Die Ontologie biomedizinischer Daten

ifomis.org1

Die Ontologie biomedizinischer Daten

Barry Smith

Institute for Formal Ontology and Medical Information Science

ifomis.org2

IFOMIS

Institute for Formal Ontology and Medical Information Science

Mission: to develop formal ontologies to support empirical research in biomedical informatics and in the life sciences in general

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Biomedical ontologies and terminology systems

currently manifest a very low degree of formal rigour

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SNOMED-CT

900,000 ‘concepts’ and relations between them, such as

is_a (for class subsumption)

part_of

causes

treats

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SNOMED’s confused treatment of is_a

beide_Hoden is_a Hoden

beide_ Gebärmuttern is_a Gebärmutter

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Halbextraktion_aus_Steißlage is_a Extraktion_aus_Steißlage

Extraktion_aus_Steißlage is_a vollständige_Steißgeburt

____________________________

Halbextraktion_aus_Steißlage is_a vollständige_Steißgeburt

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Confused treatment of objects and processes

diagnostische_endoskopische_Untersuchung_eines_Mediastinums_NOS

is_a Mediastinoskop.

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Confusion of object with knowledge about object

Kontrazeption is_a funktionaler_Befund

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National Cancer InstituteThesaurus

a biomedical thesaurus created specifically to meet the needs of the NCI

semantically modeled cancer-related terminology built using Description Logic

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NCI Thesaurus root concepts

Anatomic Structure, Anatomic System, or Anatomic Substance ?Or ? Does the NCI not know to which categoryAny item classified there belongs ?Anatomic Substance ? If yes, why is geneproduct not subsumed by it ? If no, why aredrugs and chemicals not subsumed by it ?

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Conceptual entity

Definition: none

Semantic type: – Conceptual entity– Classification

Subconcepts:– Action:

• definition: action; a thing done

– And: • Definition: an article which expresses the relation of

connection or addition, used to conjoin a word with a word, ...

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Action is_a Conceptual Entity

And is_a Conceptual Entity

Swimming is healthy and contains 8 letters

Conceptual entity

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Definition of “cancer gene”

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NCI Thesaurus architecture

Disease

BreastBreast neoplasmDisease-has-associated-anatomy

ISA

Findings-And-Disorders-Kind Anatomy-Kind

“Formal subsumption” or

“inheritance”

“Associative” relationships providing

“differentiae”

“Kinds” restrict the domain and range of

associative relationships

What diseases have a diameter of over 3 cm ?

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Confusion of objects and the states in which they participate

Disease

BreastBreast neoplasmDisease-has-associated-anatomy

ISA

Findings-And-Disorders-Kind Anatomy-Kind

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No one knows what ‘concept’ (or ‘conceptualization’) means

1. The linguistic reading

2. The psychological reading

3. The epistemological reading

4. The ontological reading

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1) The linguistic reading

A concept is a meaning that is shared in common by a collection of synonymous terms

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Unified Medical Language System

is_a =def.

If one item ‘is_a’ another item then the first item is more specific in meaning than the second item.

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Fruit

Orange

Vegetable

similarTo

ApfelsinesynonymWith

NarrowerTerm

Goble & Shadbolt

Semantic Networks

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The linguistic reading is bad

for work on ontologies in support of research in the natural sciences / evidence-based medicine

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Problem of evaluation

a good ontology/terminology/vocabulary = one which corresponds to reality as it exists beyond our concepts

if an ontology is a mere network of meanings, then the distinction between good and bad ontologies loses its foothold

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angel or devil are perfectly good concepts

so arecancelled performanceavoided meetingprevented pregnancyimagined mammal alien implant removal Chios energy healing

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The linguistic reading

yields a more or less coherent reading of relations like:

‘is_a’‘synonymous_with’‘associated_to’

but it fails miserably when it comes to relations of other types

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part_ofheart part_of human

human heart part_of human

testis part_of human

human testis part_of human

but not: human has_part human testis

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how can concepts, on the linguistic reading, figure as relata of relations

like:

part_of = def. composes, with one or more other physical units, some larger whole

contains =def. is the receptacle for fluids or other substances

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How can a set of synonymous terms serve as

a receptacle for fluids or other substances?

