MEDICAL DATA MINING

Timothy Hays, PhD

Health IT Strategy Executive

Dynamics Research Corporation (DRC)

December 13, 2012
Is a VERY Large Domain with 

Enormous Opportunities for Data Mining

• US Healthcare (2009)

• $2.5 Trillion

• 17.3% of GDP

• Healthcare system: 

• Providers, Payers and Patients, 

• Government (Federal and State) and Private/Commercial, 

• Research to (Best) Practice, 

• Regulations, Laws, and Policies (i.e. Affordable Care Act, etc.)

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Healthcare in America
• Patients and Consumers

• Providers: Government, Private or 

Commercial, hospitals, pharmacies, clinics, 

doctors’ offices, and other provider services

• Payers: employers, insurance carriers, other 

third-party payers, health plan sponsors 

(employers, unions, DOD, VA, HHS, etc.)

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Healthcare / Medical Data Mining

Areas where data mining can help! 
• Healthcare crosscuts: 

• Regulatory: laws, regulations, coding, guidelines, best 

practices, performance, costs, reporting (i.e., adverse 

event), etc.

• Research: basic research, pharmaceuticals, medical 

devices, genetics, drug-drug interaction, diagnostic test 

decision support, biomedical research data mining (basic 

or clinical results), etc.

• IT systems: interoperability, software development, 

information/data storage, security and access, reporting, 

“Big Data” and small data, usability, data transfer, 

training/educating/communicating, and so much more!

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Healthcare / Medical Data Mining

Areas where data mining can help! 
So How Do We Get There?

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Understanding and Then 

Tackling the Pieces!
Medical Data Mining

Where are the opportunities?

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Practice domains / Fields we can learn from:

• Knowledge management is the theory behind knowledge capture 

and use

• Informatics is the science of information, the practice of information 

processing, and the engineering of information systems

• Analytics is the practical application of tools (i.e. algorithms) upon 

information to gain new insights. 

• Others:

• Business Intelligence

• Competitive Intelligence

• Computational Science

• Bioinformatics

• Health Informatics

• Predictive Modeling

• Decision Support

• Artificial Intelligence, etc.

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Build On History and Knowledge
Effective Use of Data, Tools, and Analyses

Data

Decisions, 

Plans, Actions

Information

Knowledge

Unstructured, structured,

Multiple sources

Data organized into 

meaningful patterns

The application and productive 

use of information

Discovery and Analysis

Data Mining

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 Leads to:

• Question based answers

• Anomaly based discovery

• New Knowledge discovery

• Informed decisions

• Probability measures

• Predictive modeling

• Decision support

• Improved health

• Personalized medicine

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Medical Data Mining
• What questions are you trying to answer?

• Ultimately, identify answers to questions we didn’t know we had

• Do you have data, tools and analyses to answer the 

question?

• Example areas:

• Healthcare management (provider care practices)

• Fraud and abuse

• Treatment effectiveness

• Patient involvement and relationship

So, Again, How Do We Get There?

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Understanding and Then Tackling the Pieces!
Dilbert on Data

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There are Exabyte’s of data!

But is it the right data to answer your question?
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So, Again, How Do We Get There?
Evolution of the Lung Cancer Portfolio: 2003-2009

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2008

2009

Abstracts

2003
• Data: the V’s (Volume, Velocity, Variety, Veracity, 

and Visibility)

• Tools/Applications: Search algorithms, text 

mining, natural language processing (NLP), 

machine learning, etc. clustering, predictive 

modeling, relationship and link analysis, taxonomy 

generation, statistical analysis, neural networks, 

visualization, heat maps, etc.

• Analysis: Methodologies (exploration and drill 

down) and subject matter/domain experts

So, Again, How Do We Get There?

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Understanding and Then Tackling the Pieces!
•The Data V’s

• Volume - large, small, combined, separate, etc.

• Velocity – transfer: capture and retrieval includes 

streaming, batch processing, utilization, etc.

• Variety - text [structured unstructured], images, 

audio, video, etc.

• Veracity - meaningfulness [use], value, variability, 

quality, etc.

