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Health analytics with R = learning data science using examples from healthcare and direct-to-consumer genetics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Health analytics with R/ by Mary Regina Boland.
Reminder of title:	learning data science using examples from healthcare and direct-to-consumer genetics /
Author:	Boland, Mary Regina.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xviii, 660 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Medical informatics. -
Online resource:	https://doi.org/10.1007/978-3-031-74383-2
ISBN:	9783031743832

Health analytics with R = learning data science using examples from healthcare and direct-to-consumer genetics /
Boland, Mary Regina.

Health analytics with Rlearning data science using examples from healthcare and direct-to-consumer genetics /[electronic resource] :by Mary Regina Boland. - Cham :Springer Nature Switzerland :2024. - xviii, 660 p. :ill. (chiefly color), digital ;24 cm.

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.

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.

ISBN: 9783031743832

Standard No.: 10.1007/978-3-031-74383-2doiSubjects--Topical Terms:

661258
Medical informatics.

LC Class. No.: R858

Dewey Class. No.: 610.285

Health analytics with R = learning data science using examples from healthcare and direct-to-consumer genetics /
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300 $a xviii, 660 p. : $b ill. (chiefly color), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
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650 0 $a R (Computer program language) $3 784593
650 1 4 $a Gene Expression Analysis. $3 3591653
650 2 4 $a Data Analysis and Big Data. $3 3538537
650 2 4 $a Genetics. $3 530508
650 2 4 $a Biological Techniques. $3 900035
650 2 4 $a Genomic Analysis. $3 3591652
650 2 4 $a Data Science. $3 3538937
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