Cornerstone I: Representing Knowledge From Data to Knowledge Through Concept-Oriented Terminologies James J. Cimino

Cornerstone I: Representing Knowledge

From Data to Knowledge Through Concept-Oriented Terminologies

James J. Cimino

The first step on the path to knowledge is getting things by their right names.

-Chinese saying

Overview

• What is “data to knowledge”?

• Knowledge representation choices

• Knowledge-based terminology efforts

• Medical Entities Dictionary

• Proof of concepts

What is “data to knowledge”?

• Start with patient data in the medical record

• Enhance knowledge by:

– gaining a better understanding of the patient

– learning relevant knowledge

– bringing smart systems to bear to apply knowledge

– discovering new knowledge from health data

Knowledge Representation

• Terminology for representing symbols

• Format for arranging the symbols

Knowledge Representation Choices

• Guideline implementation

Guideline Implementation

• Starren and Xie, SCAMC, 1994

• National Cholesterol Education Panel Guideline

National Cholesterol Education Panel Guideline

Measure Cholesterol& Assess Risk Factors

Cholesterol 200 to 239Cholesterol <200 Cholesterol >239

HDL >35, <2 Risks HDL <35 or 2 Risks

Provide dietary informationReevaluate in 2 years

Cholesterol 200 to 239

HDL >35, <2 Risks



• National Cholesterol Education Panel Guideline• Three representations:

– PROLOG (first-order logic)

NCEP Guideline in PROLOG

rule_j(PID):-

check_lab(PID,hdl,HDL,_),!,

HDL >= 35,

total_risk(PID,Risk),!,

Risk < 2,

check_lab(PID,cholesterol), C,_),

C >= 200,

C =< 239,

print_rule_j.




• Three representations:– PROLOG (first-order logic)– CLASSIC (frames)

NCEP Guideline in CLASSIC

(CL-DEFINE-CONCEPT ‘C-PATIENT

‘(AND

(ALL CHOL

(AND INTEGER

(MIN 200) (MAX 239)))))

(CL-DEFINE-CONCEPT ‘G-PATIENT

‘(AND C-PATIENT LOW-RISK-PATIENT

(ALL HDL (AND INTEGER (MIN 35)))))




• Three representations:– PROLOG (first-order logic)– CLASSIC (frames)– CLIPS (production rules)

NCEP Guideline in CLIPS

(defrule C2G2J “Rules to reach box J”

?f1 <- (calculated-patient (state c)

(done no) (hdl ?hdl) (name ?name)

(test (>= ?hdl 35))

=>

(printout “Patient “ ?name “needs treatment”)




• Three representations:– PROLOG (first-order logic)– CLASSIC (frames)– CLIPS (production rules)

• “All three representations proved adequate for encoding the guideline”



• Terminologic knowledge

Terminology Representation Choices

• Frame-based

Frame-Based Representation

Serum Glucose Test

is-a: Lab Test

Measures: Glucose

Specimen: Serum

Units: “mg/dl”


• Frame-based


• Semantic network

Semantic Network Representation

SerumGlucose

Test

Chemical

is-a

Lab Test

is-a

Body Substance

is-a

SerumGlucose


• Frame-based



• Conceptual graphs

Conceptual Graph Representation

[Serum Glucose Test] -

(is-a) -> [Lab Test]

(measures) -> [Glucose]

(specimen) -> [Serum]


• Frame-based


• Conceptual graphs




• Terminologic knowledge

Knowledge Representation

• Terminology for representing symbols

• Format for arranging the symbols

• Terminology and format for representing terminologic knowledge

Knowledge-Based Terminology Efforts

• Jochen Bernauer, SCAMC, 1991

Jochen Bernauer, SCAMC, 1991

• Conceptual graphs to model findings

increased_uptake site femur site_attr right

during

bone_phase


• Jochen Bernauer, SCAMC, 1991• Rector, Nolan and Glowinski, SCAMC, 1993

Rector, Nolan and Glowinski, SCAMC, 1993

•GALEN project

conditions grammatically haveLocation bodyparts

fractures sensibly haveLocation bones

femurs sensiblyAndNecessarily haveDivision neck


• Jochen Bernauer, SCAMC, 1991• Rector, Nolan and Glowinski, SCAMC, 1993• Campbell and Musen, SCAMC, 1993

