Incorporating data mining applications into clinical guidelines
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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, [email protected] Computer-based Medical Systems (CBMS ’06) June 22, 2006. Outline.

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Incorporating Data Mining Applications into Clinical Guidelines

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Incorporating data mining applications into clinical guidelines

Incorporating Data Mining Applications into Clinical Guidelines

Reza Sherafat

Dr. Kamran Sartipi

Department of Computing and Software

McMaster University, Canada

{sherafr, [email protected]

Computer-based Medical Systems (CBMS ’06)

June 22, 2006


Outline

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


Decision making

Decision Making

Clinical Decision Support Systems (CDSS)

  • Computer programs

  • Provide online and patient-specific assistance to health care professionals to make better decisions

  • Clinical knowledge is stored in a knowledge-base

  • Practitioners face critical questions which requires decision making:

  • The cause of a symptom

  • Drug prescription

  • Treatment planning

  • Diagnosis of a disease

  • … (many more)

Integrating data mining applications into clinical guidelines


Data mining applications in health care

Data Mining Applicationsin Health care

Patient

Integrating data mining applications into clinical guidelines


Decision logic

Decision Logic

IF

the patient has had a heart stroke and is above 50

THEN

his health condition should be monitored!

Condition

Action

Integrating data mining applications into clinical guidelines


Decision logic cont d

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


Data mining decision logic

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


Data mining decision logic cont d

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 RuleMining: 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


Data mining decision logic cont d1

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

≥1.7 cm2

1.1-1.6 cm2

%100

%88

AI severity

%100

2-3+

0-1+

%100

%100

%66

AVR not recommended

AVR recommended

Integrating data mining applications into clinical guidelines


Application of data mining results

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


Proposed health care knowledge management framework

Knowledge Extraction

Guideline modeling

Guideline Execution

Proposed Health Care Knowledge Management Framework

Phase 1: Build the data mining models

Integrating data mining applications into clinical guidelines


Proposed health care knowledge management framework1

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


Proposed health care knowledge management framework2

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


Data and knowledge interoperability

Knowledge

Data and Knowledge Interoperability

  • HL-7 Reference Information Model (RIM)

    • A general high level health care data model

  • Clinical Document Architecture (CDA)

    • An XML-based standard for defining structured templates for clinical documents

  • Standard Terminology Systems (UMLS, SNOMED CT, etc)

    • Standard clinical vocabulary sets

  • Predictive Model Markup Language (PMML)

    • An XML-based standard for representing data mining results

  • Guideline Interchange Format 3 (GLIF3)

    • A clinical guideline definition standard

Data

Integrating data mining applications into clinical guidelines


Tool implementation

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


Conclusion

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.

Integrating data mining applications into clinical guidelines


Questions and comments

Questions and Comments

Integrating data mining applications into clinical guidelines


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