Coding of Selection Criteria for Cancer Treatment Plans Savvas Nikiforou.

Coding of Selection Criteria for Cancer Treatment Plans

Savvas Nikiforou

Automated Matching of Patients to Clinical Trials

Part of the project:

Research Group

• Faculty– Lawrence Hall

– Dmitry Goldgof

– Eugene Fink

• Students– Lynn Fletcher

– Princeton Kokku

– Savvas Nikiforou

– Rochelle Harris

Motivation

• Selecting among treatment plans

• Minimizing pain and cost of the selection process

• Reducing the physician’s effort

Expert System

• Guides the nurse through related questions

• Identifies the appropriate medical tests

Outline of the Talk

• Eligibility decisions

• Knowledge base

• Input of the knowledge

• Demonstration

Related Work

• Initial expert systemFletcher, Hall and Goldgof, 1999

• Minimizing pain and costKokku, Hall and Goldgof, 2001

Example: Selection Criteria

• Female, not older than 50

• Breast cancer, stage II

• No prior surgery

Example: Questions

Sex:

Age:

Female

Male

35

Example: Conclusion

Patient is not eligible

Example: Questions

Sex:

Age:

Female

Male

35

Example: Questions

Cancer stage:

Prior surgery? Yes No Unknown

I

II

III

IV

Example: Conclusion

Patient is eligible

Full Functionality

• Orders and groups the questions

• Considers multiple treatment plans

Old System

• A programmer has to code the questions

New System

• A programmer has to code the questions

• A nurse enters the questions through a friendly interface

• Problem: Build the interface

Outline of the Talk

• Eligibility decisions

• Knowledge base

• Input of the knowledge

• Demonstration

Main Objects

• Questions

• Tests

• Eligibility criteria

Types of Questions

• Yes / No / Unknown

• Multiple choice

• Numeric

Examples of Questions

Prior surgery? Yes No Unknown

Examples of Questions

Prior surgery? Yes No Unknown

Cancer stage: I

II

III

IV

Examples of Questions

Prior surgery? Yes No Unknown

Cancer stage: I

II

III

IV

Age:

Tests

A medical test answers several questions.

It involves certain pain and cost.

Example Test: Name and Cost

Test Name:

Cost: 42.23

Pain: 1

Blood test

Example Test: Questions

• Yes / No

Question:

Histologically proven breast cancer?

Example Test: Questions

• Multiple choice

Question: Options:

Patient’s clinical state T-1T-2T-3

Example Test: Questions

• Numeric

Question: White cell blood count 0 500000 0

Min Max Prec

Eligibility Criteria

• A logical expression that determines eligibility for a specific treatment

Example: CriteriaAND

White Blood Count >100,000

No Heart Related Problems

OR

Cancer Stage I

Cancer Stage II

Outline of the Talk

• Eligibility decisions

• Knowledge base

• Input of the knowledge

• Demonstration

Input

• Questions

• Tests

• Eligibility criteria

Input: Test

• Name:

• Cost:

• Pain:

Type test name here

$$$$.$$

0-5

Input: Question Format

• Text

• Type

Yes / No Multiple Choice Numeric

Input: Yes / No Question

Yes / No

Is patient’s age less than 64?

• Text

• Type

Input: Multiple Choice Question

Multiple Choice

Patient’s age

• Text

• Type

Options

< 20

20 to 40

41 to 60

> 60

Input: Numeric Question

Numeric

Patient’s age

• Text

• Type

Min Max Prec.

1200 0

Yes No

Input: Eligibility

• Logical structure AND

OR

• Questions

SexFemaleMale

Age

Tumors?

Lesions?

Yes No• Eligibility answers

40 65From To

Outline of the Talk

• Eligibility decisions

• Knowledge base

• Input of the knowledge

• Demonstration

Current System

Online Demo

Main results

• Formal model of selection criteria and related optimization problems

• Friendly input interface

• Visual representation of these criteria

Coming soon

• Completion of the interface

• Converting the input knowledge into internal logical structures