Campbell and Musen, SCAMC, 1993

• Conceptual graphs and SNOMED

• Pain + Chest + Radiation to + Left + Arm

(located in) -> [Chest](radiating to) -> [Arm]

-> (with laterality) -> [Left]

[Pain] -


• Jochen Bernauer, SCAMC, 1991• Rector, Nolan and Glowinski, SCAMC, 1993• Campbell and Musen, SCAMC, 1993• Lindberg, Humphreys, McCray, Methods 1993

Lindberg, Humphreys, McCray, Methods 1993

• Unified Medical Language System

Lexical groupString

String

Concept

String

String

Lexical group


• Jochen Bernauer, SCAMC, 1991• Rector, Nolan and Glowinski, SCAMC, 1993• Campbell and Musen, SCAMC, 1993• Lindberg, Humphreys, McCray, Methods 1993• Rocha, Huff, et al., CBM, 1994

Rocha, Huff, et al., CBM, 1994

• VOSER

• A server architecture for managing terminologic knowledege


• Jochen Bernauer, SCAMC, 1991• Rector, Nolan and Glowinski, SCAMC, 1993• Campbell and Musen, SCAMC, 1993• Lindberg, Humphreys, McCray, Methods 1993• Rocha, Huff, et al., CBM, 1994• Campbell, Cohn, Chute, et al., SCAMC 1996

Campbell, Cohn, Chute, et al., SCAMC 1996

• Convergent Medical Terminology

• SNOMED/Kaiser/Mayo

• Galapagos


• Jochen Bernauer, SCAMC, 1991• Rector, Nolan and Glowinski, SCAMC, 1993• Campbell and Musen, SCAMC, 1993• Lindberg, Humphreys, McCray, Methods 1993• Rocha, Huff, et al., CBM, 1994• Campbell, Cohn, Chute, et al., SCAMC 1996• Brown, O’Neil and Price, Methods, 1997

Brown, O’Neil and Price, Methods, 1997

• Read Codes

• Representation with GALEN model


• Jochen Bernauer, SCAMC, 1991• Rector, Nolan and Glowinski, SCAMC, 1993• Campbell and Musen, SCAMC, 1993• Lindberg, Humphreys, McCray, Methods 1993• Rocha, Huff, et al., CBM, 1994• Campbell, Cohn, Chute, et al., SCAMC 1996• Brown, O’Neil and Price, Methods, 1997• Spackman, Campbell, and Côte, SCAMC 1997

Spackman, Campbell, and Côte, SCAMC 1997

• SNOMED RT (Reference Terminology)

• Convergent Medical Terminology

• Description Logic Format


• Jochen Bernauer, SCAMC, 1991• Rector, Nolan and Glowinski, SCAMC, 1993• Campbell and Musen, SCAMC, 1993• Lindberg, Humphreys, McCray, Methods 1993• Rocha, Huff, et al., CBM, 1994• Campbell, Cohn, Chute, et al., SCAMC 1996• Brown, O’Neil and Price, Methods, 1997• Spackman, Campbell, and Côte, SCAMC 1997• Huff, Rocha, McDonald, et al., JAMIA 1998

Huff, Rocha, McDonald, et al., JAMIA 1998

• Logical Observations, Identfiers, Names and Codes (LOINC)

4764-5 | GLUCOSE^3H POST 100 G GLUCOSE PO | SCNC | PT | SER/PLAS | QN|


• Jochen Bernauer, SCAMC, 1991• Rector, Nolan and Glowinski, SCAMC, 1993• Campbell and Musen, SCAMC, 1993• Lindberg, Humphreys, McCray, Methods 1993• Rocha, Huff, et al., CBM, 1994• Campbell, Cohn, Chute, et al., SCAMC 1996• Brown, O’Neil and Price, Methods, 1997• Spackman, Campbell, and Côte, SCAMC 1997• Huff, Rocha, McDonald, et al., JAMIA 1998• Pharmacy system knowledge base vendors

Pharmacy System Knowledge Base Vendors

Manufactured Components

Country-Specific Packaged Product

Ingredient

Ingredient Class

is-a

Drug Class

is-a

Not-Fully-Specified Drug

is-a

Clinical Drug

is-a

Trademark Drug

is-a

International Package Identifiers

is-a

is-a

Composite Trademark Drug

Composite Clinical Drug

is-a


• Jochen Bernauer, SCAMC, 1991• Rector, Nolan and Glowinski, SCAMC, 1993• Campbell and Musen, SCAMC, 1993• Lindberg, Humphreys, McCray, Methods 1993• Rocha, Huff, et al., CBM, 1994• Campbell, Cohn, Chute, et al., SCAMC 1996• Brown, O’Neil and Price, Methods, 1997• Spackman, Campbell, and Côte, SCAMC 1997• Huff, Rocha, McDonald, et al., JAMIA 1998• Pharmacy system knowledge base vendors