• Visibility - access, security, 

Data

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Understanding and Then Tackling the Pieces!
•1000’s of Tools

•Integrated or add-on

•Question/Domain/Analysis specific

•Use with Repositories and Platforms

• Helpful but not necessary for analysis

• Relevant to data veracity/quality

Tools / Applications

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Understanding and Then Tackling the Pieces!
• Methodologies

• Research

• Algorithms

• Proprietary

•Subject matter experts

• Domain (healthcare centric) expertise

• Analysis expertise

• Tool and application experience

Analysis

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Understanding and Then Tackling the Pieces!
Medical Data Mining Spectrum

Reporting

Dashboards

Planning/Forecasting

Modeling/Predicting

Decision 

Support / 

Optimizing

Capability

Complexity

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Medical Data Mining

• It sounds good, but are there standards for data 

capture, use, definitions, sharing, etc.?

• Are standards needed (yet)?

• Are there sufficient tools, applications, analyses 

and staff available to identify valuable information 

to improve healthcare?

• Are there sufficient benefits and incentives in core 

areas where data mining is essential?

• Personalized and predictive medicine

• Fraud and abuse

• Research advancements

• Improved treatments and medical devices

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Dilbert on Federal & Corporate Realities

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An integrated approach is key!
NIH Research, Condition, and Disease Categorization 

Project
The purpose of the Research, Condition, and 

Disease Categorization (RCDC) project is to

1. Consistently categorize NIH-funded research 

projects according to research areas/categories 

2. Use an automated process, and 

3. Make the results available to Congress and the 

public

National Institutes of Health (NIH): 

Case Study

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How does it do that?

• RCDC system uses Elsevier’s Collexis technology to 

text mine biomedical concepts from research 

descriptions

• NIH research experts define a weighted classification 

system for each of 238 categories

• All NIH research is then categorized through an 

automated process

• The output is reported publicly at Report.NIH.Gov

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NIH: Case Study
The Research, Condition, and Disease 

Categorization (RCDC) project is an example of 

providing new, timely information out of 

unstructured and structured data

• RCDC pulls data from 7 databases (all containing 

well over 4 terabytes of content) that gets routed, 

compartmentalized, validated and ultimately used 

for regular reports and on-demand queries

• Allows research information to be explored 

proactively

• Is adaptable as 

1) Science evolves over time and 

2) Increased needs for data usage are identified

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NIH: Case Study
How Does RCDC Work?

Project Description

Category

Definition developed by 

NIH staff

Matching 

Process 

Matching compares individual project descriptions

to the category definition – the resultant category

matching score for a project is a reflection of how

 closely the project is related to a particular category

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Projects with 

matching 

categories

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Category Definition Created in the New System

 A definition is a list of scientific 

terms from a thesaurus (300,000 

terms and synonyms).

 Terms are selected by NIH 

Scientific Experts to define that 

research category. 

 Terms are weighted to fine-tune 

the matching process.

 Terms from grants/projects are 

matched against definitions to 

produce category project lists. 

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Analytical

Tool

Sample: Sleep Research Draft Fingerprint

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http://www.nih.gov/news/fundingresearchareas.htm

Prior View of Data (FY 2009)

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Summary 

level data

http://report.nih.gov/rcdc/categories/

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Drill down 

level data, 

not available 

on web in 

the past

http://report.nih.gov/rcdc/categories/

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Ahead of its time

• RCDC opened the door for analysis and review of 

research that was not previously possible

(including decision intelligence practices) 

• Additional uses of new analytical, visualization, and 

exploration technologies are now taking place 

because the platform exists!

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NIH: Case Study
Benefits

• Enhanced NIH’s ability to:

• Leverage existing information and processes

• Conduct text mining and perform scientific portfolio analysis

• Provide transparency into government spending on research

• Greatly improved process

• Consistent methodology (one definition per category)

• Reproducible numbers

• New open platform to support decision intelligence

• Improved public understanding of NIH spending

• Access to project listings not available previously

• Searchable, accessible query tools and reports

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Summary

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• The opportunity and future for Medical Data Mining is 

HUGE!

• Practice areas cover the landscape: Patient, Provider, 

Payer, Research, Regulatory and IT

• Tackle it in chucks!

• Question based data mining

• Don’t try to build the be-all end-all data source – use what’s 

available to begin to answer critical questions sooner rather than 

later

• Aspects of Data are critical

• The right Tool for the right job

• Analysis requires well trained analysts