Overview
- A novel transdisciplinary treatise of predictive health analytics
- Complete and self-contained treatment of the theory, experimental modeling, system development, and validation of predictive health analytics
- Unique collection of case-studies, advanced scientific concepts, lightweight tools, and end-to-end workflow protocols that can be used to learn, practice and apply to new challenges
- Includes unique interactive content supported by community of 100,000 R-developers
- Represents a blended STEM-Health Science approach to challenging biomedical problems
- Support reproducible science, transdisciplinary collaboration, and sharing
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About this book
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. Thissystem 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.Similar content being viewed by others
Keywords
- big data
- R
- statistical computing
- predictive analytics
- data science
- health analytics
- machine learning
- statistical learning in R
- hands-on machine learning
- Big Data methods
- data management
- streaming
- visualization
- neural networks
- controlled variable selection
- text mining
- natural language processing
- cross-validation
- deep learning
Table of contents (23 chapters)
Reviews
“Data Science and Predictive Analytics is an effective resource for those desiring to extend their knowledge of data science, R or both. The book is comprehensive and serves as a reference guide for data analytics, especially relating to the biomedical, health care and social fields.” (Mindy Capaldi, International Statistical Review, Vol. 87 (1), 2019)
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Data Science and Predictive Analytics
Book Subtitle: Biomedical and Health Applications using R
Authors: Ivo D. Dinov
DOI: https://doi.org/10.1007/978-3-319-72347-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Ivo D. Dinov 2018
Softcover ISBN: 978-3-030-10187-9Published: 25 January 2019
eBook ISBN: 978-3-319-72347-1Published: 27 August 2018
Edition Number: 1
Number of Pages: XXXIV, 832
Number of Illustrations: 198 b/w illustrations, 1245 illustrations in colour
Topics: Big Data, Big Data/Analytics, Health Informatics, Probability and Statistics in Computer Science, Data Mining and Knowledge Discovery