Medical Entities Dictionary (MED)

• New York Presbyterian Hospital• 60,000 concepts (procs, results, drugs, probs)• 208,242 synonyms• 84,677 hierarchical links• 113,906 semantic links• 238,040 other attributes• 66,404 translations (ICD9-CM, LOINC, MeSH,

UMLS)

Central Controlled Terminology

MED Data Structures


MED Semantic Network

MedicalEntity

PlasmaGlucose

LaboratorySpecimen

PlasmaSpecimen

AnatomicSubstance

Plasma Substance

Sampled

Part of

Has S

pecimen

Substance Measured

LaboratoryProcedure

CHEM-7

LaboratoryTest

Event

DiagnosticProcedure

Substance

BioactiveSubstance

Glucose

Chemical

Carbo-hydrate

MED Data Structures


• MUMPS global

MED MUMPS Global^med(1600) <SERUM GLUCOSE MEASUREMENT>^med(1600,1) <C0202041> . . ,4) <32703,50000> . . ,5) <> . . ,6) <Serum Glucose Measurement> . . ,7) <> . . ,8) <1724> . . ,12) <GLUC> . . ,14) <169> . . ,16) <31987> . . ,17) <mg/dl> . . ,20) <C000006> . . ,23) <1178> . . ,50) <Serum Glucose> . . ,138) <40444,40445,40446,59165> . . ,156) <MCNC> . . ,161) <QN>

MED Data Structures


• MUMPS global

• DB2

MED DB2 Tables

1234

Entities

10 Name 20 UMLS 30 Part-of

40 Specimen

Slots

1 102 102 202 30

Entity-Slots1 10 Entity2 10 C00012 40 12342 50 mg/dl

Entity/Slot/Values1 11 21 32 3

Ancestry

MED Data Structures


• MUMPS global

• DB2

• Unix

MED UNIX Data Structure

1600|SERUM GLUCOSE MEASUREMENT |1|C020241|4|32703|4|50000|12|GLUC|17|mg/dl|........

MED Data Structures


• MUMPS global

• DB2

• UNIX

Proof of Concepts

• Merging data and application knowledge

Merging Data and Application Knowledge

Plasma Glucose Test

Serum Glucose TestFingerstick Glucose Test

Lab Test

Intravascular Glucose Test Chem20 Display

Lab Display

• Class-based, reusable lab summaries

DOP Summary

WebCIS Summary

Merging Data and Application Knowledge

Plasma Glucose Test

Serum Glucose TestFingerstick Glucose Test

Lab Test

Intravascular Glucose Test Chem20 Display

Lab Display

• Class-based, reusable lab summaries

• Expert system for application maintenance

Proof of Concepts

• Merging data and application knowledge• Smarter retrievals from the record

Smarter Retrievals from the Record

• Repository stores events and results

• Clinical problems at a different level of granularity

• Re-use knowledge to map from problems to clinical data

• Produce problem-specific views of the medical record

Chest X rayCongestive

Heart Failure

Intravascular CK Test

CreatineKinase

Chest X ray 2 View

Cardiac Enzyme

Angina

Lab :1/1/99 Lab :1/1/99 Cardiac Enzyme TestCardiac Enzyme Test

Radiology :2/23/99 Radiology :2/23/99 Chest X RayChest X Ray

Radiology :2/28/96 Radiology :2/28/96 Head CTHead CT

Lab :12/28/96 Lab :12/28/96 Sickle Cell TestSickle Cell Test

Admission :3/14/96 Admission :3/14/96 StrokeStroke

Admission :2/14/98Admission :2/14/98AnginaAngina

Lab :1/1/99 Lab :1/1/99 Blood Type TestBlood Type Test

Radiology :2/1/97 Radiology :2/1/97 Knee X RayKnee X Ray

Concept-oriented(Heart)

