
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 - February 8, 2023
- Imprint: Academic Press
- Authors: Gary D. Miner, Linda A. Miner, Scott Burk, Mitchell Goldstein, Robert Nisbet, Nephi Walton, Thomas Hill
- Language: English
- Hardback ISBN:9 7 8 - 0 - 3 2 3 - 9 5 2 7 4 - 3
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 5 2 7 5 - 0
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Request a sales quotePractical 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
- Cover image
- Title page
- Table of Contents
- Copyright
- 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
- Modern medicine: an exercise in prediction and preparation
- Wasted costs in US healthcare systems
- Acknowledgment
- Guest Chapter Author’s Listing
- Guest – Authors
- 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
- Prologue to Part I
- Part I: Historical perspective and the issues of concern for health care delivery in the 21st century
- Chapter 1. What we want to accomplish with this second edition of our first “Big Green Book”
- Abstract
- Chapter outline
- Prelude
- Purpose/summary
- Chapter conclusion
- Postscript
- References
- Chapter 2. History of predictive analytics in medicine and healthcare
- Abstract
- Chapter outline
- Prelude
- Outline
- Introduction
- Part I. Development of bodies of medical knowledge
- Classification of medical practice among ancient and modern cultures
- Medical practice documents in major world cultures of Europe and the Middle East
- Summary of royal medical documentation in ancient cultures
- Effects of the middle ages on medical documentation
- Rebirth of Interest in medical documentation during the renaissance
- Medical documentation after the enlightenment
- Part II. Analytical decision systems in medicine and healthcare
- Best practice guidelines
- Medical records move into the digital world
- References
- Chapter 3. Bioinformatics
- Abstract
- Chapter outline
- Prelude
- The rise of predictive analytics in healthcare
- Moving from reactive to proactive response in healthcare
- Medicine and big data
- An approach to predictive analytics projects
- Translational bioinformatics
- Clinical decision support systems
- Hybrid clinical decision support systems
- Consumer health informatics
- Direct-to-consumer genetic testing
- Use of predictive analytics to avoid an undesirable future
- Consumer health kiosks
- Patient monitoring systems
- Public health informatics
- Medical imaging
- Clinical research informatics
- Intelligent search engines
- Personalized medicine
- Hospital optimization
- Challenges
- Portability of PA models
- Regulation of PA models
- Summary
- Postscript
- References
- Further reading
- Chapter 4. Data and process models in medical informatics
- Abstract
- Chapter outline
- Prelude
- Chapter purpose
- Introduction
- Systems for classification of diseases and mortality
- The OMOP common data model
- How this chapter facilitates patient-centric healthcare
- Postscript
- References
- Further reading
- Chapter 5. Access to data for analytics—the “Biggest Issue” in medical and healthcare predictive analytics
- Abstract
- Chapter outline
- Prelude
- Size of data in our world: estimated digital universe now and in the future
- Reasons why healthcare data is difficult to get and difficult to measure
- Multiple places where medical data are found
- Many different formats of medical data: structured and unstructured
- Changing government regulatory requirements keep changing what data is taken and kept
- Conclusion of Chapter 5: the importance of health care data analytics
- Postscript
- References
- Further reading
- Chapter 6. Precision (personalized) medicine
- Abstract
- Chapter outline
- Preamble
- What is personalized/precision medicine?
- Precision medicine, genomics, and pharmacogenomics
- Changing the definition of diseases
- Systems biology
- Efficacy of current methods—why we need personalized medicine
- Predictive analytics in personalized medicine
- The future: predictive and prescriptive medicine
- The diversity of available healthcare data
- Diversity of data types available
- All the other OMICs
- The future
- Postscript
- References
- Further reading
- Chapter 7. Patient-directed healthcare
- Abstract
- Chapter outline
- Prelude
- Empowerment in patient-directed medicine
- Research questions
- Collaboration between patients and the medical community
- Communication and trust
- How patient-directed medicine works using predictive analytics
- Communication skills in the medical setting
- Patients selecting their best models of care
- Consumerism and advertising in patient-directed healthcare
- Burden of healthcare—predicting the future
- Models of insurance—predicting the best for individuals
- Research assisting patients in self-education and decisions
- Patient self-responsibility: highlight on obesity
- The need for N of 1 studies
- Patient portals
- Alternatives and new models
- Predictive analytics for patient decision-making
- Chapter conclusion
- Postscript
- References
- Chapter 8. Regulatory measures—agencies, and data issues in medicine and healthcare
- Abstract
- Chapter outline
- Prelude
- Introduction
- What is an electronic medical records?
