knowledge acquisition for clinical-trial selection savvas nikiforou eugene fink lawrence o. hall...
Post on 21-Dec-2015
221 views
TRANSCRIPT
Knowledge Acquisition for Clinical-Trial Selection
Savvas Nikiforou
Eugene Fink
Lawrence O. Hall
Dmitry B. Goldgof
Jeffrey P. Krischer
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.
Expert System
• Guides a clinician through related questions
• Identifies appropriate medical tests
• Selects matching clinical trials
• Minimizes pain and cost of selection process
Outline
• Eligibility decisions
• Knowledge base
• Knowledge entry
• Experiments
Example: Eligibility Criteria
• Female, older than 30
• No prior surgery
• Breast cancer, stage II or III
Example: Questions
Sex:
Age:
Female
Male
25
Example: Conclusion
Patient is not eligible
Example: Questions
Sex:
Age: 35
Female
Male
Example: Questions
Cancer stage:
Prior surgery? Yes No
I
II
III
IV
Example: Conclusion
Patient is eligible
Full Functionality
• Orders and groups the questions
• Considers multiple clinical trials
Outline
• Eligibility decisions
• Knowledge base
• Knowledge entry
• Experiments
Main Objects
• Questions
• Medical tests
• Eligibility criteria
Types of Questions
• Yes / No
• Multiple choice
• Numeric
Tests
A medical test answers several questions.
It involves certain pain and cost.
Eligibility Criteria
• A logical expression that determines eligibility for a specific clinical trial
Example: CriteriaAND
Age > 30
Prior-surgery = NO
OR
Cancer-stage = II
Cancer-stage = III
Outline
• Eligibility decisions
• Knowledge base
• Knowledge entry
• Experiments
Tests and Questions
Adding tests Modifying a test
Adding yes/no questions
Adding multiple choice questions
Adding numeric questions
Deleting questions
Adding Tests
Test name:
Cost:
Pain:
Yes/No M-Choice Numeric Deleting
Adding Modifying
45.50
1
Mammogram
Adding Yes/No Questions
Breast cancer?
• Text
Yes/No M-Choice Numeric
Adding Modifying
Deleting
Adding Multiple Choice Questions
• Text Options
Yes/No M-Choice Numeric
Adding Modifying
Cancer stage IIIIIIIV
Deleting
Adding Numeric Questions
• Text Min Max
Tumor size 250
Yes/No M-Choice Numeric
Adding Modifying
Deleting
Eligibility Criteria
Adding eligibility criteria
Selecting tests
Deleting expressions
Editing questions
Defining an expression
Selecting questions
Example: Eligibility Criteria
• Female, older than 30
• Breast cancer, stage II
• Post-menopausal or surgically sterilized
Adding Eligibility Criteria
Adding criteria
Selecting tests
001 Clinical trial A
Trial number Trial name
Deleting expressions
Editing questions
Defining an expression
Selecting questions
Selecting Tests
General questions
Blood test
Mammogram
Biopsy
Urine test
Adding criteria
Selecting tests
Deleting expressions
Editing questions
Defining an expression
Selecting questions
Selecting Questions
I II III IVCancer stage:
Age: From: To:0 15030
Post-menopausal? Yes
Surgically sterilized? Yes
Adding criteria
Selecting tests
Deleting expressions
Editing questions
Defining an expression
Selecting questions
Prior surgery?
No
No
NoYes
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
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
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
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
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
Outline
• Eligibility decisions
• Knowledge base
• Knowledge entry
• Experiments
Experiments
Performance of sixteen novice users
• Entering tests and questions
• Entering eligibility criteria
0
20
40
60
80
100
0 1 2 3 4
number of a test set
time
per
ques
tion
(sec
)Entering Tests and Questions
Learning curve
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8 9 10 11
number of a clinical trial
time
per
ques
tion
(sec
)Entering Eligibility Criteria
Learning curve
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
Main Results
• Formal model of selection criteria
• Representation of related knowledge
• Friendly interface for knowledge entry