Health Analytics with R - Mary Regina Boland

Health Analytics with R

Learning Data Science Using Examples from Healthcare and Direct-to-Consumer Genetics
Buch | Hardcover
XVIII, 656 Seiten
2025
Springer International Publishing (Verlag)
978-3-031-74382-5 (ISBN)
106,99 inkl. MwSt

This textbook teaches health analytics using examples from the statistical programming language R. It utilizes real-world examples with publicly available datasets from healthcare and direct-to-consumer genetics to provide learners with real-world examples and enable them to get their hands on actual data. This textbook is designed to accompany either a senior-level undergraduate course or a Masters level graduate course on health analytics.

The reader will advance from no prior knowledge of R to being well versed in applications within R that apply to data science and health analytics.

"I have never seen a book like this and think it will make an important contribution to the field. I really like that it covers environmental, social, and geospatial data. I also really like the coverage of ethics. These aspects of health analytics are often overlooked or deemphasized. I will definitely buy copies for my team."

- Jason Moore, Cedars-Sinai Medical Center

"Overall, I have a highly positive impression of the book. It is VERY comprehensive. It covers very extensive data types. I do not recall other books with the same level of comprehensiveness."

- Shuangge Ma, Yale University

"The book is comprehensive in both aspects of genetics, and health analytics. It covers any type of information a healthcare data scientist should be familiar with, whether they are novice or experienced. I found any chapter that I looked into comprehensive, but also not too detailed (although in general this book is more than 600 pages of comprehensive and detailed relevant information)."

- Robert Moskovtich, Ben-Gurion University of the Negev

Dr. Mary Regina Boland has been in the field of informatics/health analytics for the past 14 years, specifically in academic medical centers for 13 years. She has taught a Precision Medicine/Health Analytics course for Masters-level students at the University of Pennsylvania for 5-years (2018-2023) located in Philadelphia, PA, USA, and she is currently teaching an advanced undergraduate level course called Health Analytics at Saint Vincent College in Latrobe, PA, USA.

Chapter 1-Introduction.- Chapter 2-Genetics Analysis for Health Analytics.- Chapter 3-Determining Phenotypic Traits from Single Nucleotide Polymorphism (SNP) Data.- Chapter 4-Clinical Genetic Databases: ClinVar, ACMG Clinical Practice Guidelines.- Chapter 5-Inferring Disease Risk from Genetics.- Chapter 6-Challenges in Health Analytics Due to Lack of Diversity in Genetic Research: Implications and Issues with Published Knowledge.- Chapter 7-Clinical Data and Health Data Types.- Chapter 8-Clinical Datasets: Open Access Electronic Health Records Datasets.- Chapter 9-Association Mining with Clinical Data: Phenotype-Wide Association Studies (PheWAS).- Chapter 10-Organizing a Clinical Study Across Multiple Clinical Systems: Common Data Models.- Chapter 11-Environmental Health Data Types for Health Analytics.- Chapter 12-Geospatial Analysis Using Environmental Health Data.- Chapter 13-Social Determinants of Health Data for Health Analytics.- Chapter 14-Geospatial Analysis Using Social Determinants of Health, Clinical Data and Spatial Regression Methods.- Chapter 15-Ethics.

Erscheint lt. Verlag 31.1.2025
Zusatzinfo XVIII, 656 p. 193 illus., 145 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Themenwelt Mathematik / Informatik Informatik Datenbanken
Naturwissenschaften Biologie Genetik / Molekularbiologie
Schlagworte clinical data • data analytics • Data Mining • Data Science • directtoconsumer • Direct-to-consumer genetics • Genetics • health analytics • Personalized medicine • Precision medicine • R • R Code
ISBN-10 3-031-74382-2 / 3031743822
ISBN-13 978-3-031-74382-5 / 9783031743825
Zustand Neuware
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