- Five of the best open source electronic medical records systems for medical practices
- Rise of the international classification of disease
- Six Sigma
- Quality control
- Lean concepts for healthcare: the lean hospital as a methodology of Six Sigma
- Root cause analysis
- Henry Ford Hospitals and Virginia Mason Hospital
- Postscript
- References
- Further reading
- Chapter 9. Predictive analytics with multiomics data
- Abstract
- Chapter outline
- Prelude
- Introduction to multiomics
- Basic analytics operations in multiomics
- Multiomics data integration
- Multiomics data preparation
- Analysis methods
- Data preprocessing tools in multiomics
- Focus on metabolomics
- Postscript
- References
- Further reading
- Chapter 10. Artificial intelligence and genomics
- Abstract
- Chapter outline
- Prelude
- How do we enable the clinical application of artificial intelligence in genomics?
- Genomics fast moving field—and now ready for artificial intelligence to have an impact
- Need to open existing large datasets to more researchers
- Successful artificial intelligence models will be ones that use smaller and manageable portions of the human genome
- Polygenic risk scores
- Artificial intelligence models cannot replace but must augment physicians diagnosis and treatment decisions
- Governance—balance between rapid approval of models and ensuring no human harm
- EHR and integration of artificial intelligence into clinical workflows
- What would an artificial intelligence and genomics integration look like?
- Real-world examples of artificial intelligence and genomics modeling systems emerging in 2022
- Conclusions
- Postscript
- References
- Further reading
- Prologue to Part II
- Part II. Practical step-by-step tutorials and case studies
- Prologue to Part III
- Part III: Practical application examples
- 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
- Abstract
- Chapter outline
- Prelude
- Why this chapter in this book?
- How serious is glaucoma? Why do we need to watch for it?
- What is a normal eye pressure?
- Characteristics of glaucoma disease
- Risk factors and treatment
- Basic anatomy of the eye and relation of physical structure to glaucoma disease
- What is glaucoma?
- “Minimally invasive” surgeries can be invasive
- Fluid flow in the two main types of glaucoma
- Photography of eye—looking at fundus in the diagnosis of glaucoma
- Case study: my (Gary’s) glaucoma progression (from about 2010 to 2022)
- Self-monitoring intraocular pressure by the patient for more accurate DX and treatment decisions
- i-CARE home device for patient home monitoring of intraocular pressure values
- My invasive surgery—2021—XEN-gel shunt and later Ahmed valve shunt
- Increased night-time urination frequency was an unpleasant side-effect of my using steroid eyedrops
- Predictive analytic modeling possibilities
- Even visual field tests can now be automated with artificial intelligence—machine learning methods
- Using STATISTICA statistical and predictive analytic software to visualize patient Gary’s IOP data
- Future possible treatments for glaucoma
- FINAL IOP levels for Gary upon finding “optimum mix of steroid and IOP eye drops”
- Postscript
- References
- Further reading
- 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
- Abstract
- Chapter outline
- Prelude
- Introduction
- Outputs and conclusion from a literature review
- The data used
- Algorithm outputs and decisions
- The champion algorithms
- The conclusions on our champion algorithm
- Examples to illustrate model performance for actual patients
- Conclusions and further recommendations
- Postscript
- Further reading
- 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
- Abstract
- Chapter outline
- Prelude
- Author’s note
- Rationale
- Methods
- Code examples and tutorial
- Conclusion
- Appendix
- Postscript
- References
- Further reading
- Chapter 14. Modeling precancerous colon polyps with OMOP data
- Abstract
- Chapter outline
- Prelude
- Chapter purpose
- Introduction
- Methods
- Feature selection process
- Methods of feature selection
- Data conditioning
- Modeling
- Results and discussion
- Other important aspects of the trained model
- Conclusions
- How this chapter facilitates patient-centric medical health care
- Postscript
- References
- Further reading
- Chapter 15. Prediction of pancreatic and lung cancer from metabolomics data
- Abstract
- Chapter outline
- Prelude
- Purpose of this chapter
- Introduction
- Methods
- Results
- Discussion
- Conclusions
- How this chapter facilitates patient-centric healthcare
- Postscript
- References
- Chapter 16. Covid-19 descriptive analytics visualization of pandemic and hospitalization data
- Abstract
- Chapter outline
- Preamble
- Introduction
- 3 KNIME workflow data streams
- Preparatory steps for using this tutorial
- General introduction to KNIME
- Data access—the file reader node
- Data understanding
- Country selection
- Visualization data stream
- Using the workflow for another country
- How this chapter facilitates patient-centric healthcare
- Postscript
- Further reading
- Chapter 17. Disseminated intravascular coagulation predictive analytics with pediatric ICU admissions
- Abstract
- Chapter outline
- Prelude
- Introduction
