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

Brings updated knowledge based on real-world examples on data analytics tools and systems to support the healthcare sector and medicine

Gary D. Miner (Author), Linda A. Miner (Author), Scott Burk (Author), Mitchell Goldstein (Author), Robert Nisbet (Author), Nephi Walton (Author), Thomas Hill (Author)

9780323952743, Elsevier Science

Hardback, published 6 May 2023

576 pages
27.6 x 21.6 x 3.3 cm, 1.81 kg

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.

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

Subject Areas: Medical bioinformatics [MBF]