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
Chapter 1Introduction.
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 15Ethics.
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.