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The psychological reading of ‘concept’

Concepts are ideas in the minds of human subjects

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Eugen Wüster1935

Professor of WoodworkingMachineryin the ViennaAgriculturalCollege

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Eugen Wüster

Terminology-hobbyistandfounder of International StandardsOrganizationTechnicalCommittee 37

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International Standard Bad Philosophy

Wüster: concepts are inside people’s brains

ISO terminology standards

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Wüster

a concept is a mental surrogate of a plurality of objects grouped together on the basis of perceived similarities

and what makes those objects similar is another concept

(Turtles all the way down)

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ISO: Terminologists should still postulate ‘concepts’ even when they have no idea of what the terms in question mean

In the domain of woodworking equipment we can see the similarities between groups of objects to which general terms are assigned.

Not so in medicine (consider: a carcinoma, or an embryo, in the successive phases of its development)

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Wüster / ISO on ‘objects’

object = def. anything to which human thought is or can be directed

... whether material or immaterial, real or purely imagined

ISO: In the course of producing a terminology, philosophical discussions on whether an object actually exists in reality … are to be avoided.

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3) The epistemological reading

Concepts are ‘units of knowledge’

as in ‘knowledge modeling’, ‘knowledge representation’, ‘knowledge-intense disciplines’

Even errors are ‘knowledge’ on this reading

– so here, too, the concept orientation draws as too far away from empirical science and too close to delusion and myth

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Against ‘knowledge representation’

Not

‘KNOWLEDGE-BASED SYSTEMS’

but

‘true-or-false-belief-based systems’

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Concepts are Triply Ethereal

because they are simultaneously supposed to be

1. software proxies for entities in reality (some ghostly diabetes counterpart is needed – because “you can’t get the diabetes itself inside the computer”)

2. the ‘knowledge’ (ideas and beliefs) in the minds of human experts

3. the meanings of the terms such experts use

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4) The ontological reading

concepts are not creatures of cognition or of computation

they are invariants out there in reality

Better: they are what philosophers call types, kinds, universals

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is_a

human is_a mammal

all instances of the universal human are instances of the universal mammal

is_a defined in terms of the primitive relation of instantiation between a particular and a universal

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part_of

defined in terms of the primitive relation of mereological parthood defined between one instance and another (for example between Mary and her heart)

A part_of B =def. given any instance a of A there is some instance b of B such that a part_of b

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inverse relations

nucleus part_of cell

cell has_part nucleus

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All-some definitions of relations between universals

A adjacent_to B =def

all instances of A are adjacent to (in the instance-level sense) some instance of B

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Ajacency as a relation between universals is not symmetrical

nucleus adjacent_to cytoplasm

Not: cytoplasm adjacent_to nucleus

seminal vesicle adjacent_to urinary bladder

Not: urinary bladder adjacent_to seminal vesicle

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Evaluation

Bad ontologies are (inter alia) those whose general terms lack the relation to corresponding universals in reality, and thereby also to corresponding instances.

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Good ontologies

= representations of universals and particulars in reality

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?

The concept diabetes mellitus becomes ‘associated with a diabetic patient’

concept patient concept diabetes

what it is on the

side of the patient?

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?

The concept diabetes mellitus becomes ‘associated with a diabetic patient’

concept patient concept diabetes

what it is on the

side of the patient?what is the relation here?

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what it is on the

side of the patient

Make this our starting point

+substance accident

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A bottom-up approach

begin with what confronts the physician at the point of care (or in the lab):

instances in reality (patients, disorders, pains, fractures, ...)

= the what it is on the side of the patient

and build up to terminologies from there

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What happens when a new disorder first begins to make itself manifest?

physicians delineate a certain family of cases manifesting a new pattern of symptoms

... hypothesis: they are instances of a single universal or kind

(this universal still hardly understood)

but already: need for a new term (e.g. ‘AIDS’)

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‘SARS’

not: severe acute respiratory syndrome

but: this particular severe acute respiratory syndrome, instances of which were first identified in Guangdong in 2002 and caused by instances of this particular coronavirus whose genome was first sequenced in Canada in 2003

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Users can point to instances in the lab or clinic – but not yet to universals

The terminologist plugs the gap by postulating concepts instead

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Users can point to instances in the lab or clinic – but not yet to universals

(The terminologist plugs the gap by postulating concepts instead)

