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Incorporating Data Mining
Applications into Clinical Guidelines
Reza Sherafat
Dr. Kamran Sartipi
Department of Computing and Software
McMaster University, Canada
{sherafr, sartipi}@mcmaster.ca
Computer-based Medical Systems (CBMS ’06)
June 22, 2006
1
Outline
Decision making based on data mining
results
 Data and knowledge interoperability
 Knowledge management framework
 Tool implementation
 Conclusion

Integrating data mining applications into clinical guidelines
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Decision Making
Clinical

Practitioners
Decision
face
Support
criticalSystems
questions
(CDSS)
which
requires
decision
making:
– Computer
programs
– Provide
onlineofand
– The cause
a symptom
patient-specific
assistance
– Drug prescription
to health care professionals
– Treatment planning
to make better decisions
– Diagnosis of a disease
– … (many more)
– Clinical knowledge is stored
in a knowledge-base
Integrating data mining applications into clinical guidelines
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Data Mining Applications
in Health care
Patient
Integrating data mining applications into clinical guidelines
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Decision Logic
Condition
IF
the patient has had a heart stroke and is
above 50
THEN
Action
his health condition should be monitored!
Integrating data mining applications into clinical guidelines
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Decision Logic (cont’d)

Decision making logic:
– Logical expressions
 ‘If-then-else’ structures
– Test for conditions
– Trigger actions
if ( (patient.age > 50) &&
(patient.previous_heart_stroke == true)
)
then …
Integrating data mining applications into clinical guidelines
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Data Mining Decision Logic

Data mining
– Analysis and mining of data to extract hidden facts in
the data
– The extracted facts are represented in a data
structure called “data mining model”

Training vs. Application of a data mining model:
– Training the model: Building the model
– Application of the mode: interpreting for specific
patient data
Integrating data mining applications into clinical guidelines
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Data Mining Decision Logic (cont’d)

Classification: mapping data into predefined classes.
(e.g., whether a patient has a specific disease or not)

Regression: mapping a data item to a real-valued
prediction variable. (e.g., planning treatments.)

Clustering: To identify clusters of data items. (e.g., to
cluster patients based on risk factors.)

Association Rule Mining: to find hidden associations in
the data set (e.g., how different patient data are related
based on shared relations such as: “specific diseases”,
“patients habits”, or “family disease history”.)
Integrating data mining applications into clinical guidelines
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Data Mining Decision Logic (cont’d)

An example of regression model
[source:Otto,Pearlmen]
Vmax
3-4m/s
≥4m/s
≤ 3m/s
Doppler AVA
≤ 1 cm2
%100
%88
2-3+
%100
AVR recommended
1.1-1.6 cm2
AI severity
≥1.7 cm2
0-1+
%100
%100
%66
AVR not recommended
Integrating data mining applications into clinical guidelines
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Application of Data Mining Results

Predictive Model Markup Language (PMML):
– XML based specification
– Meta model: Define the data structure of the model
– Different types of data mining models (clustering,
classifications, …)
– Extendable for model specific constructs

Share, access, exchange PMML documents
Integrating data mining applications into clinical guidelines
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Proposed Health Care
Knowledge Management Framework
Phase 1: Build the data mining models
Knowledge Extraction
Guideline modeling
Guideline Execution
Integrating data mining applications into clinical guidelines
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Proposed Health Care
Knowledge Management Framework
Phase 2: Encode data and knowledge
Knowledge Extraction
Data and knowledge
interoperability
Guideline Execution
Integrating data mining applications into clinical guidelines
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Proposed Health Care
Knowledge Management Framework
Phase 3: Apply the knowledge for specific
patient data
Knowledge Extraction
Data and knowledge
interoperability
Knowledge Interpretation
Integrating data mining applications into clinical guidelines
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Data and Knowledge Interoperability

HL-7 Reference Information Model (RIM)

Clinical Document Architecture (CDA)
– An XML-based standard for defining structured templates for
clinical documents

Data
– A general high level health care data model
Standard Terminology Systems (UMLS, SNOMED CT, etc)

Predictive Model Markup Language (PMML)
– An XML-based standard for representing data mining results

Guideline Interchange Format 3 (GLIF3)
– A clinical guideline definition standard
Integrating data mining applications into clinical guidelines
Knowledge
– Standard clinical vocabulary sets
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Tool Implementation


A guideline execution engine based on GLIF
Logic modules apply data mining models and
are accessed through web services technology
 Provides additional information to help guide the
flow in the guideline.
Integrating data mining applications into clinical guidelines
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Conclusion

Data mining results can be used as a source of
knowledge to help clinical decision making.

We described an approach to apply different types of
data mining models in CDSS.

We used PMML and CDA for knowledge and data
representation.

A tool is developed that can interpret and apply the
mined knowledge.

We envision a future that data mining analysis results
are seamlessly deployed and used at usage sites.
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Questions and Comments
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