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 - Rilegato

Miner, Gary; Miner, Linda A., Ph.D.; Burk, Scott, Ph.D.; Goldstein, Mitchell, M.D.; Nisbet, Robert, Ph.D.

Rilegato

ISBN 10: 0323952747 ISBN 13: 9780323952743

Casa editrice: Academic Pr, 2023

Riassunto

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, Second Edition discusses the needs of healthcare and medicine in the 21st century, explaining how data analytics play an important and revolutionary role. 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, such as predictive analytics, which can bolster patient care, reduce costs, and deliver greater efficiencies across a wide range of operational functions.

Sections bring a historical perspective, highlight the importance of using predictive analytics to help solve health crisis such as the COVID-19 pandemic, provide access to practical step-by-step tutorials and case studies online, and use exercises based on real-world examples of successful predictive and prescriptive tools and systems. The final part of the book focuses on specific technical operations related to quality, cost-effective medical and nursing care delivery and administration brought by practical predictive analytics.

• Brings a historical perspective in medical care to discuss both the current status of health care delivery worldwide and the importance of using modern predictive analytics to help solve the health care crisis
• Provides online tutorials on several predictive analytics systems to help readers apply their knowledge on today’s medical issues and basic research
• Teaches how to develop effective predictive analytic research and to create decisioning/prescriptive analytic systems to make medical decisions quicker and more accurate

Su questo libro

Informazioni sugli autori

Dr. Gary Miner PhD received a B.S. from Hamline University, St. Paul, MN, with biology, chemistry, and education majors; an M.S. in zoology and population genetics from the University of Wyoming; and a Ph.D. in biochemical genetics from the University of Kansas as the recipient of a NASA pre-doctoral fellowship. He pursued additional National Institutes of Health postdoctoral studies at the U of Minnesota and U of Iowa eventually becoming immersed in the study of affective disorders and Alzheimer's disease.

In 1985, he and his wife, Dr. Linda Winters-Miner, founded the Familial Alzheimer's Disease Research Foundation, which became a leading force in organizing both local and international scientific meetings, bringing together all the leaders in the field of genetics of Alzheimer's from several countries, resulting in the first major book on the genetics of Alzheimer’s disease. In the mid-1990s, Dr. Miner turned his data analysis interests to the business world, joining the team at StatSoft and deciding to specialize in data mining. He started developing what eventually became the Handbook of Statistical Analysis and Data Mining Applications (co-authored with Drs. Robert A. Nisbet and John Elder), which received the 2009 American Publishers Award for Professional and Scholarly Excellence (PROSE). Their follow-up collaboration, Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, also received a PROSE award in February of 2013. Gary was also co-author of “Practical Predictive Analytics and Decisioning Systems for Medicine (Academic Press, 2015). Overall, Dr. Miner’s career has focused on medicine and health issues, and the use of data analytics (statistics and predictive analytics) in analyzing medical data to decipher fact from fiction.

Gary has also served as Merit Reviewer for PCORI (Patient Centered Outcomes Research Institute) that awards grants for predictive analytics research into the comparative effectiveness and heterogeneous treatment effects of medical interventions including drugs among different genetic groups of patients; additionally he teaches on-line classes in ‘Introduction to Predictive Analytics’, ‘Text Analytics’, ‘Risk Analytics’, and ‘Healthcare Predictive Analytics’ for the University of California-Irvine. Recently, until ‘official retirement’ 18 months ago, he spent most of his time in his primary role as Senior Analyst-Healthcare Applications Specialist for Dell | Information Management Group, Dell Software (through Dell’s acquisition of StatSoft (www.StatSoft.com) in April 2014). Currently Gary is working on two new short popular books on ‘Healthcare Solutions for the USA’ and ‘Patient-Doctor Genomics Stories’.

Linda A. Winters-Miner, PhD, earned her bachelor’s and master’s degrees at University of Kansas, her doctorate at the University of Minnesota, and completed post-doctoral studies in psychiatric epidemiology at the University of Iowa. She spent most of her career as an educator, in teacher education and statistics and research design. She spent nearly two years as a site coordinator for a major (Coxnex) drug trial. For 23 years, she was a Program Director at Southern Nazarene University - Tulsa. Her program direction included three undergraduate programs in business and psychology and three graduate programs in management, business administration, and health care administration. She has authored or co-authored numerous articles and books including with Gary and others, the first book concerning the genetics of Alzheimer's, Alzheimer's disease: Molecular genetics, Clinical Perspectives and Promising New Research. L Miner authored some of the tutorials in the first two predictive analytic books published in 2009 and 2012 by Elsevier. For ten years, she served as a Community Faculty for Research and Data Analysis at IHI Family Practice Medical Residency program in Tulsa. She taught predictive analytics online, including ‘healthcare predictive analytics’, for the University of California-Irvine. At present, Dr. Miner is Professor Emeritus, Professional and Graduate Studies, Southern Nazarene University and serves on the Editorial Board, The Journal of Geriatric Psychiatry and Neurology.