- Background (from first edition)
- The example
- Data files
- First week of analysis
- Data mining recipes using statistica
- Data imputation
- Using the 11,459 imputed file—training data
- Training data (11,569 imputed) continued
- A problem
- Randomly separating the data and new data mining recipe
- Final analysis—a return to the past
- Conclusion—personal ending thoughts
- Postscript
- References
- Prologue to Part IV
- Part IV: Advanced topics in administration and delivery of health care including practical predictive analytics for medicine in the future
- Chapter 18. Challenges for healthcare administration and delivery: integrating predictive and prescriptive modeling into personalized–precision healthcare
- Abstract
- Chapter outline
- Prelude
- Introduction to challenges in healthcare delivery
- Postscript
- References
- Further reading
- Chapter 19. Challenges of medical research in incorporating modern data analytics in studies
- Abstract
- Chapter outline
- Prelude
- Introduction—challenges to medical researchers
- Trends that we might want to know about
- Automation and machine learning (AutoML)
- Blockchain
- Conversational artificial intelligence
- Digital twins
- Medical competitions
- Conclusion
- Postscript
- References
- Further reading
- Chapter 20. The nature of insight from data and implications for automated decisioning: predictive and prescriptive models, decisions, and actions
- Abstract
- Chapter outline
- Prelude
- Overview
- The purpose of this chapter
- The nature of insight and expertise
- Statistical analysis versus pattern recognition
- Explainability of artificial intelligence/machine learning models
- Summary
- Postscript
- References
- Chapter 21. Model management and ModelOps: managing an artificial intelligence-driven enterprise
- Abstract
- Chapter outline
- Prelude
- Introduction
- The model building/authoring life cycle
- Overview: managing the life cycles for thousands of models
- ModelOps details: managing model pipelines and reusable steps
- Efficiency, agility, elasticity, and technology
- Conclusion
- Postscript
- References
- Further reading
- Chapter 22. The forecasts for advances in predictive and prescriptive analytics and related technologies for the year 2022 and beyond
- Abstract
- Chapter outline
- Prelude
- Section I: specific technological trends predicted for 2022–2023+
- Part I—healthcare: what trends can we expect in the year 2022 and beyond?
- What do these three things mean?
- Part 2—In general: PA and business intelligence trends for 2022
- TOP 10 analytics and business intelligence trends for 2022
- Key artificial intelligence and data analytics trends for 2022 and beyond
- Section II: overriding philosophies which will guide trends over the next 10 years
- Postscript
- References
- Chapter 23. Sampling and data analysis: variability in data may be a better predictor than exact data points with many kinds of Medical situations
- Abstract
- Chapter outline
- Prelude
- Sampling and data analysis issues
- One issue—electronic health record and specific measures taken on patients
- Pulse oximetry data measurements, as an example
- Eye-intraocular pressure measurements: a personal example by one of the authors to illustrate the problem of when and how data is collected
- Types of data analysis that may be helpful in solving the types of issues presented in this chapter
- Clinical Dx and treatment needed changes for true patient-centered care
- Postscript
- References
- Further reading
- Chapter 24. Analytics architectures for the 21st century
- Abstract
- Chapter outline
- Prelude
- Introduction
- Organizational design for success
- Data design for success
- Brief considerations in data architecture
- Analytics design for success
- Conclusion
- Postscript
- References
- Chapter 25. Predictive models versus prescriptive models; causal inference and Bayesian networks
- Abstract
- Chapter outline
- Prelude
- Introduction
- Classification of AI and ML models in medicine
- Causation—the most misunderstood concept in data science today
- Causal inference and why it is important
- A summary example of causal modeling
- Conclusion
- Postscript
- References
- Further reading
- Chapter 26. The future: 21st century healthcare and wellness in the digital age
- Abstract
- Chapter outline
- Prelude
- Overview
- Background and need for change
- Listing of other e-items in this “outside of healthcare facilities” category but within at least the partial control of patients
- Examples of wearable devices that are working for people today
- Trends and expectations for the future of health IT and analytics
- Bottom-Up “small-sized” but working individually controlled data gathering and instant analytics output systems
- Final concluding statements
- Patient-centered (precision) health for the future
- References
- Further reading
- Appendix A. Modeling new COVID-19 deaths
- Introduction
- Processing steps in this workflow
- Summary
- Index
- Edition: 2
- Published: February 8, 2023
- Imprint: Academic Press
- No. of pages: 576
- Language: English
- Hardback ISBN: 9780323952743
- eBook ISBN: 9780323952750
GM
Gary D. Miner
LM
Linda A. Miner
SB
Scott Burk
MG
Mitchell Goldstein
RN
Robert Nisbet
NW
Nephi Walton
TH