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It’s sometimes hard to grasp the universals in reality to which our general terms refer.So, let’s guarantee that every general term ‘w’ has a precisely tailored referent:

‘the concept w’We can then forget the messy job of coming to grips with reality, and substitute instead the more pleasant job of grasping the conceptual entities we ourselves have created

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Better: terminology building should start from the instances that we apprehend in the lab or clinic

Assertions in scientific texts pertain to universals in reality

Assertions in the EHR pertain to instances of these universals

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Universals are those invariants in realitywhich make possible the use of general terms in scientific inquiry and the use of standardized tests and standardized therapies in clinical care

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Universals have instances

SNOMED CT comprehends universals in the realms of disorders, symptoms, anatomical structures, ...

In each case we have corresponding instances

= the what it is on the side of the patient

but such instances are poorly recorded in EHRs so far

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The Great Task of Terminology Building in an Age of Evidence-Based Medicine

Terminology work should start with instances in reality, and seek to build up from there to align our terms with the corresponding universals

We can then abandon the detour through concepts altogether

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Terminologies should be aligned not with concepts but with universals in

reality

including the universals instituted by therapies, acts of measurement, portions of bodily substance, etc.

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Define a node of a terminology:

<p, Sp, d>with p a preferred term (string)

Sp a set of synonymsd an (optional) definition

Define a terminology:T = <N, L, v>

N a set of nodesL a set of links (graph-theoretical edges)v a version number

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The ideal: one-to-one correspond between nodes and universals in reality

Problem: bad terms (‘phlogiston’, ‘diabetes’) At any given stage we will have:

N = N1 N> N< where

N1 = terms which correspond to exactly one universalN> = terms which correspond to more than one universal N< = terms which correspond to less than one universal

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The belief in scientific progress

with the passage of time, N> and N< will become ever smaller, so that N1 will approximate ever more closely to N *

Assumption: the vast bulk of the beliefs expressed / presupposed in biomedical texts are true. Hence N1 already constitutes a very large portion of N (the collection of terms already in general use).

*modulo the fact that the totality of universals will itself change with the passage of time

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There are hearts

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But science is an asymptotic process

At all stages prior to the ideal end to our labors, we will not know where the boundaries between N1, N<, and N> are to be drawn

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We do not know how the terms are presently distributed between N1, N< and N>,

So: is the distinction of purely theoretical interest – a matter of abstract (philosophical) housekeeping ?

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We typically have at our disposal a whole developing series of versions of a terminology

New idea: we can create locally our own alternative developing series in order to test out alternative hypotheses regarding how to classify given particulars as instances of given types of disorders or symptoms

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How make instances visible to reasoning systems?

Create an EHR regime in which explicit alphanumerical IUIs (instance unique identifiers) are automatically assigned to each instance when it first becomes relevant to the treatment of a given patient

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We could then perform experiments with terminologies

Our referent-tracking machinery will give us the facility to experiment with different scenarios as concerns the division between N1, N<, and N>

better terminologies

better decision-support for diagnosis

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How medical terms are introduced

we have a pool of cases (instances) manifesting a certain hitherto undocumented pattern of irregularities (deviations from the norm)

the universal kind which they instantiate is unknown – and the challenge is to solve for this unknown

(cf. the discovery of Pluto)

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Instance vector

an ordered triple

<i, p, t>

i is a IUI, p a preferred term, and t a time

instance #5001 is associated with

SNOMED-CT code glomus tumor

at 4/28/2005 11:57:41 AM

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Instantiation of a terminology

Let D be a set of IUIs (collected by a given hospital)

For a terminology T= <N,L,v> define an instantiation

It(T, D)

as the set of all instance vectors <i, p, t> for i in D and p in N

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Instantiation of a term

For each term p, define its t-extension

It(T, D)(p)

as the set of all IUIs i for for which <i, p, t> is included in It(T, D)

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Tracking invariantsFor each p we subject its t-extensions for varying t and D to statistically based factor-analysis in order to determine whether

1. p is in N1(it designates a single universal): the instances in It(T, D)(p) manifest a common invariant pattern

2. p is in N>

3. p is in N<

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We can track patterns for It(T, D)(p)

e.g. in relation to the IUIs for patients in given geographical areas, or at given stages of development and growth

In relation to a given patient, we can track patterns e.g. for different diagnoses, e.g.

