Strategies In Biomedical Data Science PDF Download
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Author | : Jay A. Etchings |
Publisher | : John Wiley & Sons |
Total Pages | : 466 |
Release | : 2017-01-03 |
Genre | : Medical |
ISBN | : 1119256186 |
Download Strategies in Biomedical Data Science Book in PDF, ePub and Kindle
An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.
Author | : Robert Hoyt |
Publisher | : Lulu.com |
Total Pages | : 260 |
Release | : 2019-11-25 |
Genre | : Science |
ISBN | : 179476173X |
Download Introduction to Biomedical Data Science Book in PDF, ePub and Kindle
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.
Author | : National Academies of Sciences, Engineering, and Medicine |
Publisher | : National Academies Press |
Total Pages | : 93 |
Release | : 2020-07-09 |
Genre | : Computers |
ISBN | : 0309672759 |
Download Planning for Long-Term Use of Biomedical Data Book in PDF, ePub and Kindle
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 | : National Academies of Sciences, Engineering, and Medicine |
Publisher | : National Academies Press |
Total Pages | : 185 |
Release | : 2020-10-04 |
Genre | : Science |
ISBN | : 0309670039 |
Download Life-Cycle Decisions for Biomedical Data Book in PDF, ePub and Kindle
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 | : Rafael A. Irizarry |
Publisher | : CRC Press |
Total Pages | : 537 |
Release | : 2016-10-04 |
Genre | : Mathematics |
ISBN | : 1498775861 |
Download Data Analysis for the Life Sciences with R Book in PDF, ePub and Kindle
This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.
Author | : Ryan Sandefer |
Publisher | : |
Total Pages | : |
Release | : 2015-12-08 |
Genre | : |
ISBN | : 9781584264439 |
Download Data Analytics in Healthcare Research Book in PDF, ePub and Kindle
Author | : Ulrika Jägare |
Publisher | : John Wiley & Sons |
Total Pages | : 449 |
Release | : 2019-06-12 |
Genre | : Computers |
ISBN | : 1119566274 |
Download Data Science Strategy For Dummies Book in PDF, ePub and Kindle
All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the “what” and the “why” of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you’ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. Learn exactly what data science is and why it’s important Adopt a data-driven mindset as the foundation to success Understand the processes and common roadblocks behind data science Keep your data science program focused on generating business value Nurture a top-quality data science team In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.
Author | : Kun Chang Lee |
Publisher | : Academic Press |
Total Pages | : 298 |
Release | : 2020-10-18 |
Genre | : Science |
ISBN | : 0128193158 |
Download Data Analytics in Biomedical Engineering and Healthcare Book in PDF, ePub and Kindle
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
Author | : National Library of Medicine (U.S.). Board of Regents |
Publisher | : |
Total Pages | : 48 |
Release | : 2018 |
Genre | : Medical informatics |
ISBN | : |
Download A Platform for Biomedical Discovery and Data-powered Health Book in PDF, ePub and Kindle
Author | : James D. Malley |
Publisher | : Cambridge University Press |
Total Pages | : 301 |
Release | : 2011-02-24 |
Genre | : Medical |
ISBN | : 1139496859 |
Download Statistical Learning for Biomedical Data Book in PDF, ePub and Kindle
This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random ForestsTM, neural nets, support vector machines, nearest neighbors and boosting.