Health Analytics with R : Learning Data Science Using Examples from Healthcare and Direct-to-Consumer Genetics / by Mary Regina Boland.

Format:

Book

Author/Creator:

Boland, Mary Regina.

Language:

English

Subjects (All):

Biology--Technique.

Biology.

Gene expression.

Quantitative research.

Genetics.

Genomics.

Artificial intelligence--Data processing.

Artificial intelligence.

Gene Expression Analysis.

Data Analysis and Big Data.

Biological Techniques.

Genomic Analysis.

Data Science.

Local Subjects:

Gene Expression Analysis.

Data Analysis and Big Data.

Genetics.

Biological Techniques.

Genomic Analysis.

Data Science.

Physical Description:

1 online resource (663 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

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.

Contents:

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.

ISBN:

9783031743832

3031743830

OCLC:

1482736999