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
Published on 8. February 2023
576 pages
978-0-323-95275-0 (ISBN)
Description
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
More details
Other editions
Additional editions
Gary D. Miner | Linda A. Miner | Scott Burk
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
Book
05/2023
2nd Edition
Academic Press
€113.50
Shipment within 10-15 days
Persons
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 (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'.
Content
Part I: Historical Perspective and the Issues of Concern for Health Care Delivery in the 21st Century1. History of Medical Health Care Delivery & Basic Medical Research2. "Things That Matter !!!" - Why This Book?3. Biomedical Informatics4. Access to Data for Analytics - the 'Biggest Issue' in Medical and Healthcare Predictive Analytics5. Regulatory Measures - Agencies, and Data Issues in Medicine and Healthcare6. Personalized Medicine7. Patient-Directed Healthcare8. OMICS or MULTIOMICS9. Challenges and Considerations of AI and GenomicsPart II: Practical Step-by-Step Tutorials and Case StudiesTUTORIAL A Case Study: Imputing Medical Specialty Using Data Mining ModelsTUTORIAL AA: VOC for Cancer Detection / PredictionTUTORIAL B Case Study: Using Association Rules of Investigate Characteristics of Hospital Readmissions TUTORIAL BB Case Study: COVID-19 Descriptive Analysis Around the WorldTUTORIAL C Constructing Decision Trees for Medicare Claims Using R and RattleTUTORIAL D Predictive and Prescriptive Analytics for Optimal Decisioning: Hospital Readmission Risk MitigationTUTORIAL E Obesity Group: Predicting Medicine and Conditions That Achieved the Greatest Weight Loss in a Group of Obese/Morbidly Obese PatientsTUTORIAL F1 Obesity Individual: Predicting Best Treatment or an Individual from Portal Data at a ClinicTUTORIAL F2 Obesity Individual: Automatic Binning of Continuous Variables and WoE to Produce a Better Model than the "Hand Binned" Stepwise Regression ModelTUTORIAL G Resiliency Study for First- and Second-Year Medical ResidentsTUTORIAL H Medicare Enrollment Analysis Using Visual Data MiningTUTORIAL I Case Study: Detection of Stress-Induced Ischemia in Patients with Chest Pain "Rule-Out ACS" ProtocolTUTORIAL J1 Predicting Survival or Mortality for Patients with Disseminated Intravascular Coagulation and/or Critical illnessesTUTORIAL J2 Decisioning for DICTUTORIAL K Predicting Allergy SymptomsTUTORIAL L Exploring Discrete Database Networks of TriCare Health Data Using R and ShinyTUTORIAL M Schistosomiasis Data from WHOTUTORIAL N The Poland Medical BundleTUTORIAL O Medical Advice Acceptance PredictionTUTORIAL P Using Neural Network Analysis to Assist in Classifying Neuropsychological DataTUTORIAL 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 ActTUTORIAL S Availability of Hospital Beds for Newly Admitted Patients: The Impact of Environmental Services on Hospital ThroughputTUTORIAL 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 MachinesTUTORIAL V Heart Disease: Evaluating Variables That Might Have an Effect on Cholesterol Level (Using Recode of Variables Function) TUTORIAL W Blood Pressure Predictive FactorsTUTORIAL X Gene Search and the Related Risk Estimates: A Statistical Analysis of Prostate Cancer DataTUTORIAL Y Ovarian Cancer Prediction via Proteomic Mass SpectrometryTUTORIAL Z Influence of Stent Vendor Representatives in the Catheterization LabPart III: Practical Solutions and Advanced Topics in Administration and Delivery of Health Care Including Practical Predictive Analytics for Medicine1. Challenges for Healthcare Administration and Delivery: Integrating Predictive and Prescriptive Modeling into Personalized Health Care2. Challenges of Medical Research for the Remainder of the 21st Century3. Introduction to the Cornerstone Chapters of this Book, Chapters 12 -15: The "Three Processes": Quality Control, Predictive Analytics, and Decisioning4. The Nature of Insight from Data and Implications for Automated Decisioning: Predictive and Prescriptive Models, Decisions, and Actions5. Decisioning Systems (Platforms) Coupled with Predictive Analytics in a Real Hospital Setting - A Model for the World6.
