Learning Data Science Using Examples from Healthcare and Direct-to-Consumer Genetics
Buch, Englisch, 660 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1172 g
ISBN: 978-3-031-74382-5
Verlag: Springer
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
Zielgruppe
Upper undergraduate
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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.