It(T, D)(p) vs. It(T, D)(q + r)

to see which gives a better match

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Diagnostic decision-support

Consider the characteristic patterns of correction which arise in the early phases of diagnosis of degenerative diseases such as multiple sclerosis.

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The (true) story of Jane Smith

(with thanks to Werner Ceusters)

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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

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A visit to the hospital City Health Centre Dr. Peters

(City HC) Dr. Longley

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Diagnosis: a severe spiral fracture of the femur

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The City HC’s medical record

captures in a structured form all of the ‘clinically significant’ information in the narrative notes

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Structured Medical Record

www.medappz.com/

04/07/1990 – 17:10Dr. Peters

Jane SmithOrthopedics

Emergency visit: 04/07/1990 – 17.00

Severe

Left upper leg

Since fall on floor

Constant

26442006 closed fracture of shaft of femur

81134009 fracture, closed, spiral

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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

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 ?

ProblemsDifferent patients. Same supermarket? Maybe the same (irrelevant ?) freezer section ?Or different supermarkets, but always in the freezer sections ?

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

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Main problems of EHRsStatements refer only implicitly to the

concrete entities about which they give information.

Codes are general: they tell us only that some instance of the class the codes refer to, is referred to in the statement, but not what instance precisely.

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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 particular / instance

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CUI (coo-ey): Concept Unique Identifier (e.g. a SNOMED code)

UUI (oo-ey): Universal Unique Identifier

IUI (you-ey): Instance Unique Identifier (e.g. a Social Security Number)

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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

UniversalsEHR system

HC

Freezer section

Person

Femur

Fracture

Image

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

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IUI assignmentsThe act of IUI assignment can be represented as:

<IUIa , Ai , td>

IUIa = IUI of the registering agent

Ai = <IUIa, IUIp, tap, c>

IUIa = IUI of the author of the

assertion

IUIp = IUI of the particular

tap = time of assignment

c = optional description

td = time of registering Ai in the IUI-repository

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A SNOMED-CT example <IUI-0945, 18/04/2005, SNOMED-CT v0301, IUI-

1921, 367720001, forever>• #IUI-0945: author of the statement• #IUI-1921: the left testicle of patient #IUI-78127• 367720001: the SNOMED concept-code to which “left testis” is

(in SNOMED) attached as term

So we can denote #IUI-1921 by means of• that left testis• that entire left testis• that testicle, that male gonad, that testis• that genital structure• that physical anatomical entity• BUT NOT: that SNOMED-CT concept

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Pragmatics of IUIs in EHRs

IUI assignment requires (just a bit) more effort compared to current use of general codes from concept-based systems

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IUIs in structured EHRs

www.medappz.com/

04/07/1990 – 17:10Dr. Peters

Jane SmithOrthopedics

Emergency visit: 04/07/1990 – 17.00

Severe

Left upper leg

Since fall on floor

Constant

26442006 closed fracture of shaft of femur

81134009 fracture, closed, spiral

Replaced by the IUI for thepatient’s left upper legThat IUI might be found byusing “left upper leg” as asearch term to query theRTDB

Both replaced by the IUI for that fractureBy means of PTCO statements is the IUI relatedto the SNOMED-codes

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Advantage: betterreality

representation5572 04/07/1990 26442006 closed fracture of shaft of femur

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




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

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

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Other AdvantagesMappings between ontologies and coding

systems created as by-product of tracking– Descriptions about the same particular using

different systems e.g. in different hospitals

Quality control of ontologies and concept-based systems– Systematically inconsistent descriptions within

or across terminologies may indicate poor definition of the respective terms

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Other AdvantagesReferent tracking can be used in decision

support when making diagnoses

We can consider the results of assignment of different clinical codes to one and the same collection of IUIs assembled over a period (and thereby uncover new patterns of symptoms, e.g. in a case of multiple sclerosis)

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ConclusionReferent tracking can solve a number of

problems in an elegant way.Existing (or emerging) technologies can be

used for the implementation.Old technologies can play an interesting

role.Big Brother feeling is to be expected, but

with adequate measures easy to fight.Pilot is being established

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The Endhttp://ifomis.org

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Die Ontologie biomedizinischer Daten

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