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

Practical Data Analytics for Innovation in Medicine (eBook, ePUB)

Building Real Predictive and Prescriptive Models in Personalized Healthcare and Medical Research Using AI, ML, and Related Technologies

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 576
• Erscheinungstermin: 8. Februar 2023
• Englisch
• ISBN-13: 9780323952750
• Artikelnr.: 67425990

Autorenporträt

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'.

Inhaltsangabe

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