Scott Burk PhD is Chief Data Officer at M&M Predictive Analytics LLC, USA.

Dr. Goldstein MD, FAAP attended the University of Miami’s Honor Program in Medical Education under an Isaac B. Singer full tuition scholarship, completed his pediatric residency training at the University of California, Los Angeles, and finished his Neonatal Perinatal Medicine training at the University of California, Irvine in 1994. Dr. Goldstein is board certified in both Pediatrics and Neonatal Perinatal Medicine. He is an Associate Professor of Pediatrics at Loma Linda University Children’s Hospital and emeritus medical director of the Neonatal Intensive Care Unit at Citrus Valley in West Covina, CA. He has been in clinical practice for 20 years. At the various places he has worked, Dr. Goldstein has become fluent in a multitude of EMR’s including EPIC, Cerner, and Meditech. As a member of the Department Deputies Users Group at Loma Linda University Hospital, Dr. Goldstein participates in an ongoing EMR improvement process.
Dr. Goldstein is a past president of the Perinatal Advisory Council, Legislation, Advocacy and Consultation (PACLAC) as well as a past president of the National Perinatal Association (NPA). Dr. Goldstein is the twice recipient of the annual Jack Haven Emerson Award presented to the physician with the most promising study involving innovative pulmonary research and the 2013 recipient of the National Perinatal Association Stanley Graven lifetime achievement award presented for his ongoing commitment to the advancement of neonatal and perinatal health issues. He is the editor of PACLAC’s Neonatal Guidelines of Care as well as the Principal author of both the National Perinatal Association’s 2011 Best Practice Checklist – Oxygen Management for Preterm Infants and Respiratory Syncytial Virus (RSV) Prophylaxis 2012 Guidelines. Dr. Goldstein serves on the editorial board of the Journal of Perinatology as well as Neonatology Today, has represented the NPA to the American Academy of Pediatrics (AAP) perinatal section, and is a moderator of NICU-NET, a neonatal listserv. He is an executive board member and is on the nominations committee for the Section on Advances in Therapeutics & Technology (SOATT) of the AAP. Dr. Goldstein chaired the NPA National Conferences in 2004, 2008 and 2011 and continues to be active in conference planning as the CME Continuing Medical Education (CME) chair for PACLAC.

His research interests include the development of non-invasive monitoring techniques, evaluation of signal propagation during high frequency ventilation, and data mining techniques for improving quality of care. Dr. Goldstein has also been a vocal advocate for RSV prophylaxis and “right” sizing technology for the needs of neonates. Dr. Goldstein’s recent publications have included “Critical Complex Congenital Heart Disease (CCHD)” which was dual published in Neonatology Today and Congenital Cardiology Today, the “Late Preterm Guidelines of Care” published in the Journal of Perinatology, and “How Do We COPE with CPOE” published in Neonatology Today.

Bob Nisbet, PhD, is a Data Scientist, currently modeling precancerous colon polyp presence with clinical data at the UC-Irvine Medical Center. He has experience in predictive modeling in Telecommunications, Insurance, Credit, Banking. His academic experience includes teaching in Ecology and in Data Science. His industrial experience includes predictive modeling at AT&T, NCR, and FICO. He has worked also in Insurance, Credit, membership organizations (e.g. AAA), Education, and Health Care industries. He retired as an Assistant Vice President of Santa Barbara Bank & Trust in charge of business intelligence reporting and customer relationship management (CRM) modeling.

Dalla quarta di copertina

Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Healthcare and Medical Research Using AI, ML, and Related Technologies, Second 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.

Dettagli dell?edizione

• Editore: Academic Pr
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 0323952747
• ISBN 13: 9780323952743
• Rilegatura: Copertina rigida
• Numero edizione: 2
• Numero di pagine: 533
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile