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

Format:

Book

Author/Creator:

Miner, Gary D., author.

Language:

English

Subjects (All):

Integrated delivery of health care.

Machine learning.

Medical care--Data processing.

Medical care.

Artificial intelligence--Medical applications.

Artificial intelligence.

Medical Informatics Computing.

Big Data.

Data Science.

Data Analysis.

Artificial Intelligence.

Forecasting.

Medical Subjects:

Medical Informatics Computing.

Big Data.

Data Science.

Data Analysis.

Artificial Intelligence.

Forecasting.

Physical Description:

1 online resource (578 pages)

Edition:

Second edition.

Place of Publication:

London, England : Academic Press, 2023.

Summary:

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.

Contents:

Front Cover

Practical Data Analytics for Innovation in Medicine

Copyright Page

Dedication

Contents

About the authors

Foreword for the 2nd edition-John Halamka

Foreword for the 1st edition by Thomas H. Davenport

Foreword for the 1st edition by James Taylor

Foreword for the 1st edition by John Halamka

Preface and overview for the 2nd edition

Preface to the 1st edition

Modern medicine: an exercise in prediction and preparation

Wasted costs in US healthcare systems

References

Acknowledgment

Guest Chapter Author's Listing

Guest - Authors

Endorsements and reviewer Blurbs-from the 1st edition

Instructions for using software for the tutorials-how to download from web pages-for the 2nd edition

Prologue to Part I

I. Historical perspective and the issues of concern for health care delivery in the 21st century

1 What we want to accomplish with this second edition of our first "Big Green Book"

Prelude

Purpose/summary

First reasons for our writing this book

Highlighted new material

Descriptive statistics, data organization, and example

Randomized controlled trials

From observation to randomized controlled trials-eliminating bias

Basic predictive analytics and example

Example

Research standards common to both traditional and predictive analytics

Pandemic as related to research standards and accurate data

Especially for the second edition

Chapter conclusion

Postscript

2 History of predictive analytics in medicine and healthcare

Outline

Introduction

Part I. Development of bodies of medical knowledge

Earliest medical records in ancient cultures

The oldest official medical documents

Classification of medical practice among ancient and modern cultures.

Medical practice documents in major world cultures of Europe and the Middle East

Egypt

Mesopotamia

Greece

Medicine in Preclassical Greece

Hippocrates and classical Greece

Ancient Rome

Galen

Arabia

Summary of royal medical documentation in ancient cultures

Effects of the middle ages on medical documentation

Rebirth of Interest in medical documentation during the renaissance

The printing press

The Protestant Reformation

Erasmus

Human anatomy

Andreas Vesalius (1514-1564)

William Harvey (1578-1657)

Medical documentation after the enlightenment

Medical case documentation

The development of the National Library of Medicine

Part II. Analytical decision systems in medicine and healthcare

Computers and medical databases

Early medical databases

Medical literature databases

National Library of Medicine list of online medical databases

Other medical research databases

Bills of Mortality in London, United Kingdom

Best practice guidelines

Guidelines of the American Academy of Neurology

Medical records move into the digital world

Healthcare data systems

3 Bioinformatics*

The rise of predictive analytics in healthcare

Moving from reactive to proactive response in healthcare

Medicine and big data

An approach to predictive analytics projects

The predictive analytics process in healthcare

Process steps in Fig. 3.1

Step 1. Problem definition

Step 2. Identify available data sources

Step 3. Formulate a hypothesis

Step 4. Data preprocessing

Step 5. Data set design

Step 6. Feature selection

Step 7. Model building

Step 8. Model evaluation

Step 9. Model implementation

Step 10. Validation of clinical utility

Translational bioinformatics

Clinical decision support systems.

