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
Academic, industry and medical researchers in medical informatics and health data analytics; practitioners, clinicians; and healthcare workers. Policy makers; IT professionals; data scientists
Table of Contents
Part I: Historical Perspective and the Issues of Concern for Health Care Delivery in the 21st Century 1. History of Medical Health Care Delivery & Basic Medical Research 2. "Things That Matter !!!" - Why This Book? 3. Biomedical Informatics 4. Access to Data for Analytics – the ‘Biggest Issue’ in Medical and Healthcare Predictive Analytics 5. Regulatory Measures – Agencies, and Data Issues in Medicine and Healthcare 6. Personalized Medicine 7. Patient-Directed Healthcare 8. OMICS or MULTIOMICS 9. Challenges and Considerations of AI and Genomics
Part II: Practical Step-by-Step Tutorials and Case Studies TUTORIAL A Case Study: Imputing Medical Specialty Using Data Mining Models TUTORIAL AA: VOC for Cancer Detection / Prediction TUTORIAL B Case Study: Using Association Rules of Investigate Characteristics of Hospital Readmissions TUTORIAL BB Case Study: COVID-19 Descriptive Analysis Around the World TUTORIAL C Constructing Decision Trees for Medicare Claims Using R and Rattle TUTORIAL D Predictive and Prescriptive Analytics for Optimal Decisioning: Hospital Readmission Risk Mitigation TUTORIAL E Obesity Group: Predicting Medicine and Conditions That Achieved the Greatest Weight Loss in a Group of Obese/Morbidly Obese Patients TUTORIAL F1 Obesity Individual: Predicting Best Treatment or an Individual from Portal Data at a Clinic TUTORIAL F2 Obesity Individual: Automatic Binning of Continuous Variables and WoE to Produce a Better Model than the "Hand Binned" Stepwise Regression Model TUTORIAL G Resiliency Study for First- and Second-Year Medical Residents TUTORIAL H Medicare Enrollment Analysis Using Visual Data Mining TUTORIAL I Case Study: Detection of Stress-Induced Ischemia in Patients with Chest Pain "Rule-Out ACS" Protocol TUTORIAL J1 Predicting Survival or Mortality for Patients with Disseminated Intravascular Coagulation and/or Critical illnesses TUTORIAL J2 Decisioning for DIC TUTORIAL K Predicting Allergy Symptoms TUTORIAL L Exploring Discrete Database Networks of TriCare Health Data Using R and Shiny TUTORIAL M Schistosomiasis Data from WHO TUTORIAL N The Poland Medical Bundle TUTORIAL O Medical Advice Acceptance Prediction TUTORIAL P Using Neural Network Analysis to Assist in Classifying Neuropsychological Data TUTORIAL Q Developing Interactive Decision Trees using Inpatient Claims (with SAS Enterprise Miner) TUTORIAL R Divining Healthcare Charges for Optimal Health Benefits Under the Affordable Care Act TUTORIAL S Availability of Hospital Beds for Newly Admitted Patients: The Impact of Environmental Services on Hospital Throughput TUTORIAL T Predicting Vascular Thrombosis: Comparing Predictive Analytic Models and Building an Ensemble Model for "Best Prediction" TUTORIAL U Predicting Breast Cancer Diagnosis Using Support Vector Machines TUTORIAL V Heart Disease: Evaluating Variables That Might Have an Effect on Cholesterol Level (Using Recode of Variables Function) TUTORIAL W Blood Pressure Predictive Factors TUTORIAL X Gene Search and the Related Risk Estimates: A Statistical Analysis of Prostate Cancer Data TUTORIAL Y Ovarian Cancer Prediction via Proteomic Mass Spectrometry TUTORIAL Z Influence of Stent Vendor Representatives in the Catheterization Lab
Part III: Practical Solutions and Advanced Topics in Administration and Delivery of Health Care Including Practical Predictive Analytics for Medicine 1. Challenges for Healthcare Administration and Delivery: Integrating Predictive and Prescriptive Modeling into Personalized Health Care 2. Challenges of Medical Research for the Remainder of the 21st Century 3. Introduction to the Cornerstone Chapters of this Book, Chapters 12 -15: The "Three Processes": Quality Control, Predictive Analytics, and Decisioning 4. The Nature of Insight from Data and Implications for Automated Decisioning: Predictive and Prescriptive Models, Decisions, and Actions 5. Decisioning Systems (Platforms) Coupled with Predictive Analytics in a Real Hospital Setting - A Model for the World 6. The Latest in Predictive and Prescriptive Analytics 7. The Coming Standard for a Data Model – OMOP (Observational Medical Outcomes Partnership) as per Observational Health Data Sciences and Informatics (OHDS) at University of California-Irvine 8. A Real Case Study of GLAUCOMA (eye disease) and suggested PREDICTIVE MODELING for identifying individual patient predictions of best treatment with high accuracy 9. Analytics Architectures for the 21st Century 10. Causation and How This ‘Cutting Edge Concept’ Works with Predictive Analytics and Prescriptive Analytics (Decisioning) 11. 21st Century Healthcare and Wellness: Getting the Health Care Delivery System That Meets Global Needs
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. 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 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
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
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 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
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.