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  • Select an option from different alternatives Be reminded of some facts
  • Online data access, data entry Offline knowledge access
  • Unified approach to implement complex decision making logic. Flexibility to represent different types of logic Flexibility in decision making logic transfer Provide some sort of knowledge interoperability Knowledge is produced in a place Be available to be used elsewhere! Clarity on data interactions with the data source
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    1. 1. 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
    2. 2. Outline <ul><li>Decision making based on data mining results </li></ul><ul><li>Data and knowledge interoperability </li></ul><ul><li>Knowledge management framework </li></ul><ul><li>Tool implementation </li></ul><ul><li>Conclusion </li></ul>
    3. 3. Decision Making <ul><li>Practitioners face critical questions which requires decision making: </li></ul><ul><ul><li>The cause of a symptom </li></ul></ul><ul><ul><li>Drug prescription </li></ul></ul><ul><ul><li>Treatment planning </li></ul></ul><ul><ul><li>Diagnosis of a disease </li></ul></ul><ul><ul><li>… (many more) </li></ul></ul><ul><li>Clinical Decision Support Systems (CDSS) </li></ul><ul><ul><li>Computer programs </li></ul></ul><ul><ul><li>Provide online and patient-specific assistance to health care professionals to make better decisions </li></ul></ul><ul><ul><li>Clinical knowledge is stored in a knowledge-base </li></ul></ul>
    4. 4. Data Mining Applications in Health care Patient
    5. 5. Decision Logic <ul><li>IF </li></ul><ul><li>the patient has had a heart stroke and is above 50 </li></ul><ul><li>THEN </li></ul><ul><li>his health condition should be monitored! </li></ul>Condition Action
    6. 6. Decision Logic (cont’d) <ul><li>Decision making logic: </li></ul><ul><ul><li>Logical expressions </li></ul></ul><ul><ul><ul><li>‘ If-then-else ’ structures </li></ul></ul></ul><ul><ul><ul><ul><li>Test for conditions </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Trigger actions </li></ul></ul></ul></ul><ul><ul><li>if ( (patient.age > 50) && (patient.previous_heart_stroke == true) ) </li></ul></ul><ul><ul><li>then … </li></ul></ul>
    7. 7. Data Mining Decision Logic <ul><li>Data mining </li></ul><ul><ul><li>Analysis and mining of data to extract hidden facts in the data </li></ul></ul><ul><ul><li>The extracted facts are represented in a data structure called “data mining model” </li></ul></ul><ul><li>Training vs. Application of a data mining model: </li></ul><ul><ul><li>Training the model: Building the model </li></ul></ul><ul><ul><li>Application of the mode: interpreting for specific patient data </li></ul></ul>
    8. 8. Data Mining Decision Logic (cont’d) <ul><li>Classification : mapping data into predefined classes. (e.g., whether a patient has a specific disease or not) </li></ul><ul><li>Regression : mapping a data item to a real-valued prediction variable. (e.g., planning treatments.) </li></ul><ul><li>Clustering : To identify clusters of data items. (e.g., to cluster patients based on risk factors.) </li></ul><ul><li>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”.) </li></ul>
    9. 9. Data Mining Decision Logic (cont’d) <ul><li>An example of regression model [source:Otto,Pearlmen] </li></ul>V max Doppler AVA AVR not recommended AVR recommended AI severity ≥ 4 m/s 3-4 m/s ≤ 3 m/s ≤ 1 cm2 ≥ 1.7 cm2 1.1-1.6 cm2 2-3+ %100 %100 %66 0-1+ %100 %100 %88
    10. 10. Application of Data Mining Results <ul><li>Predictive Model Markup Language ( PMML ): </li></ul><ul><ul><li>XML based specification </li></ul></ul><ul><ul><li>Meta model : Define the data structure of the model </li></ul></ul><ul><ul><li>Different types of data mining models (clustering, classifications, …) </li></ul></ul><ul><ul><li>Extendable for model specific constructs </li></ul></ul><ul><li>Share, access, exchange PMML documents </li></ul>
    11. 11. Proposed Health Care Knowledge Management Framework Phase 1: Build the data mining models Guideline modeling Knowledge Extraction Guideline Execution
    12. 12. Proposed Health Care Knowledge Management Framework Data and knowledge interoperability Knowledge Extraction Guideline Execution Phase 2: Encode data and knowledge
    13. 13. Proposed Health Care Knowledge Management Framework Data and knowledge interoperability Knowledge Extraction Knowledge Interpretation Phase 3: Apply the knowledge for specific patient data
    14. 14. Data and Knowledge Interoperability <ul><li>HL-7 Reference Information Model ( RIM ) </li></ul><ul><ul><li>A general high level health care data model </li></ul></ul><ul><li>Clinical Document Architecture ( CDA ) </li></ul><ul><ul><li>An XML-based standard for defining structured templates for clinical documents </li></ul></ul><ul><li>Standard Terminology Systems ( UMLS, SNOMED CT, etc) </li></ul><ul><ul><li>Standard clinical vocabulary sets </li></ul></ul><ul><li>Predictive Model Markup Language ( PMML ) </li></ul><ul><ul><li>An XML-based standard for representing data mining results </li></ul></ul><ul><li>Guideline Interchange Format 3 ( GLIF3 ) </li></ul><ul><ul><li>A clinical guideline definition standard </li></ul></ul>Data Knowledge
    15. 15. Tool Implementation <ul><li>A guideline execution engine based on GLIF </li></ul><ul><li>Logic modules apply data mining models and are accessed through web services technology </li></ul><ul><li>Provides additional information to help guide the flow in the guideline. </li></ul>
    16. 16. Conclusion <ul><li>Data mining results can be used as a source of knowledge to help clinical decision making. </li></ul><ul><li>We described an approach to apply different types of data mining models in CDSS. </li></ul><ul><li>We used PMML and CDA for knowledge and data representation. </li></ul><ul><li>A tool is developed that can interpret and apply the mined knowledge. </li></ul><ul><li>We envision a future that data mining analysis results are seamlessly deployed and used at usage sites. </li></ul>
    17. 17. GLIF3 Clinical Guidelines <ul><li>Flow charts </li></ul><ul><ul><li>Define the flow of actions, state transitions, and events in delivering care. </li></ul></ul><ul><li>Different nodes in the flow: </li></ul><ul><ul><li>The flow passes through different nodes </li></ul></ul><ul><ul><li>Action steps, </li></ul></ul><ul><ul><li>Decision steps, </li></ul></ul><ul><li>At decision nodes the execution engine consults with the data mining results knowledge base to select the right path. </li></ul><ul><li>At action steps additional information and facts from the knowledge base are presented to the user </li></ul>
    18. 18. Questions and Comments