Incorporating Data Mining Applications into Clinical Guidelines

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. 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

3. 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

4. Data Mining Applicationsin Health care Patient Integrating data mining applications into clinical guidelines

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. Questions and Comments Integrating data mining applications into clinical guidelines