Health Analytics with R

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

  Includes homework questions and a lab tutorial script that individuals can work through at the end of each chapter Links genetic data and other health datasets toward actionable insights in a step-by-step manner Connects DTC genetic data from 23andme and other DTC platforms to information about traits that have health implications

  Autorentext

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.

Inhalt

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.