Big Data in Otolaryngology
- A paraître
Description détaillée : Big Data in Otolaryngology
In Big Data in Otolaryngology, Dr. Jennifer Villwock leads a team of expert authors who provide a comprehensive view of many key impacts of big data in otolaryngology—including understanding what big data is and what we can and cannot learn from it; best practices regarding analysis; translating findings to clinical care and associated cautions; ethical issues; and future directions.
What are the Key Features of "Big Data in Otolaryngology" ?
- Covers the clinical relevance of big data in otolaryngology, lessons and limitations of large administrative datasets, biologic big data, and much more.
- Discusses artificial intelligence (AI) in otolaryngology and its clinical application.
- Presents a patient perspective on big data in otolaryngology and its use in clinical care, as well as a glimpse into the future of big data.
- Compiles the knowledge and expertise of leading experts in the field who have assembled the most up-to-date recommendations for managing big data in otolaryngology.
- Consolidates today's available information on this timely topic into a single, convenient resource.
Jennifer A. Vilwock : Author of "Big Data in Otolaryngology"
Jennifer A. Villwock, MD, Associate Professor, Otolaryngology-Head and Neck Surgery, The University of Kansas Medical Center, Kansas City, Kansa, USA
À PARAÎTRE OU DERNIÈRE PARUTION DANS LA MÊME CATÉGORIE :
Sommaire : Big Data in Otolaryngology
1.Introduction: Big Data - Science Fiction or Clinically Relevant?
2.Large Administrative Datasets: Lessons and Limitations
3.Biologic Big Data: Introduction to Genomics, Proteomics, and Metabolomics
4.Sources of High-Dimensional Data - The Electronic Health Record, Health Systems, and Insurance and Payor Data
5.Best Practices When Interpreting Big Data Studies: Considerations and Red Flags
6.Current Big Data Approaches to Clinical Questions in Otolaryngology
7.Translating Big Data to Patient Care
8.Bias in Big Data: Historically Underrepresented Groups and Implications
9.Artificial Intelligence in Otolaryngology
10.Clinical Applications of Artificial Intelligence: Clinical Decision Aids, Imaging Analysis, and Disease Prediction
11.The Patient Perspective on Big Data and Its Use in Clinical Care
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