eco r european centre for ontological research ontology for indexing electronic patient records....

ECOREuropean Centre forOntological Research

Ontology for indexing electronic patient records.

There is only one right way:Referent Tracking !

STIC-Santé seminar, Paris, Dec 8, 2005

Dr. W. CeustersEuropean Centre for Ontological Research

Saarland University, Saarbrücken - Germany


Electronic Health Record

• ISO/TS 18308:2003– Electronic Health Record (EHR):

• A repository of information regarding the health of a subject of care, in computer processable form.

– EHR system:• the set of components that form the mechanism by which

electronic health records are created, used, stored, and retrieved. It includes people, data, rules and procedures, processing and storage devices, and communication and support facilities.

• More common meaning of EHR system: – only the “software being executed”


The Medical Informatics dogmaTo structure or NOT to be

• Fact: computers can only deal with a structured representation of reality:– structured data:

• relational databases, spread sheets

– structured information:• XML simulates context

– structured knowledge:• rule-based knowledge systems

• Conclusion: a need for structured data entry

(???)

ECOREuropean Centre forOntological Research Structured EHR data entry

• Current technical solutions:– Data entry forms

• provide the structure• various paradigms:

– Rigid, pre-fixed– Adaptable to user-preferences, but fixed when used– Dynamically adapting to entered data in context

– Terminologies, coding and classification systems: • Provide the language to be used;• Are claimed

– To allow exchange of information preserving meaning– To be a good basis for record indexing to allow subsequent

processing for statistics and epidemiology

ECOREuropean Centre forOntological Research‘Traditional’ semantic indexing

• Statement:– ‘ Joe Smith has a fracture of the left tibia ’

• Becomes indexed as :

– #12 M-2xg41 A-2t68

– M-2xg41 code in SnowMeat with terms:– fracture, fractures, fracture NOS, broken, ...

– A-2t68 ibidem associated with:– left tibia, left tibia NEC, ...

– Additional terms through– hierarchy: bone, bones, os, ...– associations: lower leg, limb, body part, ...


At least 2 major drawbackswith traditional semantic indexing

1. Bad organisation and structure of ‘traditional’ terminologies and concept systems

2. Codes from such systems do not capture ‘what they are about’, ‘what was on the side of the patient’.


Problems with terminologies (1)

Lack of face value

Agrammatical constructions

Shift in ontological category (or ambiguous meaning)



‘ventricle’ used in 2 different meanings



• Mixing of differentiae• Ontological nonsense



Incomplete classification


5572 04/07/1990 26442006 closed fracture of shaft of femur

5572 04/07/1990 81134009 Fracture, closed, spiral


5572 12/07/1990 9001224 Accident in public building (supermarket)

5572 04/07/1990 79001 Essential hypertension

0939 24/12/1991 255174002 benign polyp of biliary tract



47804 03/04/1993 58298795 Other lesion on other specified region


298 22/08/1993 2909872 Closed fracture of radial head




PtID Date ObsCode Narrative

0939 20/12/1998 255087006 malignant polyp of biliary tract

An index through which the ‘whats’ are lost

*

*

*

* cause, not disorder

How many disorders have patients 5572, 2309 and 298 each had thus far in their lifetime ?

How many numerically different disorders are listed here ?

How many different types of disorders are listed here ?

ECOREuropean Centre forOntological Research Would it be easier if you

could see the code labels ?


































Same patient, same hypertension code:Same (numerically identical) hypertension ?

Different patients, same fracture codes:Same (numerically identical) fracture ?

Same patient, different dates, same fracture

codes: same (numerically identical)

fracture ?

Same patient, same date,2 different fracture codes:

same (numerically identical) fracture ?

Same patient, different dates, Different codes. Same (numericallyidentical) polyp ?

A look at the problems ...Different patients. Same supermarket? Maybe the same (irrelevant ?) freezer section ?Or different supermarkets, but always in the freezer sections ?

