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

By:

Boland, Mary Regina

Material type: Text

TextPublication details: Cham Springer 2024Description: xviii, 660 pISBN:

9783031743825

Subject(s):

DDC classification:

610.21 BOL

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. (https://link.springer.com/book/10.1007/978-3-031-74383-2)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Public Policy & General Management	610.21 BOL (Browse shelf(Opens below))	1	Available		009079

Table of contents:
Front Matter
Pages i-xviii
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Introduction
Front Matter
Pages 1-1
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Health Analytics and Precision Medicine
Mary Regina Boland
Pages 3-19
Genetics Analysis for Health Analytics
Front Matter
Pages 21-21
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Processing Direct-to-Consumer Genetic Data from Major Industry Platforms including 23andme, AncestryDNA
Mary Regina Boland
Pages 23-71
Determining Phenotypic Traits from Single Nucleotide Polymorphism (SNP) Data
Mary Regina Boland
Pages 73-108
Clinical Genetic Databases: ClinVar, ACMG Clinical Practice Guidelines
Mary Regina Boland
Pages 109-153
Inferring Disease Risk from Genetics Using Cohort-Level Analysis
Mary Regina Boland
Pages 155-214
Challenges in Health Analytics Due to Lack of Diversity in Genetic Research: Implications and Issues with Published Knowledge
Mary Regina Boland
Pages 215-260
Clinical Data Analysis for Health Analytics
Front Matter
Pages 261-261
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Clinical Data and Health Data Types
Mary Regina Boland
Pages 263-302
Clinical Datasets: Open Access Electronic Health Records Datasets
Mary Regina Boland
Pages 303-332
Association Mining with Clinical Data: Phenotype-Wide Association Studies (PheWAS)
Mary Regina Boland
Pages 333-364
Organizing a Clinical Study Across Multiple Clinical Systems: Common Data Models
Mary Regina Boland
Pages 365-408
Environmental Data and Social Determinants of Health for Health Analytics
Front Matter
Pages 409-409
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Environmental Health Data Types for Health Analytics
Mary Regina Boland
Pages 411-449
Geospatial Analysis Using Environmental Health Data
Mary Regina Boland
Pages 451-491
Social Determinants of Health Data for Health Analytics
Mary Regina Boland
Pages 493-536
Geospatial Analysis Using Social Determinants of Health, Clinical Data, and Spatial Regression Methods
Mary Regina Boland
Pages 537-579
Ethics for Health Analytics
Front Matter
Pages 581-581
Ethics
Mary Regina Boland
Pages 583-622
[https://link.springer.com/book/10.1007/978-3-031-74383-2?page=2#toc]

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.

(https://link.springer.com/book/10.1007/978-3-031-74383-2)

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