ECO R European Centre for Ontological Research Computational Linguistics for Referent Tracking in Electronic Healthcare Records: a research agenda CogSCI

ECOREuropean Centre forOntological Research

Computational Linguistics for Referent Tracking in Electronic Healthcare

Records: a research agenda

CogSCI Colloquium Oct 19, 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 computational linguistics

ECOREuropean Centre forOntological Research European Centre for

Ontological Research


ECOR’s members & partners

Local members

Externalmembers

Partners

Status Oct 2, 2005


Goals and objectives

• sustained and coordinated collaboration with institutions with proven track record of excellence in ontological research and in the application of ontology to solve concrete problems.

• interdisciplinary approach based on philosophical rigour • exchange of research personnel for short research visits• participation in joint projects, • joint supervision of doctoral research, • joint production of software and authorship of research

papers • collaborate in seeking funding at national and international

levels for ontology-related research and development activities


Recently also in the US


Short personal history

1959 - 20051977

1989

1992

1998

2002

2004


The Electronic Health Record


Current US GOV eHealth goals & strategies

• G1: Inform Clinical Practice:– S1. Provide incentives for EHR adoption. – S2. Reduce risk of EHR investment. – S3. Promote EHR diffusion in rural and underserved areas.

• G2: Interconnect Clinicians. – S1. Regional collaborations. – S2. Develop a national health information network. – S3. Coordinate federal health information systems.

• Goal 3: Personalize Care. – S1. Encourage use of Personal Health Records. – S2. Enhance informed consumer choice. – S3. Promote use of telehealth systems.

• Goal 4: Improve Population Health. – S1. Unify public health surveillance architectures. – S2. Streamline quality and health status monitoring. – S3. Accelerate research and dissemination of evidence.

US Department of Health and Human Services July 21, 2004


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

(???)


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: ...


The alphabetic index of ICD-9-CM

• hydrops 782.3• abdominis 789.5• amnii (complicating pregnancy)

(see also hydramnios) 657• congenital - see Hydrops, fetalis• fetal(is) or new-born 778.• due to iso-immunisation 773.3• not due to iso-immunisation 778.0• meningeal NEC 331.4• pericardium - see Pericarditis

ECOREuropean Centre forOntological Research Snomed International (1995)

Number of records (V3.1)• T Topography 12,385• M Morphology 4,991• F Function 16,352• L Living Organisms 24,265• C Drugs &Biological Products 14,075• A Physical Agents, Forces and Activities 1,355• D Disease/ Diagnosis 28,623• P Procedures 27,033• S Social Context 433• J Occupations 1,886• G General Modifiers 1,176• TOTAL RECORDS 132,641

ECOREuropean Centre forOntological Research Snomed International (1995):

knowledge in the codes.

leaflet posterior anatomic

mitralcardiac valve

cardiovascular

T - 23 5 3 2

ECOREuropean Centre forOntological Research Snomed International :

multiple ways to express the same thing

D5-46210 Acute appendicitis, NOS

D5-46100 Appendicitis, NOS

G-A231 Acute

M-41000 Acute inflammation, NOS

G-C006 In

T-59200 Appendix, NOS

G-A231 Acute

M-40000 Inflammation, NOS

G-C006 In

T-59200 Appendix, NOS


The search for internal formal consistency: medSORT-II (Evans & Hersh, ‘93)

no pin-prick sensation in calf ==>

| <neuro-sensation-mx>

| <method> | <pin-prock-test> [pin-prick]

| <locus> | <body-region> [calf]

| <result> | <eval-attr> | <attr> [sensation]

| <value> [absent]

ECOREuropean Centre forOntological Research UMLS: Unified Medical

Language System (NLM)• Tool for information retrieval of 4 components:

– Metathesaurus contains information about biomedical concepts and how they are represented in diverse terminological systems.

– Semantic Network contains information about concept categories and the permissible relationships among them

– Information Sources Map contains both human-readable and machine-processable information about all kinds of biomedical terminological systems

– Specialist lexicon: english words with POS


UMLS Semantic Network

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


Previous work

• Many of these deficiencies can be identified corrected or prevented by doing the right sort of “ontology” using a proper tool.– SNOMED-CT– NCIT– UMLS Semantic Network

• But this is NOT the topic of this presentation


What’s wrong with currentuse of terminologies (and)

ontologies 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


The story of Jane Smith

an old case, well known in the literature ...