Heart Disease

Chest

Discharge :1/15/99Discharge :1/15/99 CHFCHF

Discharge :1/15/99Discharge :1/15/99 CHFCHF

Admission :2/14/98Admission :2/14/98AnginaAngina

Lab :1/1/99 Lab :1/1/99 Cardiac Enzyme TestCardiac Enzyme Test

Radiology :2/23/99 Radiology :2/23/99 Chest X RayChest X Ray

Proof of Concepts


• Smarter retrievals from the record• “Just-in-Time” education

“Just-in-time” Education

• Medline button

• Infobuttons

“Just-in-time” Education

• Medline button

• Infobuttons

• Text-to-Web

• Medline button

• Infobuttons

• Text-to-Web

“Just-in-time” EducationDXplain

Medline

CholesterolGuideline

DietaryInteractions

PDR

Micromedex

Clinical InfoSystem

Webpath

CHORUS

Radiol Museumof South Bank

LaboratoryTest Results

MedicationOrders

X-rayReports

ICD9

Proof of Concepts


• Smarter retrievals from the record

• “Just-in-Time” education• Expert systems

Expert Systems

• Hripcsak, et al., Ann. Int. Med., 1995

Hripcsak, et al., Ann. Int. Med., 1995

• Identify chest x-ray reports suspicious for 6 clinical conditions to trigger alerts

Method Sens SpecLaypersons 22-47% 97-99%Radiologists 73-98% 96-99%Internists 68-98% 97-99%Keyword 51-79% 79-92%NLP/MED/Rule-based 81% 98%

Expert Systems


• Clinical decision support system

Clinical Decision Support System

• Data monitor runs rules against incoming reports

• Tuberculosis cultures come back 4-8 weeks later

• One day, hundreds of TB alerts came in

What Happened to the Tuberculosis Alert?

No Growth

Medical Logic Module

No Growth to Date

No Growth after ...

How We Outsmarted the Lab

No Growth

No Growth after 48 Hours


“No Growth” Results


No Growth to Date

Medical Logic Module

Expert Systems



• DXplain Button

DXplain Button

• Elhanan, et al., SCAMC 1997

• Convert of test results to clinical findings

• Pass findings to DXplain

CholesterolHypercholesterolemia

Abnormalities ofSerum Cholesterol

Serum

Serum Specimen

Serum Cholesterol Test

Expert Systems



• DXplain Button

Proof of Concepts



• “Just-in-Time” education

• Expert systems• Data mining

Data Mining

• Wilcox and Hripcsak, SCAMC 1997

Wilcox and Hripcsak, SCAMC 1997

Data Mining



• Compare traditional coding methods with NLP to identify conditions in a set of patient records (x-ray reports)

Method Sens SpecLaypersons 36% 86%Expert-coded cases 27-37% 95-98%ICD-9-coded cases 12-29% 86-90%Physicians 85% 98%NLP/MED/Rule-based 81% 98%

Wilcox and Hripcsak, SCAMC 1998

Data Mining



Proof of Concepts




• Expert systems

• Data mining• Database maintenance and use

Database Maintenance and Use

• Tables, columns, events all modeled in the MED

• Allows linkage of data model to controlled terminology

• Terminologies can be reused

• Impact of terminology changes on data model can be tracked

Proof of Concepts




• Expert systems

• Data mining

• Database maintenance and use• Terminology maintenance and use

Terminology Maintenance and Use

• Integrating terminologies from merging hospitals

• Automated update of medication terminology

• Detection of errors and inconsistencies

Proof of Concepts




• Expert systems

• Data mining

• Database maintenance and use

• Terminology maintenance and use

Is it Worth the Trouble?

Meed:

• noun

• 1 archaic : an earned reward or wage

• 2 : a fitting return or recompense

• Date: before 12th century

• Etymology: from Old English:

MED

Summary

• Putting knowledge in your terminology gets you:– Better ways to get knowledge out of your EMR– Better ways to get knowledge out of resources– Better ways to use other knowledge bases– Bettter ways to use terminology– Better ways to manage applications– Better ways to manage data and terminology

• Representation scheme is less important

• Desiderata for controlled terminology

Desiderata

•Desirable qualities for terminology

Desiderata

•Desirable qualities for terminology

“Go placidly amid the noise and haste, and remember what peace there may be in silence.”

“I’d rather be sailing”

Documents

Cornerstone I: Representing Knowledge From Data to Knowledge Through Concept-Oriented Terminologies James J. Cimino