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 /: building real predictive and prescriptive models in personalized healthcare and medical research using AI, ML, and related technologies. (2022)

• Record Type: Book
• Further Information: Note: Gary Miner [and six others].
• Authors: Miner, Gary; Miner, Linda A; Burk, Scott; Goldstein, Mitchell; Nisbet, Robert; Walton, Nephi; Hill, Thomas
• 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
• Edition: Second edition
• Publisher Details: Amsterdam : Academic Press
• Publication Date: 2022
• Extent: 1 online resource
• Subjects: 610.28563; Personalized medicine; Integrated delivery of health care; Medical care -- Data processing; Medical technology; Artificial intelligence; Machine learning
• Languages: English
• ISBNs: 9780323952750
• Related ISBNs: 9780323952743
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.