Jane’s favourite supermarket

July 4th, 1990: Jane goes shopping:

The freezer section of Jane’s favourite supermarket

The only available warning sign used outside

A very suspiciously shaped upper leg


A visit to the hospital

City Health Centre Dr. Peters

(City HC) Dr. Longley


Diagnosis: a severe spiral fracture of the femur


CityHC’s representation formalism(for statements in records)

Rector AL, Nowlan WA, Kay S, Goble CA, Howkins TJ.A framework for modelling the electronic medical record.Methods Inf Med. 1993 Apr;32(2):109-19.

Categories: “represent concepts and are analogous to classes in other formalisms”

Individuals: “concrete instances of categories which persist in space and time”Occurrences: “are

specific occurrences of individuals and must be situated in space and time. The most importantgroup of occurrences are observations — i.e. agents’ observations of individuals.”


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 CityHC’s EHR• 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

• 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

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– ...


An ontological analysis

continuantsCity HC

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 HC’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

Jane’s fracture as seen by Dr. PetersJane’s fracture as seen by Dr.

Longley

Instances of

Jane’s fracture

UniversalsEHR system

HC

Freezer section

Person

Femur

Fracture

Image

ECOREuropean Centre forOntological Research Ontological recategorisation

CityHC Dr. PetersJane

Smith

JaneSmith’sFractureOf Femur

FractureOf Femur

Severe Spiral

City HCexists on 4th July1990

Dr. Peterslocated atCity HC on 4th July1990

Jane Smith’sconsultation withDr. Peters atCity HC on 4th July1990

Dr. Peters’assessment ofJane Smith’sfracture offemur atCity HC on 4th July1990

JaneSmith’sFracture

Of Femur’sseverity

JaneSmith’sFracture

Of Femur’sshape


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

CityHC Dr. PetersJane

Smith

JaneSmith’s

FractureOf Femur

FractureOf Femur

SevereSpiral

Jane Smith’sconsultation withDr. Peters atCity HC on 4th July1990

Dr. Peters’assessment ofJane Smith’sfracture offemur atCity HC on 4th July1990

JaneSmith’s

FractureOf Femur’s

severity

JaneSmith’s

FractureOf Femur’s

shape

4th July 1990

particulars

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


A case study

• Goals:– Demonstrate the application of referent tracking

to a concrete patient story;– Make you familiar with the ontological analysis

of what is involved;– Understand the actions a NLU algorithm has to

perform when transforming (running) text into a series of IUI-assertions (= information extraction);

– Create interest of the computational linguists amongst you to embark on joined projects with us.


Jim Cimino’s Woods Hole caseJane Smith is a 30 year old, Native American female who presents to the emergency room with the chief complaint of cough and chest pain.The patient reports that she has had a productive cough for three days but that chest pain developed one hour ago. She gives a history of hypertension. She also reports that she was treated in the past for tuberculosis while she was pregnant. The patient reports an allergy to Bufferin.Physical examination revealed a well-developed, well-nourished female in moderate respiratory distress. Vital signs showed a pulse of 90, a respiratory rate of 22, an oral temperature of 100.3, and a blood pressure of 150/100. Examination revealed rales and rhonchi in the left upper chest. Abdominal exam revealed a tender, palpable liver edge.Labs:Chem7 (serum): Glucose 100 (70-105) Chem7 (plasma): Glucose 150 (75-110)CBC: Hgb 15 (12.0-15.8), Hct 45 (42.4-48.0), WBC 11,000 (3,540-9,060), Platelets 145,000 (165,000-415,000)A fingerstick blood sugar was 80Urinalysis showed protein of 1+ and glucose of 0.A blood culture was positive for methicillin-resistant Staphylococcusaureus (MRSA)

ECOREuropean Centre forOntological Research case study continued ...

• ECG - Sinus Rhythm, 74BPM, Axis -30 degrees, ST segment 2mm elevated andT-waves down in leads I, L, V5 and V6

Chest X-ray Left upper lobe infiltrate, left ventricular hypertrophy

The patients nurse reported that the patient seemed depressed about her condition. On questioning, the nurse found that the patient was caringfor her elderly father and was concerned that she would no longer be able to manage caring for herself and him. The nurse asked the patients physician to consider an antidepressant and a social work consult.

A medical student reviewing the case is concerned about the risk of MRSA in patients with pneumonia and a recent myocardial infarction. She decides to do a literature search.


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

CS3-complaining

CS3-chest pain

Has-Saying

CS3-coughing

Has-Saying

“chest-pain”

Has-’referent’

“coughing”

Has-’referent’

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

Documents

ECO R European Centre for Ontological Research Computational Linguistics for Referent Tracking in Electronic Healthcare Records: a research agenda CogSCI