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 {sherafr, sartipi}@mcmaster.ca Computer-based Medical Systems (CBMS ’06) June 22, 2006

2. Integrating data mining applications into clinical guidelines2 Outline Decision making based on data mining results Decision making based on data mining results Data and knowledge interoperability Data and knowledge interoperability Knowledge management framework Knowledge management framework Tool implementation Tool implementation Conclusion Conclusion

3. Integrating data mining applications into clinical guidelines3 Decision Making Practitioners face critical questions which requires decision making: Practitioners face critical questions which requires decision making: –The cause of a symptom –Drug prescription –Treatment planning –Diagnosis of a disease –… (many more) 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

4. Integrating data mining applications into clinical guidelines4 Data Mining Applications in Health care Patient

5. Integrating data mining applications into clinical guidelines5 Decision Logic IF the patient has had a heart stroke and is above 50 the patient has had a heart stroke and is above 50THEN his health condition should be monitored! Condition Action

6. Integrating data mining applications into clinical guidelines6 Decision Logic (cont’d) Decision making logic: Decision making logic: –Logical expressions  ‘If-then-else’ structures –Test for conditions –Trigger actions if ( (patient.age > 50) && (patient.previous_heart_stroke == true) ) then …

7. Integrating data mining applications into clinical guidelines7 Data Mining Decision Logic Data mining 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 vs. Application of a data mining model: –Training the model: Building the model –Application of the mode: interpreting for specific patient data

8. Integrating data mining applications into clinical guidelines8 Data Mining Decision Logic (cont’d) Classification: mapping data into predefined classes. (e.g., whether a patient has a specific disease or not) 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.) 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.) 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”.) 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”.)

9. Integrating data mining applications into clinical guidelines9 Data Mining Decision Logic (cont’d) An example of regression model [source:Otto,Pearlmen] An example of regression model [source:Otto,Pearlmen] V max Doppler AVA AVR not recommended AVR recommended AI severity ≥4m/s 3-4m/s ≤ 3m/s ≤ 1 cm2 ≥1.7 cm2 1.1-1.6 cm2 2-3+ %100 %66 0-1+ %100 %88

10. Integrating data mining applications into clinical guidelines10 Application of Data Mining Results Predictive Model Markup Language (PMML): 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 Share, access, exchange PMML documents

11. Integrating data mining applications into clinical guidelines11 Proposed Health Care Knowledge Management Framework Guideline modeling Knowledge Extraction Guideline Execution Phase 1: Build the data mining models

12. Integrating data mining applications into clinical guidelines12 Proposed Health Care Knowledge Management Framework Data and knowledge interoperability Knowledge Extraction Guideline Execution Phase 2: Encode data and knowledge

13. Integrating data mining applications into clinical guidelines13 Proposed Health Care Knowledge Management Framework Data and knowledge interoperability Knowledge Extraction Knowledge Interpretation Phase 3: Apply the knowledge for specific patient data

14. Integrating data mining applications into clinical guidelines14 Knowledge Data and Knowledge Interoperability HL-7 Reference Information Model (RIM) HL-7 Reference Information Model (RIM) –A general high level health care data model Clinical Document Architecture (CDA) Clinical Document Architecture (CDA) –An XML-based standard for defining structured templates for clinical documents Standard Terminology Systems (UMLS, SNOMED CT, etc) Standard Terminology Systems (UMLS, SNOMED CT, etc) –Standard clinical vocabulary sets Predictive Model Markup Language (PMML) Predictive Model Markup Language (PMML) –An XML-based standard for representing data mining results Guideline Interchange Format 3 (GLIF3) Guideline Interchange Format 3 (GLIF3) –A clinical guideline definition standard Data

15. Integrating data mining applications into clinical guidelines15 Tool Implementation A guideline execution engine based on GLIF A guideline execution engine based on GLIF Logic modules apply data mining models and are accessed through web services technology Logic modules apply data mining models and are accessed through web services technology Provides additional information to help guide the flow in the guideline. Provides additional information to help guide the flow in the guideline.

16. Integrating data mining applications into clinical guidelines16 Conclusion Data mining results can be used as a source of knowledge to help clinical decision making. 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 described an approach to apply different types of data mining models in CDSS. We used PMML and CDA for knowledge and data representation. We used PMML and CDA for knowledge and data representation. A tool is developed that can interpret and apply the mined knowledge. 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. We envision a future that data mining analysis results are seamlessly deployed and used at usage sites.

17. Integrating data mining applications into clinical guidelines17 GLIF3 Clinical Guidelines Flow charts Flow charts –Define the flow of actions, state transitions, and events in delivering care. Different nodes in the flow: Different nodes in the flow: –The flow passes through different nodes –Action steps, –Decision steps, At decision nodes the execution engine consults with the data mining results knowledge base to select the right path. At decision nodes the execution engine consults with the data mining results knowledge base to select the right path. At action steps additional information and facts from the knowledge base are presented to the user At action steps additional information and facts from the knowledge base are presented to the user

18. Integrating data mining applications into clinical guidelines18 Questions and Comments