VT

1
VT
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Ontologie und die Integration des medizinischen
Wissens

• Barry Smith

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IFOMIS

• Institute for Formal Ontology and
• Medical Information Science
• Faculty of Medicine
• University of Leipzig

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http//ifomis.de

• Institut für formale Ontologie
• und Medizinische Informationswissenschaft

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Ontologie als Zweig der Philosophie

• die Wissenschaften von den Arten und Strukturen
  von Objekten, Qualitäten, Prozessen, Ereignissen,
  Funktionen und Relationen in allen Bereichen der
  Wirklichkeit

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Aristotle
Erste Ontologe

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Eine biologische Ontologie
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Linnaeus

• 1763 Genera Morborum (Nosologie, oder Ontologie
  von Krankheitsarten)

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Q Warum Ontologie in der Informatik?

• A Das Babelturmproblem der Informationssysteme

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Das Babelturmproblem

• Jede Krankenhausinformationssystem verwendet
  eigene Termini- und Kategoriensysteme, um die
  eingegebene Information zu organisieren.
• Wie können wir die Inkompatibiläten lösen, die
  entstehen, wenn Information von verschiedenen
  Quellen zusammgebracht wird?
• Vgl. Wie können wir Chemie und Biologie
  fusionisieren?
• Wie können wir Anatomie und Physiologie
  fusionisieren?

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Wie lösen (z.B. Medizinstudenten) dieses Problem?

• durch die Begegnung mit dem Patienten
• Der Patient und die in ihm ablaufenden Prozesse
  dienen als Kristallisationspunkt für eine
  sinnvolle Ordnung sonst isoliert stehender
  (gelernter) Fakten.Integration entsteht durch
  die Bildung praktischen Wissens
• (aus Wissen-dass wird Wissen-wie)

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Computer sind dumm

• Analog müssen in Medizininformations-systemen
  isolierte Wissensartefakte zu einheitlichem und
  anwendbarem Wissen integriert werden.
• Aber wie?

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Ursprünglicher Traum der Ontologie in der
Informatik

• Eine einzige allumfassende Taxonomie von allen
  Gegenstandsarten, die als zentrale einheitliche
  Kategoriensystem für alle Informationssysteme
  dienen würde.
• Dieser Traum ist ausgeträumt ...

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Gegenwärtige Lösungen

• Standardisierte Terminologien
• UMLS
• SNOMED
• HL7
• ICD-10
• usw.

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UMLS

• Universal Medical Language System
• National Library of Medicine
• Bethesda, MD
• eine Zusammenstellung verschiedener
  maschinenlesbarer Quellterminologien

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Example 1 UMLS

• 134 semantic types
• 800,000 concepts
• 10 million interconcept relationships

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Example 2 SNOMED-RT

• Systematized Nomenclature of Medicine
• A Reference Terminology der American College of
  Pathologists

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Example 2 SNOMED-RT
121,000 concepts, 340,000 relationships commo
n reference point for comparison and aggregation
of data throughout the entire healthcare process
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Standardisierte Terminologien

• sollen Zugriff auf biomedizinische Literatur und
  Faktendatenbanken erleichtern
• eine neue Art medizinischer Forschung soll
  dadurch ermöglicht werden

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Blood
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Representation of Blood in UMLS
Blut als Gewebe
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Representation of Blood in MeSH
Blut als Körperflüssigkeit
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Database standardization

• is desparately needed in medicine
• to enable the huge amounts of data
• resulting from clinical trials by different
  groups working on the same drugs/therapies/diagnos
  tic methods
• to be fused together

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How make ONE SYSTEM out of this?

• To reap the benefits of standardization we need
  to resolve such incompatibilities?

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• Defizite traditioneller Kodiersysteme (SNOMED)
• 1DB-62110Diabetic nephropathy
• 2DB-61000Diabetes mellitus
• G-C025Causing
• D7-11000Nephropathy
• 3DB-61000Diabetes mellitus
• G-C025Causing
• DF-00000Disease
• G-C006Locatedin
• T-71000Kidney
• Fehlende formal Sprache
• Medizinische Begriffs-und Dokumentationssysteme
  WS 2000/2001Barbara Heller, IMISE, UNI
  Leipzig16.01.2001 / Folie 7 von

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• Defizite traditioneller Kodiersysteme (SNOMED)
• DB-62110Diabetic nephropathyG-C006LocatedinT-71000
  Kidney
• 5DB-62110Diabetic nephropathy
• G-C006LocatedinT-11000Bone6DB-62110Diabetic
  nephropathy

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It will develop medical ontologies

• at different levels of granularity
• cell ontology
• drug ontology
• protein ontology
• gene ontology
• already exists (but in a variety of mutually
  incompatible forms)

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and also

• anatomical ontology
• epidemiological ontology
• disease ontology
• therapy ontology
• pathology ontology

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together with

• physicians ontology
• patients ontology
• and even
• hospital management ontology

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Presentation overview

• Problem description patient eligibility for
  clinical trial
• Meaning theories
• Required technology for natural language
  understanding
• Implementation of a realist ontology for medical
  natural language understanding
• Conclusions
• If enough time a guided tour of LinkFactory

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The Medical Informatics Dogma

• Everything should be structured
• Fact computers can only deal with structured
  representations of reality
• structured data
• relational databases, spreadsheets
• structured information
• XML simulates context
• structured knowledge
• rule-based knowledge systems
• Typical conclusion (Dogma?)
• there is a need for structured data, hence
• there is a need for structured data entry

