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
Authors: Gary D. Miner, Linda A. Miner, Scott Burk, Mitchell Goldstein, Robert Nisbet, Nephi Walton, Thomas Hill
Language: English
Hardback ISBN:
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eBook ISBN:
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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.

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

Gary D. Miner

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’.

Affiliations and expertise

CEO, M&M Predictive Analytics LLC; UCI Adjunct Professor for Continuing Education, Predictive Analytics Program; Associate Editor, The Journal of Geriatric Psychiatry and Neurology; Private Consulting, Tulsa, OK, USA

Linda A. Miner

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.

Affiliations and expertise

Professor Emeritus and Director of Academic Programs, Southern Nazarene University; Editorial Board, The Journal of Geriatric Psychiatry and Neurology; Private Consulting, Tulsa, OK, USA

Scott Burk

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

Affiliations and expertise

Chief Data Officer, Data Scientist, Architect & Thought Leader TIBCO; Professor, Data Mining and Predictive Analytics, New York University; Temple, TX, USA

Mitchell Goldstein

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.

Affiliations and expertise

Professor of Pediatrics, Division of Neonatology, Director Neonatal ECMO Program; Loma Linda School of Medicine, Loma Linda University Children’s Hospital; Loma Linda, CA, USA

Robert Nisbet

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.

Affiliations and expertise

Researcher-Medical Informatics, H.H. Chao Comprehensive Digestive Disease Center, University of California Irvine Medical Center, Private Consulting, Santa Barbara, CA, USA

Nephi Walton

Nephi Walton MD, MS, FACMG, FAMIA earned his MD from the University of Utah School of Medicine and a Masters degree in Biomedical Informatics from the University of Utah Department of Biomedical Informatics where he was a National Library of Medicine fellow. His Masters work was focused on data mining and predictive analytics of viral epidemics and their impact on hospitals. He was the winner of the 2009 AMIA Data Mining Competition and has published papers and co-authored books on data mining and predictive analytics. Also during his time at the University of Utah he spent several years studying genetic epidemiology of autoimmune disease and the application of analytical methods to determining genetic risk for disease, a work that continues today. His work has included several interactive medical education products. He founded a company called Brainspin that continues this work and has won international awards for innovative design in this area. He is currently a combined Pediatrics/Genetics fellow at Washington University where he is pursuing several research interests including the application of predictive analytics models to genomic data and integration of genomic data into the medical record. He continues to work with the University of Utah and Intermountain Healthcare to further his work in viral prediction models and hospital census prediction and resource allocation models.

Affiliations and expertise

Associate Medical Director, Intermountain Healthcare, Precision Genomics, Salt Lake City, UT, USA

Thomas Hill

Dr. Thomas Hill is Senior Director for Advanced Analytics (Statistica products) in the TIBCO Analytics group. He previously held positions as Executive Director for Analytics at Statistica, within Quest's and at Dell's Information Management Group. He was a Co-founder and Senior Vice President for Analytic Solutions for over 20 years at StatSoft Inc. until the acquisition by Dell in 2014. At StatSoft, he was responsible for building out Statistica into a leading analytics platform. Dr. Hill received his Vordiplom in psychology from Kiel University in Germany, earned an M.S. in industrial psychology and a Ph.D. in psychology from the University of Kansas. He was on the faculty of the University of Tulsa from 1984 to 2009, where he conducted research in cognitive science and taught data analysis and data mining courses. He has received numerous academic grants and awards from the National Science Foundation, the National Institute of Health, the Center for Innovation Management, the Electric Power Research Institute, and other institutions. Over the past 20 years, his team has completed diverse consulting projects with companies from practically all industries in the United States and internationally on identifying and refining effective data mining and predictive modeling / analytics solutions for diverse applications. Dr. Hill has published widely on innovative applications for data mining and predictive analytics. He is the author (with Paul Lewicki, 2005) of Statistics: Methods and Applications, the Electronic Statistics Textbook (a popular on-line resource on statistics and data mining), a co-author of Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications (2012) and Practical Predictive Analytics and Decisioning Systems for Medicine (2014); he is also a contributing author to the popular Handbook of Statistical Analysis and Data Mining Applications (2009). Dr. Hill also authored numerous patents related to data science, Machine Learning, and specialized applications of of analytics to various domains.

Affiliations and expertise

StatSoft, Inc., Tulsa, OK, USA

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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

Purchase options

Innovate. Sustain. Transform.

Institutional subscription on ScienceDirect

Gary D. Miner

Linda A. Miner

Scott Burk

Mitchell Goldstein

Robert Nisbet

Nephi Walton

Thomas Hill

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