Health Analytics with R : Learning Data Science Using Examples from Healthcare and Direct-to-Consumer Genetics （2024）

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Health Analytics with R : Learning Data Science Using Examples from Healthcare and Direct-to-Consumer Genetics （2024）

Boland, Mary Regina

ウェブストア価格 ¥24,994（本体¥22,722）
Springer International Publishing AG（2025/01発売）
外貨定価 UK£ 89.99
ポイント 227pt

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製本 Hardcover:ハードカバー版／ページ数 660 p.
商品コード 9783031743825

Full Description

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 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.