Modernizing Medical Research: AI and Medical Records

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Modernizing Medical Research: AI and Medical Records, by Bill Inmon, David Rapien, and Sylvia Sydow, MD

Unlock the hidden potential of medical research with cutting-edge data analytics—discover how structured and unstructured (textual) medical data can revolutionize patient care, drive groundbreaking discoveries, and transform the future of healthcare. 

Topics

Chapter 1: The Need for Modernizing Medical Research

Unlocking the treasure trove

Research and medical records

The challenges of looking at a population

Text as an obstacle

Textual disambiguation

A step by step approach

Heuristic analysis

From ah? to aha!

The need for speed

Obstacles to achieving speed of analysis

Autonomy of analysis

In summary


Chapter 2: Taxonomies and Ontologies

Taxonomies

Relevance

Completeness of the taxonomy

Unique identification

Medical taxonomies

How are taxonomies used?

A real world, external foundation

The size of the taxonomy

Periodic maintenance

Ontologies

In summary


Chapter 3: Ingesting Text

Privacy and the law

Storage media

Images

Medical record selection

Medical record structure

Managing volumes of data

Different data formats

The Internet

In print

Spreadsheets

On a voice recording

Email

In summary


Chapter 4: Research Journals and Clinical Trials

Medical journals

Clinical trials and patient records

Text and clinical trials

The structure of the study

The number of studies/clinical trials

Reading the entire document

Resolving terminology differences

In summary


Chapter 5: Unification of Text

Encoding

Measurement

Language

Common structure

Spelling

Grouping similar objects

Acronyms

Homographs

Calculations

Further elucidation

Formatting adjustments

Different name for the same object

In summary


Chapter 6: Building the Database

The basic interface

Simple questions

The Datavox database

Adjusting the vocabulary

A closer look at the output data

In summary


Chapter 7: Exploration

Exploration and clinical trials

Computer systemization

The potential of no resolution

Lessons learned from an exploration

Different approaches to exploration

Scatter diagrams

Correlation

A Pearson coefficient matrix

The need for speed

In summary


Chapter 8: Analytics

Many forms of analysis

Operating from the same database

Textual database versus a structured database

Different types of analytical tools

Spreadsheet data analysis – Excel

Genai based query suite – Thoughtspot

Knowledge graphs – Neo4j

Dashboards – Tableau


Chapter 9: Analytics on Structured and Textual Data

Analytics in the structured environment

Analytics on both structured and textual data

Different organizations of data

Intersecting data

Connectors

Stable/unstable connectors

Types of relationships

No relationship

Universal relationship

Direct relationship

Indirect relationship

Classification-based relationship

Different connections

An example of blending textual and structured data

In summary


Chapter 10: The Analytical Lifecycle

A “normal” procedure

Flexibility

Length of time

Speed of textual ETL

Attitude

Reconsidering the study

Medical research is at a crossroads—traditional methods of analyzing patient records, clinical trials, and research journals are no longer enough to keep pace with the rapid evolution of medicine. Modernizing Medical Research unveils a revolutionary approach that bridges the gap between raw medical text and structured databases, enabling researchers to extract critical insights with unprecedented speed and accuracy. Whether you’re a healthcare professional, data scientist, or medical researcher, this book provides the key to unlocking a new era of medical discovery.

At the heart of modern medical research lies an untapped goldmine: unstructured text. Millions of patient records, research papers, and clinical trial reports contain invaluable information, yet much of it remains inaccessible due to the limitations of traditional database analysis. This book presents a groundbreaking methodology for converting raw text into structured data, making it possible to analyze vast datasets efficiently and uncover patterns that were previously impossible to detect.

This book combines technical expertise with real-world medical applications. It delves into crucial topics such as text ingestion, taxonomies, ontologies, and heuristic analysis, providing a roadmap for researchers and analysts to leverage artificial intelligence, machine learning, and natural language processing in the pursuit of medical advancements.

One of the greatest challenges in healthcare analytics is overcoming the complexity and ambiguity of medical text. Modernizing Medical Research explains how textual contextualization—an advanced Extract, Transform, and Load (ETL) technique—can convert messy, unstructured medical data into structured databases ready for analysis. The result? Faster, more reliable insights that can drive better clinical decisions, improve patient outcomes, and accelerate medical breakthroughs.

The book also explores how structured databases enable large-scale population studies, revealing trends and correlations that individual case studies cannot capture. From early disease detection to the identification of treatment effectiveness, these analytical techniques have the potential to reshape medical research and usher in an era of precision medicine. Researchers will learn how to efficiently organize and analyze vast amounts of medical information, leading to evidence-based practices that can improve healthcare globally.

A must-read for healthcare and information technology professionals, this book offers a practical and highly accessible guide to implementing modern data techniques in medical research. It details step-by-step processes for handling large datasets, integrating structured and unstructured data, and applying AI-driven analytics to uncover hidden relationships in medical records.

For those working with clinical trials and medical journals, Modernizing Medical Research demonstrates how computational tools can enhance study design, streamline data extraction, and improve the reliability of research findings. By providing concrete examples and real-world case studies, the authors illustrate how modern analytics can reduce research bottlenecks and speed up the journey from hypothesis to discovery.

In an age where healthcare data is expanding at an exponential rate, traditional research methods are no longer sufficient. This book presents a forward-thinking, innovative approach that will empower medical researchers, healthcare organizations, and policymakers to harness the full power of data-driven insights. The future of medicine lies in our ability to process and analyze vast amounts of information quickly—and Modernizing Medical Research shows exactly how to make that future a reality.

If you’re ready to take medical research to the next level—combining the power of big data, artificial intelligence, and healthcare analytics—then this book is your ultimate guide. Modernizing Medical Research is essential for anyone looking to transform how we understand, analyze, and advance medical science in the digital age.

About Bill, Dave, and Sylvia

About Bill:

Bill Inmon, the “father of the data warehouse,” has written 60 books published in nine languages. ComputerWorld named Bill one of the ten most influential people in the history of the computer profession. Bill’s latest adventure is the building of technology known as textual disambiguation.

 

About Dave:

David Rapien is an Associate Professor – Educator of Information Systems and Business Analytics at the University of Cincinnati’s Lindner College of Business. Along with teaching for over 25 years, Dave has developed and managed data integration systems in the Sports Management, Medical, Insurance, Banking, Legal, Horse Racing, and School Administration industries. David owes his success to his wife, Laura, and his four children for their constant support, and to his dad, for teaching him how to explain complex ideas in simple terms.

 

About Sylvia:

Sylvia Sydow is a graduate of the University of Nebraska Medical School and is a retired board certified emergency medicine physician. Her retirement years are filled with her interest in gardening and raising Scottish terriers.

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