Hybrid clinical decision support systems

Consumer health informatics

Patient-focused informatics

Health literacy

Consumer education

Direct-to-consumer genetic testing

Use of predictive analytics to avoid an undesirable future

Consumer health kiosks

Who uses the Internet? Nearly everybody

Patient monitoring systems

Applications for predictive analytics in intensive care unit patient monitoring systems

Challenges of medical devices in the intensive care unit

Public health informatics

The major problem: lack of resources

Social networks and the "Pulse" of public health

Predictive analytics and prevention and disease and injury

Biosurveillance

Food-borne illness

Medical imaging

Clinical research informatics

Intelligent search engines

Personalized medicine

Hospital optimization

Challenges

Data storage volumes

Data privacy and security

Standards and consistency of data

Interpretability of models

Evidence-based guidelines and adoption of PA models

Portability of PA models

Regulation of PA models

Summary

Further reading

4 Data and process models in medical informatics

Chapter purpose

Systems for classification of diseases and mortality

Bills of mortality

The ICD system

The OMOP common data model

Reasons for OMOP

The OMOP CDM provides a common data format

OMOP CDM architecture is patient-centric

Additional data processing operations necessary to serve the analysis of OMOP data

The CRISP-DM processing model

CRISP-DM phases

How this chapter facilitates patient-centric healthcare

5 Access to data for analytics-the "Biggest Issue" in medical and healthcare predictive analytics

Prelude.

Size of data in our world: estimated digital universe now and in the future

Convergence of healthcare and modern technologies

Reasons why healthcare data is difficult to get and difficult to measure

Multiple places where medical data are found

Many different formats of medical data: structured and unstructured

Another problem is inconsistent definitions

Changing government regulatory requirements keep changing what data is taken and kept

What are some of the benefits of using good data analytics in medical research and healthcare delivery?

Conclusion of Chapter 5: the importance of health care data analytics

6 Precision (personalized) medicine

Preamble

What is personalized/precision medicine?

Personalized medicine versus precision medicine

P4 medicine

P5 to P10 medicine

Precision medicine, genomics, and pharmacogenomics

Differences among us

Differences go beyond our body and into our environment

Changes from birth to death

Ancestry and disease

Gene therapies

It is not about just our genome

Changing the definition of diseases

Systems biology

Efficacy of current methods-why we need personalized medicine

Predictive analytics in personalized medicine

The future: predictive and prescriptive medicine

Application of predictive analytics and decisioning in predictive and prescriptive medicine

The diversity of available healthcare data

Diversity of data types available

Phenotypic data

Clinical information

Real-time physiological data

Imaging data

Genomic data

DNA-the center piece of heredity and bodily differences

DNA replication and mutation

Somatic mutations

Germline mutations

The personal genome project

The Electronic Medical Records and Genomics network.

The Patient-Centered Outcomes Research Institute

Transcriptomics data

Epigenomics data

Proteomic data

Glycomic data

Metabolomic data

Metagenomic data

Nutrigenomics data

Behavioral measures data

Socioeconomic status data

Personal activity monitoring data

Climatological data

Environmental data

All the other OMICs

The future

Challenge #1

Challenge #2

Challenge #3

Challenge #4

Challenge #5

Challenge #6

Challenge #7

Challenge #8

Challenge #9

Challenge #10

Challenge #11

Challenge #12

Challenge #13

7 Patient-directed healthcare

Empowerment in patient-directed medicine

Self-monitoring, N of 1 study

Research questions

The responsible patient

Patients changing how medicine is practiced

Patient empowerment versus compliance

Collaboration between patients and the medical community

Patient involvement

Patient involvement in medical education

Limitations of patient involvement

Evidence supporting patient involvement

Family-wise statistical errors

Communication and trust

Communication and trust during the pandemic

Collaboration and limitations

How patient-directed medicine works using predictive analytics

Privacy concerns can hinder research

Predictive analytics for patient-directed research

Cultures and decisions

Coordination of care and communication for patient-directed healthcare

Communication skills in the medical setting

Communication studies

Barriers to productive communication

Patients selecting their best models of care

Medical homes

The integrated healthcare delivery system model

Comparison with accountable care organization

Direct pay/direct care model

Consumerism and advertising in patient-directed healthcare.

Advertising to patients.

Notes:

Includes bibliographical references and index.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Miner, Gary D. Practical Data Analytics for Innovation in Medicine

ISBN:

0-323-95275-5

OCLC:

1370493372