ECOREuropean Centre forOntological Research Main problem areas

for current EHR indexing• Statements refer only very implicitly to the concrete

entities about which they give information.• Idiosyncracies of concept-based terminologies

– tell us only that some instance of the class the codes refer to, is refered to in the statement, but not what instance precisely.

– Are usually confused about classes and individuals.• “Country” and “Belgium”.

• Mixing up the act of observation and the thing observed.

• Mixing up statements and the entities these statements refer to.


Consequences

• Very difficult to:– Count the number of (numerically) different diseases

• Bad statistics on incidence, prevalence, ...• Bad basis for health cost containment

– Relate (numerically same or different) causal factors to disorders:

– Dangerous public places (specific work floors, swimming pools),

– dogs with rabies,

– HIV contaminated blood from donors,

– food from unhygienic source, ...

• Hampers prevention

– ...

ECOREuropean Centre forOntological Research Proposed solution:

Referent Tracking

• Foundation:

Realist ontology

ECOREuropean Centre forOntological Research Ontology

• ‘Ontology’: the study of being as a science• ‘An ontology’ is a representation of some pre-

existing domain of reality which– (1) reflects the properties of the objects within its

domain in such a way that there obtains a systematic correlation between reality and the representation itself,

– (2) is intelligible to a domain expert– (3) is formalized in a way that allows it to support

automatic information processing

• ‘ontological’ (as adjective):– Within an ontology.– Derived by applying the methodology of ontology– ...

ECOREuropean Centre forOntological Research Proposed solution:

Referent Tracking

• Purpose:– explicit reference to the concrete individual entities

relevant to the accurate description of each patient’s condition, therapies, outcomes, ...

• Method:– Introduce an Instance Unique Identifier (IUI) for each

relevant individual (= particular, = instance).– Distinguish between

• IUI assignment: for instances that do exist• IUI reservation: for entities expected to come into existence in

the future


Referent Tracking basedsemantic indexing

• Statement:– ‘Joe Smith has a fracture of the left tibia ’

• Becomes indexed as :

– #12 #234 #876– #234 is_located_in #876– #876 is_part_of #12– #876 is_instance_of left_tibia– ...

this this

•With Relationships and universals from a realist ontology

concepts from a terminology

{


An ontological analysis

continuantsCity hospital

The freezer section of Jane’s favourite supermarket

Jane’s left femur

Jane’s left femur fracture

Jane Smith

Dr. Peters

Jane’s left femur

Jane’s fracture’s image

Dr. Longley

City hospital’s EHR system

t Jane’s fallingJane’s femur breakingDr. Peter’s examination of Jane’s fractureDr. Peter’s ordering of an X-rayShooting the pictures of Jane’s leg

occurrents

Jane’s fracture’s healingDr. Peter’s diagnosis making

Jane diesFreezer section dismantledDr. Longley’s examination of Jane’ s fracture

UniversalsEHR system

HC

Freezer section

Person

Femur

Fracture

Image


Essentials of Referent Tracking

• Generation of universally unique identifiers;• deciding what particulars should receive a IUI;• finding out whether or not a particular has already

been assigned a IUI (each particular should receive maximally one IUI);

• using IUIs in the EHR, i.e. issues concerning the syntax and semantics of statements containing IUIs;

• determining the truth values of statements in which IUIs are used;

• correcting errors in the assignment of IUIs.

ECOREuropean Centre forOntological Research Advantage: better

reality representation

















IUI-001

IUI-001

IUI-001

IUI-003

IUI-004

IUI-004

IUI-005

IUI-005

IUI-005

IUI-007

IUI-007

IUI-007

IUI-002

IUI-012


Steps in referent trackingbased semantic indexing.


The environment

#IUI-1 ‘affects’ #IUI-2#IUI-3 ‘affects’ #IUI-2#IUI-1 ‘causes’ #IUI-3

Referent TrackingDatabase

EHR

CAG repeat

Juvenile HD

persondisorder

continuantOntology

Natural LanguageUnderstandingTechnology


Jim Cimino’s Woods Hole case

First sentence:

Jane Smith is a 30 year old, Native American female who presents to the emergency room with the chief complaint of cough and chest pain.


