety, and spur drug development. Data sharing can also increase public trust in clinical trials and conclusions derived from them by lending transparency to the clinical research process. Much of this information, however, is never shared. Retention of clinical research data by investigators and within organizations may represent lost opportunities in biomedical research. Despite the potential benefits that could be accrued from pooling and analysis of shared data, barriers to data sharing faced by researchers in industry include concerns about data mining, erroneous secondary analyses of data, and unwarranted litigation, as well as a desire to protect confidential commercial information. Academic partners face significant cultural barriers to sharing data and participating in longer term collaborative efforts that stem from a desire to protect intellectual autonomy and a career advancement system built on priority of publication and citation requirements. Some barriers, like the need to protect patient privacy, pre- sent challenges for both sectors. Looking ahead, there are also a number of technical challenges to be faced in analyzing potentially large and heterogeneous datasets. This public workshop focused on strategies to facilitate sharing of clinical research data in order to advance scientific knowledge and public health. While the workshop focused on sharing of data from preplanned interventional studies of human subjects, models and projects involving sharing of other clinical data types were considered to the extent that they provided lessons learned and best practices. The workshop objectives were to examine the benefits of sharing of clinical research data from all sectors and among these sectors, including, for example: benefits to the research and development enterprise and benefits to the analysis of safety and efficacy. Sharing Clinical Research Data: Workshop Summary identifies barriers and challenges to sharing clinical research data, explores strategies to address these barriers and challenges, including identifying priority actions and "low-hanging fruit" opportunities, and discusses strategies for using these potentially large datasets to facilitate scientific and public health advances.

Author: Paul A. Lapchak
Publisher:
ISBN: 9783319453460
Category : Cardiology
Languages : en
Pages : 795

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Author: Helmut Lechner
Publisher:
ISBN: 9780444807823
Category : Cerebrovascular disease
Languages : en
Pages : 350

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Book Description

Author: Kun Chang Lee
Publisher: Academic Press
ISBN: 012819314X
Category : Science
Languages : en
Pages : 296

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Book Description
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