Data Science and Predictive Analytics Biomedical and Health Applications using R

ISBN: 3319723464

Category: Uncategorized

Posted on 2018-11-09, by voska89.


Data Science and Predictive Analytics: Biomedical and Health Applications using R by Ivo D. Dinov
English | PDF,EPUB | 2018 | 851 Pages | ISBN : 3319723464 | 503.78 MB

Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall.
First, the volume of data is increasing much faster than the corresponding rise of our computational processing power (Kryder's law > Moore's law). Second, traditional discipline-bounds inhibit expeditious progress. Third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. There are very few rigorous instructional resources, interactive learning materials, and dynamic training environments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pillars for the urgently needed bridge to close that supply and demand predictive analytic skills gap.
Exposing the enormous opportunities presented by the tsunami of Big data, this textbook aims to identify specific knowledge gaps, educational barriers, and workforce readiness deficiencies. Specifically, it focuses on the development of a transdisciplinary curriculum integrating modern computational methods, advanced data science techniques, innovative biomedical applications, and impactful health analytics.
The content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. Big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies.
The two examples below demonstrate the powerful need for scientific knowledge, computational abilities, interdisciplinary expertise, and modern technologies necessary to achieve desired outcomes (improving human health and optimizing future return on investment). This can only be achieved by appropriately trained teams of researchers who can develop robust decision support systems using modern techniques and effective end-to-end protocols, like the ones described in this textbook.
* A geriatric neurologist is examining a patient complaining of gait imbalance and posture instability. To determine if the patient may suffer from Parkinson's disease, the physician acquires clinical, cognitive, phenotypic, imaging, and genetics data (Big Data). Most clinics and healthcare centers are not equipped with skilled data analytic teams that can wrangle, harmonize and interpret such complex datasets. A learner that completes a course of study using this textbook will have the competency and ability to manage the data, generate a protocol for deriving biomarkers, and provide an actionable decision support system. The results of this protocol will help the physician understand the entire patient dataset and assist in making a holistic evidence-based, data-driven, clinical diagnosis.
* To improve the return on investment for their shareholders, a healthcare manufacturer needs to forecast the demand for their product subject to environmental, demographic, economic, and bio-social sentiment data (Big Data). The organization's data-analytics team is tasked with developing a protocol that identifies, aggregates, harmonizes, models and analyzes these heterogeneous data elements to generate a trend forecast. This system needs to provide an automated, adaptive, scalable, and reliable prediction of the optimal investment, e.g., R&D allocation, that maximizes the company's bottom line. A reader that complete a course of study using this textbook will be able to ingest the observed structured and unstructured data, mathematically represent the data as a computable object, apply appropriate model-based and model-free prediction techniques. The results of these techniques may be used to forecast the expected relation between the company's investment, product supply, general demand of healthcare (providers and patients), and estimate the return on initial investments.
Visit My Blog For Daily Very Exclusive Content,We Are Here For You And Without You And Your Support We Can't Continue
Thanks For Buying Premium From My Links For Support

Download (Uploadgig)
Download ( Rapidgator )
Download ( NitroFlare )

Sponsored High Speed Downloads
7244 dl's @ 2301 KB/s
Download Now [Full Version]
7941 dl's @ 2996 KB/s
Download Link 1 - Fast Download
6579 dl's @ 3865 KB/s
Download Mirror - Direct Download

Search More...
Data Science and Predictive Analytics Biomedical and Health Applications using R

Search free ebooks in!

Download this book

No active download links here?
Please check the description for download links if any or do a search to find alternative books.

Related Books

  1. Ebooks list page : 37669
  2. 2017-11-03[PDF] Analytics in a Big Data World: The Essential Guide to Data Science and its Applications
  3. 2011-09-30Earth Materials and Health: Research Priorities for Earth Science and Public Health
  4. 2011-09-22Numerical data and functional relationships in science and technology: Ternary alloy systems. Iron systems
  5. 2018-01-29[PDF] C and Data Analytics 2 Bundle Manuscript Essential Beginners Guide on Enriching Your C Programming Skills and Learn Practical Data Analytics, Data Science, and Predictive Analytics for Beginners
  6. 2012-01-19Security Operations Management, Second Edition - Robert McCrie Professor And Chair Law Police Science And Criminal Justice Administration John Jay
  7. 2012-01-09Security Operations Management, Second Edition - Robert McCrie Professor And Chair Law Police Science And Criminal Justice Administration John Jay
  8. 2011-11-04Research Opportunities in Corrosion Science and Engineering
  9. 2011-10-30Research Opportunities in Corrosion Science and Engineering
  10. 2011-10-08An Assessment of Naval Hydromechanics Science and Technology (Compass Series)
  11. 2011-10-04Science and Technology for Army Homeland Security: Report 1
  12. 2011-09-16Gulf War and Health: Volume 2. Insecticides and Solvents
  13. 2011-09-11Basic Research in Information Science and Technology for Air Force Needs
  14. 2011-09-08Science and Technology in Kazakhstan: Current Status and Future Prospects
  15. 2011-09-01The Fundamental Role of Science and Technology in International Development
  16. 2011-07-16Science and Technology and the Future Development of Societies: International Workshop Proceedings
  17. 2011-07-12Gulf War and Health: Volume 2. Insecticides and Solvents
  18. 2011-06-03Science and Technology for Army Homeland Security: Report 1
  19. 2018-05-10Data Science and Analytics with Python
  20. 2018-04-22Data Science and Analytics with Python


No comments for "Data Science and Predictive Analytics Biomedical and Health Applications using R".

    Add Your Comments
    1. Download links and password may be in the description section, read description carefully!
    2. Do a search to find mirrors if no download links or dead links.


    required, will not be published

    need login


    Not clear? Click here to refresh.

    Back to Top
    The Jurassic Games FRENCH WEBRIP 2018 | Saints Row Gat out of Hell Update 1-RELOADED | Pro Evolution Soccer 2013 (Xbox 360) pes 2013