Step 1: identify the phrases referring to particulars

Jane Smith is a 50 year old ,

Native American female who presents

to the emergency room

with the chief complaint

of cough and chest pain.







Step 2: indentify to what particulars these phrases refer

Jane Smith Jane Smith’s age

Jane Smith’s race Jane SmithJane Smith’s gender Jane Smith’s showing up at ...

A specific emergency room of health facility XYZ

Jane Smith’s complaining primarily about ...

A temporal part of Jane Smith’slife marked by happenings of coughs

Jane Smith’s chest

A specific pain experienced by Jane Smith


Compare with simple clinical coding in juxtaposition






“Jane Smith” CS1-age

CS1-native-americanCS1-female-gender

CS1-emergency room

CS1-chief-complaint

CS1-coughing CS1-chest-pain

CS2-woman

CS2-painCS2-chest


Compare with the output of the perfect semantic analyser we all would dream of

CS3-50 years oldHas-Age

CS3-woman

Is-A

CS3-native american

Is-ACS3-complaining

“Jane Smith”

Has-Sayer

CS3-chest pain

Has-Saying

CS3-coughing

Has-Saying

CS3-consultation

Has-happening-during

CS3-Em.RoomHas-Loc

Has-participant

Compare with the output of the NAIVE !!! semantic analyser we all would dream of


What it (more or less) should be with traditional coding

CS3-complaining

CS3-chest pain

Has-Saying

CS3-coughing

Has-Saying

“chest-pain”

Has-’referent’

“coughing”Has-’referent’


What it (more or less) should be with referent tracking

CS3-complaining

CS3-chest pain

Has-Saying

CS3-coughing

Has-Saying

“chest-pain”

Has-referent

“coughing”

Has-referent

J.S.’ complaining at t1

J.S.’ chest pain at t-1

J.S.’ coughing at t-1

Has-code

Has-code

Has-code

ECOREuropean Centre forOntological Research Step 3: are relevant and

necessary particulars missing ?• Referred to:

– Jane Smith– Jane Smith’s age– Jane Smith’s race– Jane Smith’s gender– Jane Smith’s showing up at ...– The specific emergency room in the health facility– Jane Smith’s primarily complaining ...– The temporal part ... coughs– Jane Smith’s chest– Jane Smith’s particular pain

• Missing:– The health facility– The healthcare worker she consulted– The particular coughs (under the condition she tells the objective truth)– The underlying disorder (under whatever state of affairs)


Step 4: IUI assignment

• Assumptions: – the RTS contains already:

• IUI-1 Jane Smith

Coi = <IUIa, ta, CS3, IUI-1, woman, tr>

• IUI-1.1 Ri = <IUIa, ta, depends-on, BFO, {IUI-1.1, IUI-1}, tr>

Coi = <IUIa, ta, CS1, IUI-1.1, age, tr>

• IUI-1.2 Coi = <IUIa, ta, CS1, IUI-1.2, cherokee, tr>

Ri = <IUIa, ta, depends-on, BFO, {IUI-1.2, IUI-1}, tr>

• IUI-1.3 Coi = <IUIa, ta, CS3, IUI-1.3, chest pain, tr>

Ri = <IUIa, ta, is-located-in, BFO, {IUI-1.3, IUI-1}, tr>

– All dates in the statements are 2 years earlier than now

• What to do with:• Jane Smith• Jane Smith’s race (CS1: native American)• Jane Smith’s gender (CS1: female)• Jane Smith’s chest pain (CS3: chest pain)• Jane Smith’s age (50)

ECOREuropean Centre forOntological Research Conclusion

• Referent tracking can solve a number of problems in an elegant way, specifically those related to traditional semantic indexing.

• Existing (or emerging) technologies can be used for the implementation.

• Old technologies (cbs) can play an interesting role.

• The proof of the pudding is in the eating– Pilote is going to be set up

• Collaboration sought for dealing with NLU

eco r european centre for ontological research ontology for indexing electronic patient records....

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