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Big Data in Otolaryngology, 1st Edition

ISBN: 9780443105203
ISBN: 9780443105203
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Διαστάσεις 24 × 19 cm
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Διαθέσιμο - Προπαραγγελία|Διαθεσιμότητα: 11-14 ημέρες

Περιγραφή

Big data plays an increasingly important role in today’s practice of otolaryngology and in all of healthcare. 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.
Key Features
  • 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.
Author Information
Edited by Jennifer A. Villwock, MD, Associate Professor, Otolaryngology-Head and Neck Surgery, The University of Kansas Medical Center, Kansas City, Kansas

Περιεχόμενα

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