1. Knowledge Acquisition for Clinical-Trial Selection Savvas Nikiforou Eugene Fink Lawrence O. Hall Dmitry B. Goldgof Jeffrey P. Krischer

2. Expert System The system analyzes a patient’s data and determines whether the patient is eligible for clinical trials at the H. Lee Moffitt Cancer Center.

3. Expert System Guides a clinician through related questions Identifies appropriate medical tests Selects matching clinical trials Minimizes pain and cost of selection process

4. Outline Eligibility decisions Knowledge base Knowledge entry Experiments

5. Example: Eligibility Criteria Female, older than 30 No prior surgery Breast cancer, stage II or III

6. Example: Questions Sex: Age: Female Male 25

7. Example: Conclusion Patient is not eligible

8. Example: Questions Sex: Age: 35 Female Male

9. Example: Questions Cancer stage: Prior surgery? Yes No I II III IV

10. Example: Conclusion Patient is eligible

11. Full Functionality Orders and groups the questions Considers multiple clinical trials

12. Outline Eligibility decisions Knowledge base Knowledge entry Experiments

13. Main Objects Questions Medical tests Eligibility criteria

14. Types of Questions Yes / No Multiple choice Numeric

15. Tests A medical test answers several questions. It involves certain pain and cost.

16. Eligibility Criteria A logical expression that determines eligibility for a specific clinical trial

17. Example: Criteria AND Age > 30 Prior-surgery = NO OR Cancer-stage = II Cancer-stage = III

18. Outline Eligibility decisions Knowledge base Knowledge entry Experiments

19. Tests and Questions Adding tests Modifying a test Adding yes/no questions Adding multiple choice questions Adding numeric questions Deleting questions

20. Adding Tests Test name: Cost: Pain: Yes/NoM-ChoiceNumericDeleting Adding Modifying 45.50 1 Mammogram

21. Adding Yes/No Questions Breast cancer? Text Yes/NoM-ChoiceNumeric Adding Modifying Deleting

22. Adding Multiple Choice Questions TextOptions Yes/NoM-ChoiceNumeric Adding Modifying Cancer stage I II III IV Deleting

23. Adding Numeric Questions TextMinMax Tumor size 250 Yes/NoM-ChoiceNumeric Adding Modifying Deleting

24. Eligibility Criteria Adding eligibility criteria Selecting tests Deleting expressions Editing questions Defining an expression Selecting questions

25. Example: Eligibility Criteria Female, older than 30 Breast cancer, stage II Post-menopausal or surgically sterilized

26. Adding Eligibility Criteria Adding criteria Selecting tests 001Clinical trial A Trial numberTrial name Deleting expressions Editing questions Defining an expression Selecting questions

27. Selecting Tests General questions Blood test Mammogram Biopsy Urine test Adding criteria Selecting tests Deleting expressions Editing questions Defining an expression Selecting questions

28. Selecting Questions I II III IV Cancer stage: Age:From:To: 0 150 30 Post-menopausal? Yes Surgically sterilized? Yes Adding criteria Selecting tests Deleting expressions Editing questions Defining an expression Selecting questions Prior surgery? No Yes

29. Defining an Expression Cancer-stage = II Surgically-sterilized = YES Post-menopausal = YES Age > 30 Adding criteria Selecting tests Deleting expressions Editing questions Defining an expression Selecting questions

30. Defining an Expression AND Adding criteria Selecting tests Deleting expressions Editing questions Defining an expression Selecting questions Cancer-stage = II Surgically-sterilized = YES Post-menopausal = YES Age > 30

31. Defining an Expression Adding criteria Selecting tests Deleting expressions Editing questions Defining an expression Selecting questions Surgically-sterilized = YES Post-menopausal = YES AND Age > 30 Cancer-stage = II

32. Defining an Expression Adding criteria Selecting tests Deleting expressions Editing questions Defining an expression Selecting questions Surgically-sterilized = YES AND Age > 30 OR Post-menopausal = YES Cancer-stage = II

33. Defining an Expression Adding criteria Selecting tests Deleting expressions Editing questions Defining an expression Selecting questions AND Age > 30 OR Post-menopausal = YES Cancer-stage = II Surgically-sterilized = YES

34. Outline Eligibility decisions Knowledge base Knowledge entry Experiments

35. Performance of sixteen novice users Entering tests and questions Entering eligibility criteria

36. Entering Tests and Questions Learning curve

37. Entering Eligibility Criteria Learning curve

38. Summary Learning time: 1 hour Adding a test: 2 to 10 minutes Building a knowledge base for Moffitt breast-cancer trials: 8 to 10 hours Adding eligibility criteria: 30 to 60 minutes

39. Main Results Formal model of selection criteria Representation of related knowledge Friendly interface for knowledge entry