Health Analytics with R

• Verfasst von: Boland, Mary Regina [VerfasserIn]
• Titel: Health Analytics with R
• Titelzusatz: Learning Data Science Using Examples from Healthcare and Direct-to-Consumer Genetics
• Verf.angabe: by Mary Regina Boland
• Ausgabe: 1st ed. 2024.
• Verlagsort: Cham
• Verlagsort: Cham
• Verlag: Springer Nature Switzerland
• Verlag: Imprint: Springer
• E-Jahr: 2024
• Jahr: 2024.
• Jahr: 2024.
• Umfang: 1 Online-Ressource(XVIII, 660 p. 193 illus., 145 illus. in color.)
• ISBN: 978-3-031-74383-2
• DOI: doi:10.1007/978-3-031-74383-2
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• K10plus-PPN: 1913639657
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4645477633
• Lokale URL Inst.: Zum Volltext

Abstract

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.

Abstract

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.