04960nam a22006375i 4500 978-3-031-74383-2 DE-He213 20251030114652.0 cr nn 008mamaa 241231s2024 sz | s |||| 0|eng d 9783031743832 978-3-031-74383-2 10.1007/978-3-031-74383-2 doi QH324 QH450-450.6 PSAK bicssc PDN bicssc SCI008000 bisacsh PSAK thema PDN thema Boland, Mary Regina. author. aut http://id.loc.gov/vocabulary/relators/aut 89937 Health Analytics with R [electronic resource] : Learning Data Science Using Examples from Healthcare and Direct-to-Consumer Genetics / by Mary Regina Boland. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XVIII, 660 p. 193 illus., 145 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. Biology Technique. 67845 Gene expression. 77673 Quantitative research. Genetics. 68863 Genomics. 64972 Artificial intelligence Data processing. Gene Expression Analysis. 77674 Data Analysis and Big Data. Genetics. 68863 Biological Techniques. 67847 Genomic Analysis. 78940 Data Science. SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031743825 Printed edition: 9783031743849 Printed edition: 9783031743856 https://doi.org/10.1007/978-3-031-74383-2 ZDB-2-SME ZDB-2-SXM 66220 66220