Dr. W. Ceusters European Centre for Ontological Research

ECOREuropean Centre forOntological Research

Requirements for natural language understanding in

referent-tracking based electronic patient records.

CS seminar, Bolzano, Dec 5, 2005

Dr. W. CeustersEuropean Centre for Ontological Research

Saarland University, Saarbrücken - Germany


Presentation overview

• ECOR and me

• The Electronic Health Record (EHR)

• Problems with terminologies and their use in the EHR

• Realist ontology

• Referent Tracking

• Opportunities for narural language understanding

ECOREuropean Centre forOntological Research European Centre for

Ontological Research


Short personal history

1959 - 20051977

1989

1992

1998

2002

2004


The Electronic Health Record


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”


A replacement for

This and that


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

(???)


Example of data entry form

www.comchart.com

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• Exchange of information preserving meaning• Statistics and epidemiology

ECOREuropean Centre forOntological Research The International

Classification of diseases (WHO).• ...

• Chapter II: Neoplasms (C00-D48)• Chapter III: Diseases of the Blood and Blood-forming organs and

certain disorders involving the immune mechanism (D50-D89)• Excludes : auto-immune disease (systemic) NOS (M35.9)• ....• Nutritional Anemias (D50-D53)• D50 Iron deficiency anaemia• Includes: ...• D50.0 Iron deficiency anaemia secondary to blood loss (chronic)• Excludes : ...• D50.1 ...• D51 Vit B12 deficiency anaemia• Haemolytic Anemias (D55-D59) • ...• Chapter IV: ...

ECOREuropean Centre forOntological Research Main problems

• Internal and external consistency of terminologies.

• What do the terms in a terminology stand for ?


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


What’s wrong with current use of terminologies in the

EHR ?


Current mainstream thinking

datainformation

knowledge

wisdom

- representation

- representation

- representation

(- representation)

Questions not often enough asked:• What part of our data corresponds with

something out there in reality ?• What part of reality is not captured by our

data, but should because it is relevant ?

RealityWhat is there on the side of the patient


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

A look at the database:Use of SNOMED codes for ‘unambiguous’

understanding

*

*

*

* 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 EHRs• 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


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.


IUI assignment

• = an act carried out by the first ‘cognitive agent’ feeling the need to acknowledge the existence of a particular it has information about by labelling it with a UUID.

• ‘cognitive agent’:– A person;– An organisation;– A device or software agent, e.g.

• Bank note printer,• Image analysis software.


Criteria for IUI assignment (1)1. The particular’s existence must be determined:

– Easy for persons in front of you, body parts, ...– Easy for ‘planned acts’: they do not exist before the

plan is executed !• Only the plan exists and possibly the statements made about

the future execution of the plan

– More difficult: subjective symptoms• But the statements the patient makes about them do exist !

– However: • no need to know what the particular exactly is, i.e. which

universal it instantiates• No need to be able to point to it precisely

– One bee out of a particular swarm that stung the patient, one pain out of a series of pain attacks that made the patient worried

– But: this is not a matter of choice, not ‘any’ out of ...


Criteria for IUI assignment (2)

2. The particular’s existence ‘may not already have been determined as the existence of something else’:

• Morning star and evening star• Himalaya• Multiple sclerosis

3. May not have already been assigned a IUI.

4. It must be relevant to do so:• Personal decision, (scientific) community guideline, ... • Possibilities offered by the EHR system• If a IUI has been assigned by somebody, everybody else

making statements about the particular should use it

ECOREuropean Centre forOntological Research Representation in the EHR

• Relevant particulars referred to using IUIs• Relationships that obtain between particulars

at time t expressed using relations from an ontology (type OBO)

• Statements describing for each particular, at time t:– Of what universal from an ontology it is an instance

of– AND/OR (if one insists):– By means of what concept from a concept-based

system it can sensibly be described


A shift in mind set

• Not:– ‘this patient has a fracture of the left tibia ’

• But:

– #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

{

ECOREuropean Centre forOntological Research Pragmatics of IUIs in EHRs

• IUI assignment requires an additional effort• In principle no difference qua (or just a little bit more) effort

compared to using directly codes from concept-based systems– A search for concept-codes is replaced by a search for the

appropriate IUI using exactly the same mechanisms• Browsing• Code-finder software• Auto-coding software (CLEF NLP software Andrea Setzer)

– With that IUI comes a wealth of already registered information– If for the same patient different IUIs apply, the user must make

the decision which one is the one under scrutiny, or whether it is again a new instance

• A transfert or reference mechanism makes the statements visible through the RTDB

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


Other Advantages

• mapping as by-product of tracking– Descriptions about the same particular using

different ontologies/concept-based systems

• Quality control of ontologies and concept-based systems– Systematic “inconsistent” descriptions in or

cross terminologies may indicate poor definition of the respective terms


How to make this practicalfor the text-based parts

of an EHR ?

Referent tracking

in the linguistic sense !

ECOREuropean Centre forOntological Research The problem summarised

• natural language is the only medium that is able to communicate clinical information about individual patients without loss of necessary detail;

• (virtual) structured data repositories are required to make subsequent analyses possible;

• any transformation from free language to coding and classification systems results in information loss that is unacceptable for individual patient care, but at the other hand is a conditio sine qua non for population based studies;

• today’s graphical user interfaces can deal reasonably well with picking lists build around controlled vocabularies that fulfil a bridging function from free language towards coding and classification systems but are incompatible with referent tracking


The ultimate scenario

#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 Most important difference:

Use of generic terms

Use of concrete particulars











Jane Smith Jane Smith’s age

Jane Smith’s race Jane SmithJane Smith’s gender Jane Smith’sshowing 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 JaneSmith








CS1-emergency room

CS1-chief-complaint














CS1-emergency room

CS1-chief-complaint


CS2-woman

CS2-painCS2-chest

CS2-womanCS2-woman

CS2-painCS2-painCS2-chestCS2-chest



CS3-woman

Is-A

CS3-woman

Is-A

CS3-native american

Is-A

CS3-native american

Is-ACS3-complaining

“Jane Smith”

Has-Sayer

“Jane Smith”

Has-Sayer

CS3-chest pain

Has-Saying

CS3-chest pain

Has-Saying

CS3-coughing

Has-Saying

CS3-coughing

Has-Saying

CS3-consultation


CS3-consultation


CS3-Em.RoomHas-Loc CS3-Em.RoomHas-Loc

Has-participantHas-participant

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.

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

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

• Big Brother feeling is to be expected but with adequate measures easy to fight.

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

• Collaboration sought for dealing with NLU

Dr. W. Ceusters European Centre for Ontological Research

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