1. Selection of Clinical Trials: Knowledge Representation and Acquisition Committee: Eugene Fink Lawrence O. Hall Dmitry B. Goldgof Savvas Nikiforou

2. Automated Matching of Patients to Clinical Trials Faculty: Lawrence O. Hall Dmitry B. Goldgof Eugene Fink Part of the project: Students: Lynn Fletcher Princeton Kokku Savvas Nikiforou Bhavesh Goswami Tim Ivanovskiy Rebecca Smith

3. Expert System The system analyzes a patient’s data and determines whether the patient is eligible for Moffitt clinical trials.

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

5. Outline Previous work Eligibility decisions Knowledge base Knowledge entry Experiments

6. Previous Work Medical expert systems Knowledge acquisition Medical systems at USF

7. Medical Expert Systems If-then rules: –Mycin (1972), Puff (1977), Centaur (1977) Qualitative reasoning: –Oncocin (1981), Eon (1995), OncoDoc (1998) Bayesian networks: –Hepar (1990), AIDS 2 (1990)

8. Knowledge Acquisition Teiresias (1974): Knowledge for Mycin Salt (1985): Elevator-design rules Opal (1987): Knowledge for Oncocin Protégé (1987, 2000): General-purpose tools for developing knowledge acquisition interfaces

9. Medical Systems at USF Selection of clinical trials for cancer patients Bayesian networks (Theocharous) Qualitative reasoning (Fletcher and Hall) No knowledge acquisition tools

10. Outline Previous work Eligibility decisions Knowledge base Knowledge entry Experiments

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

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

13. Example: Conclusion Patient is not eligible

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

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

16. Example: Conclusion Patient is eligible

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

18. Old System A programmer has to code the questions

19. New System A programmer has to code the questions A nurse enters the questions through a friendly interface Problem: Build the interface

20. Outline Previous work Eligibility decisions Knowledge base Knowledge entry Experiments

21. Main Objects Questions Medical tests Eligibility criteria

22. Types of Questions Yes / No / Unknown Multiple choice Numeric

23. Examples of Questions Prior surgery? Yes No Unknown Cancer stage: I II III IV Age:

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

25. Example Test: Name and Cost Test name: Cost:50.00 Pain:1 Mammogram

26. Example Test: Questions Yes / No Question: Breast cancer?

27. Example Test: Questions Multiple choice Question: Cancer stage I II III IV Options:

28. Example Test: Questions Numeric Question: Tumor size 025 0 MinMaxPrec

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

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

31. Outline Previous work Eligibility decisions Knowledge base Knowledge entry Experiments

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

33. Adding Tests Test name: Cost: 45.50 Pain:1 Mammography test Yes/NoM-ChoiceNumericDeleting Adding Modifying

34. Mammography test 45.5050.00 Modifying a Test Test name: Cost: Pain:1 Mammogram Yes/NoM-ChoiceNumericDeleting Adding Modifying

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

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

37. Adding Numeric Questions Tumor size TextMinMaxPrec 2500 Yes/NoM-ChoiceNumeric Adding Modifying Deleting

38. Deleting Questions Patient’s age Cancer stage Breast cancer? Tumor size Yes/NoM-ChoiceNumericDeleting Adding Modifying

39. Cancer stage Tumor size Yes/NoM-ChoiceNumericDelete Adding Modifying Deleting Questions

40. Demo

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

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

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

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

45. Selecting Questions I II III IV Cancer stage: Age:From:To: 0 150 30 Post-menopausal? UnknownNoYes Surgically sterilized? UnknownNoYes Adding criteria Selecting tests Deleting expressions Editing questions Defining an expression Selecting questions Prior surgery? UnknownNoYes

46. 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

47. 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

48. 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

49. 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

50. 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

51. Demo

52. Outline Previous work Eligibility decisions Knowledge base Knowledge entry Experiments

53. Performance of seven novice users Entering tests and questions Entering eligibility criteria

54. Entering Tests and Questions Learning curve

55. Entering Eligibility Criteria Learning curve

56. Entering Eligibility Criteria

57. 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

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

59. Future Work Probabilities of different answers Logical connections among questions Detection of identical and related questions