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.
1. Practical Data Analytics for Innovation in Medicine - p. IFC1 2. Cover - p. IFC1 3. Practical Data Analytics for Innovation in Medicine - p. IFC1 4. Title Page - p. iii 5. Copyright - p. iv 6. Table of Contents - p. iv 7. Dedication - p. v 8. About the authors - p. xix 9. Foreword for the 2nd edition–John Halamka - p. xxiii 10. Foreword for the 1st edition by Thomas H. Davenport - p. xxv 11. Foreword for the 1st edition by James Taylor - p. xxvii 12. Foreword for the 1st edition by John Halamka - p. xxix 13. Preface and overview for the 2nd edition - p. xxxi 14. Preface to the 1st edition - p. xxxiii 15. Acknowledgment - p. xxxv 16. Guest Chapter Author’s Listing - p. xxxvii 17. Endorsements and reviewer Blurbs—from the 1st edition - p. xxxix 18. Instructions for using software for the tutorials—how to download from web pages—for the 2nd edition - p. xli 19. Chapter 1 What we want to accomplish with this second edition of our first “Big Green Book” - p. 5 20. Chapter 2 History of predictive analytics in medicine and healthcare - p. 15 21. Chapter 3 Bioinformatics - p. 35 22. Chapter 4 Data and process models in medical informatics - p. 57 23. Chapter 5 Access to data for analytics—the “Biggest Issue” in medical and healthcare predictive analytics - p. 65 24. Chapter 6 Precision (personalized) medicine - p. 73 25. Chapter 7 Patient-directed healthcare - p. 105 26. Chapter 8 Regulatory measures—agencies, and data issues in medicine and healthcare - p. 159 27. Chapter 9 Predictive analytics with multiomics data - p. 171 28. Chapter 10 Artificial intelligence and genomics - p. 185 29. 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 - p. 199 30. 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 - p. 257 31. 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 - p. 325 32. Chapter 14 Modeling precancerous colon polyps with OMOP data - p. 339 33. Chapter 15 Prediction of pancreatic and lung cancer from metabolomics data - p. 355 34. Chapter 16 Covid-19 descriptive analytics visualization of pandemic and hospitalization data - p. 361 35. Chapter 17 Disseminated intravascular coagulation predictive analytics with pediatric ICU admissions - p. 375 36. Chapter 18 Challenges for healthcare administration and delivery: integrating predictive and prescriptive modeling into personalized–precision healthcare - p. 395 37. Chapter 19 Challenges of medical research in incorporating modern data analytics in studies - p. 401 38. Chapter 20 The nature of insight from data and implications for automated decisioning: predictive and prescriptive models, decisions, and actions - p. 405 39. Chapter 21 Model management and ModelOps: managing an artificial intelligence-driven enterprise - p. 417 40. Chapter 22 The forecasts for advances in predictive and prescriptive analytics and related technologies for the year 2022 and beyond - p. 433 41. Chapter 23 Sampling and data analysis: variability in data may be a better predictor than exact data points with many kinds of Medical situations - p. 443 42. Chapter 24 Analytics architectures for the 21st century - p. 457 43. Chapter 25 Predictive models versus prescriptive models; causal inference and Bayesian networks - p. 473 44. Chapter 26 The future: 21st century healthcare and wellness in the digital age - p. 487 45. Appendix A Modeling new COVID-19 deaths - p. 511 46. Index - p. 519 47. Index - Continued - p. 528
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.
1. Practical Data Analytics for Innovation in Medicine - p. IFC1 2. Cover - p. IFC1 3. Practical Data Analytics for Innovation in Medicine - p. IFC1 4. Title Page - p. iii 5. Copyright - p. iv 6. Table of Contents - p. iv 7. Dedication - p. v 8. About the authors - p. xix 9. Foreword for the 2nd edition–John Halamka - p. xxiii 10. Foreword for the 1st edition by Thomas H. Davenport - p. xxv 11. Foreword for the 1st edition by James Taylor - p. xxvii 12. Foreword for the 1st edition by John Halamka - p. xxix 13. Preface and overview for the 2nd edition - p. xxxi 14. Preface to the 1st edition - p. xxxiii 15. Acknowledgment - p. xxxv 16. Guest Chapter Author’s Listing - p. xxxvii 17. Endorsements and reviewer Blurbs—from the 1st edition - p. xxxix 18. Instructions for using software for the tutorials—how to download from web pages—for the 2nd edition - p. xli 19. Chapter 1 What we want to accomplish with this second edition of our first “Big Green Book” - p. 5 20. Chapter 2 History of predictive analytics in medicine and healthcare - p. 15 21. Chapter 3 Bioinformatics - p. 35 22. Chapter 4 Data and process models in medical informatics - p. 57 23. Chapter 5 Access to data for analytics—the “Biggest Issue” in medical and healthcare predictive analytics - p. 65 24. Chapter 6 Precision (personalized) medicine - p. 73 25. Chapter 7 Patient-directed healthcare - p. 105 26. Chapter 8 Regulatory measures—agencies, and data issues in medicine and healthcare - p. 159 27. Chapter 9 Predictive analytics with multiomics data - p. 171 28. Chapter 10 Artificial intelligence and genomics - p. 185 29. 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 - p. 199 30. 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 - p. 257 31. 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 - p. 325 32. Chapter 14 Modeling precancerous colon polyps with OMOP data - p. 339 33. Chapter 15 Prediction of pancreatic and lung cancer from metabolomics data - p. 355 34. Chapter 16 Covid-19 descriptive analytics visualization of pandemic and hospitalization data - p. 361 35. Chapter 17 Disseminated intravascular coagulation predictive analytics with pediatric ICU admissions - p. 375 36. Chapter 18 Challenges for healthcare administration and delivery: integrating predictive and prescriptive modeling into personalized–precision healthcare - p. 395 37. Chapter 19 Challenges of medical research in incorporating modern data analytics in studies - p. 401 38. Chapter 20 The nature of insight from data and implications for automated decisioning: predictive and prescriptive models, decisions, and actions - p. 405 39. Chapter 21 Model management and ModelOps: managing an artificial intelligence-driven enterprise - p. 417 40. Chapter 22 The forecasts for advances in predictive and prescriptive analytics and related technologies for the year 2022 and beyond - p. 433 41. Chapter 23 Sampling and data analysis: variability in data may be a better predictor than exact data points with many kinds of Medical situations - p. 443 42. Chapter 24 Analytics architectures for the 21st century - p. 457 43. Chapter 25 Predictive models versus prescriptive models; causal inference and Bayesian networks - p. 473 44. Chapter 26 The future: 21st century healthcare and wellness in the digital age - p. 487 45. Appendix A Modeling new COVID-19 deaths - p. 511 46. Index - p. 519 47. Index - Continued - p. 528
