(Ebook) Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Personalized Healthcare and Medical Research Using AI, ML, and Related Technologies by Gary D. Miner, Linda A. Miner, Scott Burk, Mitchell Goldstein, Robert Nisbet, Nephi Walton, Thomas Hill ISBN 9780323952743, 0323952747

(Ebook) Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Personalized Healthcare and Medical Research Using AI, ML, and Related Technologies by Gary D. Miner, Linda A. Miner, Scott Burk, Mitchell Goldstein, Robert Nisbet, Nephi Walton, Thomas Hill ISBN 9780323952743, 0323952747

Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Personalized Healthcare and Medical Research Using AI, ML, and Related Technologies, 2nd Edition discusses the needs of healthcare and medicine in the 21st century and explains how data analytics play an important and revolutionary role on fulfilling them. With healthcare effectiveness and economics facing growing challenges, there is a rapidly emerging movement to fortify medical treatment and administration by tapping the predictive power of big data, and it has shown solid results: predictive analytics bolster patient care, reduce cost, and deliver greater efficiencies across a wide range of operational functions. The first part of the book brings a historical perspective and the issues of concern for healthcare delivery currently, highlighting the importance of using predictive analytics to help solve health crisis such as the COVID-19 pandemic. The second part provides access to practical step-by-step tutorials and case studies online, available in the book's companion website, to help reader to apply the knowledge gained through exercises based on real-world examples of successful predictive and prescriptive tools and systems. The final part focuses on specific technical operations related to quality, cost-effective medical and nursing care delivery and administration brought by practical predictive analytics; in addition, it discusses future developments on decisioning platforms that allow rapid/instant decisions on medical care and delivery. The book is a valuable resource for researchers, practitioners, healthcare industry workers, policy makers, and members of medical and biomedical fields who are interested to learn about recent developments on data analytics applied to healthcare and medicine. Brings a historical perspective in medical care to discuss both the current status of health care delivery worldwide and the importance of using modern predicti