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

Gary D. Miner, Linda A. Miner, Scott Burk, Mitchell Goldstein, Robert Nisbet, Nephi Walton, Thomas Hill

• Publisher: Academic Press
• Year: 2023
• Print ISBN: 9780323952743
• eBook ISBN: 9780323952750
• Edition: 2
• Topic: Biological Sciences
• Subtopic: Molecular Biology
• Index: Biological Sciences

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

Table of contents

• Practical Data Analytics for Innovation in Medicine
• Cover
• Title Page
• Copyright
• Table of Contents
• Dedication
• About the authors
• Foreword for the 2nd edition–John Halamka
• Foreword for the 1st edition by Thomas H. Davenport
• Foreword for the 1st edition by James Taylor
• Foreword for the 1st edition by John Halamka
• Preface and overview for the 2nd edition
• Preface to the 1st edition
• Acknowledgment
• Guest Chapter Author’s Listing
• Endorsements and reviewer Blurbs—from the 1st edition
• Instructions for using software for the tutorials—how to download from web pages—for the 2nd edition
• Chapter 1 What we want to accomplish with this second edition of our first “Big Green Book”
• Chapter 2 History of predictive analytics in medicine and healthcare
• Chapter 3 Bioinformatics
• Chapter 4 Data and process models in medical informatics
• Chapter 5 Access to data for analytics—the “Biggest Issue” in medical and healthcare predictive analytics
• Chapter 6 Precision (personalized) medicine
• Chapter 7 Patient-directed healthcare
• Chapter 8 Regulatory measures—agencies, and data issues in medicine and healthcare
• Chapter 9 Predictive analytics with multiomics data
• Chapter 10 Artificial intelligence and genomics
• Chapter 11 Glaucoma (eye disease): a real case study; with suggested predictive analytic modeling for identifying an individual patient’s best diagnosis and best treatment
• Chapter 12 Using data science algorithms in predicting ICU patient urine output in response to diuretics to aid clinicians and healthcare workers in clinical decision-making
• Chapter 13 Prediction tool development: creation and adoption of robust predictive model metrics at the bedside for greatly benefiting the patient, like preterm infants at risk of bronchopulmonary dysplasia, using Shiny-R
• Chapter 14 Modeling precancerous colon polyps with OMOP data
• Chapter 15 Prediction of pancreatic and lung cancer from metabolomics data
• Chapter 16 Covid-19 descriptive analytics visualization of pandemic and hospitalization data
• Chapter 17 Disseminated intravascular coagulation predictive analytics with pediatric ICU admissions
• Chapter 18 Challenges for healthcare administration and delivery: integrating predictive and prescriptive modeling into personalized–precision healthcare
• Chapter 19 Challenges of medical research in incorporating modern data analytics in studies
• Chapter 20 The nature of insight from data and implications for automated decisioning: predictive and prescriptive models, decisions, and actions
• Chapter 21 Model management and ModelOps: managing an artificial intelligence-driven enterprise
• Chapter 22 The forecasts for advances in predictive and prescriptive analytics and related technologies for the year 2022 and beyond
• Chapter 23 Sampling and data analysis: variability in data may be a better predictor than exact data points with many kinds of Medical situations
• Chapter 24 Analytics architectures for the 21st century
• Chapter 25 Predictive models versus prescriptive models; causal inference and Bayesian networks
• Chapter 26 The future: 21st century healthcare and wellness in the digital age
• Appendix A Modeling new COVID-19 deaths
• Index
• Index - Continued