Data Science and Predictive Analytics: Biomedical and Health Applications using R

Data Science and Predictive Analytics is a textbook that combines mathematical foundations, computational algorithms, statistical inference techniques, and machine learning approaches to address biomedical informatics, health analytics, and decision-science challenges. It offers a transdisciplinary curriculum with fourteen chapters covering visualization, linear modeling, dimensionality reduction, supervised classification, black-box machine learning, qualitative learning, unsupervised clustering, model performance assessment, feature selection, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition includes additional learning-based strategies and electronic appendices. The book is suitable for formal didactic instructor-guided course education and self-learning, and is presented at the upper-division and graduate-level college courses.

Format: Hardback
Length: 918 pages
Publication date: 17 February 2023
Publisher: Springer International Publishing AG

This comprehensive textbook delves into the intricate realm of biomedical informatics, health analytics, and decision science, offering a robust foundation of mathematical principles, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches. Each concept is meticulously formulated with a rigorous symbolic approach, accompanied by computational algorithms and comprehensive end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows facilitate active learning, showcasing comprehensive data manipulation, interactive visualizations, and sophisticated analytics. The content encompasses open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings.

In addition to the formidable challenges associated with handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there exist unique opportunities that arise with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics specifically address knowledge gaps, overcome educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum that integrates core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications.

The textbook is organized into fourteen chapters, beginning with an introduction that gradually builds foundational skills from visualization to linear modeling, dimensionality reduction, and supervision. Throughout the chapters, real-world case studies and practical examples are employed to illustrate the application of the discussed concepts, enhancing the reader's understanding and relevance of the material.

By incorporating a comprehensive blend of mathematical foundations, computational algorithms, statistical inference techniques, and machine learning approaches, this textbook equips readers with the necessary tools and knowledge to address complex biomedical informatics, health analytics, and decision science challenges. It serves as a valuable resource for students, researchers, and professionals seeking to advance their expertise in these fields and make meaningful contributions to the healthcare and scientific communities.

Weight: 1586g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031174827
Edition number: 2nd ed. 2023