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Practical Data Analytics for Innovation in Medicine 2023

1. Practical Data Analytics for Innovation in Medicine

2. Cover

3. Title Page

4. Copyright

5. Table of Contents

6. Dedication

7. About the authors

8. Foreword for the 2nd edition–John Halamka

9. Foreword for the 1st edition by Thomas H. Davenport

10. Foreword for the 1st edition by James Taylor

11. Foreword for the 1st edition by John Halamka

12. Preface and overview for the 2nd edition

13. Preface to the 1st edition

14. Acknowledgment

15. Guest Chapter Author’s Listing

16. Endorsements and reviewer Blurbs—from the 1st edition

17. Instructions for using software for the tutorials—how to download from web pages—for the 2nd edition

18. Chapter 1 What we want to accomplish with this second edition of our first “Big Green Book”

19. Chapter 2 History of predictive analytics in medicine and healthcare

20. Chapter 3 Bioinformatics

21. Chapter 4 Data and process models in medical informatics

22. Chapter 5 Access to data for analytics—the “Biggest Issue” in medical and healthcare predictive analytics

23. Chapter 6 Precision (personalized) medicine

24. Chapter 7 Patient-directed healthcare

25. Chapter 8 Regulatory measures—agencies, and data issues in medicine and healthcare

26. Chapter 9 Predictive analytics with multiomics data

27. Chapter 10 Artificial intelligence and genomics

28. 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

29. 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

30. 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

31. Chapter 14 Modeling precancerous colon polyps with OMOP data

32. Chapter 15 Prediction of pancreatic and lung cancer from metabolomics data

33. Chapter 16 Covid-19 descriptive analytics visualization of pandemic and hospitalization data

34. Chapter 17 Disseminated intravascular coagulation predictive analytics with pediatric ICU admissions

35. Chapter 18 Challenges for healthcare administration and delivery: integrating predictive and prescriptive modeling into personalized–precision healthcare

36. Chapter 19 Challenges of medical research in incorporating modern data analytics in studies

37. Chapter 20 The nature of insight from data and implications for automated decisioning: predictive and prescriptive models, decisions, and actions

38. Chapter 21 Model management and ModelOps: managing an artificial intelligence-driven enterprise

39. Chapter 22 The forecasts for advances in predictive and prescriptive analytics and related technologies for the year 2022 and beyond

40. Chapter 23 Sampling and data analysis: variability in data may be a better predictor than exact data points with many kinds of Medical situations

41. Chapter 24 Analytics architectures for the 21st century

42. Chapter 25 Predictive models versus prescriptive models; causal inference and Bayesian networks

43. Chapter 26 The future: 21st century healthcare and wellness in the digital age

44. Appendix A Modeling new COVID-19 deaths

45. Index

46. Index - Continued