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Structured data entry

• Current technical solutions
• rigid data entry forms
• coding and classification systems
• But
• the description of biological variability
  requires the flexibility of natural language and
  it is generally desirable not to interfere with
  the traditional manner of medical recording
  (Wiederhold, 1980)
• Initiatives to facilitate the entry of narrative
  data have focused on the control rather than the
  ease of data entry (Tanghe, 1997)

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Drawbacks of structured data entry

• Loss of information
• qualitatively
• limited expressiveness and inherent defects of
  coding and classification systems, controlled
  vocabularies, and traditional medical
  terminologies
• use of purpose oriented systems
• dont use data for another purpose than
  originally foreseen (J VDL)
• quantitatively
• too time-consuming to code all information
  manually
• Speech recognition and forms for structured data
  entry are not best friends

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Areas for application of medical natural language
understanding

• Coding patient data
• Structured information extraction from
  unstructured clinical notes
• Clinical protocols and guidelines
• Assessing patient eligibility for clinical trial
  entry
• Triggering and alerts
• Linking case descriptions to scientific
  literature
• Easy access to content
• ... towards a medical semantic web

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Clinical history description

• Mr. Kovács is an 83-year-old man with a past
  medical history of hypertension, congestive heart
  failure, atrial fibrillation, hypercholesterolemia
  , and a history of CVA who presented himself to
  Budapest Emergency Room on April 25 with primary
  complaint of right-sided chest pain since April
  24. The patient was in his usual state of health
  until April 24 when he experienced right-sided
  chest pain after 10 minutes of bicycling exercise
  at the YMCA. He described the chest pain as a
  dull ache in the right side of his chest
  radiating posteriorly to the right scapular area.
  He rated the intensity as 7 out of 10. The chest
  pain lasted about 3 minutes and resolved with
  rest. That same night, the patient once again
  experienced right-sided chest pain while lying in
  bed just before he went to sleep. He describes
  the pain as right-sided chest pain with same
  radiation to posterior at an intensity of 6-7 out
  of 10. The chest pain lasted about 10 minutes and
  resolved spontaneously.

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Inclusion criteria of the INVEST study

• 1. Male or female
• 2. Age 50 to no upper limit
• 3. a) Hypertension documented as according to the
  6th report of the Joint National Committee on
  Detection and Evaluation of the treatment of high
  BP (JNC VI) , b) and the need for drug therapy
  (previously documented hypertension in patients
  currently taking antihypertensive agents is
  acceptable)
• 4. Documented CAD (e.g., classic angina pectoris
  stable angina pectoris Heberden angina
  pectoris), myocardial infarction three or more
  months ago, abnormal coronary angiography, or
  concordant abnormalities on two different types
  of stress tests
• 5. Willingness to sign informed consent

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Do they match ?

• Mr. Kovács is an 83-year-old man with past
  medical history of hypertension, congestive heart
  failure, atrial fibrillation, hypercholesterolemia
  , history of CVA who presented to Budapest
  Emergency Room on April 25 with chief complaint
  of right-sided chest pain since April 24. The
  patient was in his usual state of health until
  April 24 when he experienced right-sided chest
  pain after 10 minutes of bicycling exercise at
  YMCA. He described the chest pain as a dull ache
  in the right side of his chest radiating
  posteriorly to the right scapular area. He rated
  the intensity as 7 out of 10. The chest pain
  lasted about 3 minutes and resolved with rest.
  That same night, the patient once again
  experienced right-sided chest pain while lying in
  bed right before he went to sleep. He describes
  the pain as right-sided chest pain with same
  radiation to posterior at an intensity of 6-7 out
  of 10. The chest pain lasted about 10 minutes and
  resolved spontaneously.

• 1. Male or female
• 2. Age 50 to no upper limit
• 3. Hypertension documented according to the 6th
  report of the Joint National Committee on
  Detection and Evaluation of the treatment of high
  BP (JNC VI) and the need for drug therapy
  (previously documented hypertension in patients
  currently taking antihypertensive agents is
  acceptable)
• 4. Documented CAD (e.g., classic angina pectoris
  (stable angina pectoris Heberden angina
  pectoris), myocardial infarction three or more
  months ago, abnormal coronary angiography, or
  concordant abnormalities on two different types
  of stress tests)
• 5. Willingness to sign informed consent

??
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If the computer is to make this deduction ...

• 1. Male or female
• 2. Age 50 to no upper limit
• 3. Hypertension documented according to the 6th
  report of the Joint National Committee on
  Detection and Evaluation of the treatment of high
  BP (JNC VI) and the need for drug therapy
  (previously documented hypertension in patients
  currently taking antihypertensive agents is
  acceptable)
• 4. Documented CAD (e.g., classic angina pectoris
  (stable angina pectoris Heberden angina
  pectoris), myocardial infarction three or more
  months ago, abnormal coronary angiography, or
  concordant abnormalities on two different types
  of stress tests)
• 5. Willingness to sign informed consent

• Mr. Kovács is an 83-year-old man with past
  medical history of hypertension, congestive heart
  failure, atrial fibrillation, hypercholesterolemia
  , history of CVA who presented to Budapest
  Emergency Room on April 25 with chief complaint
  of right-sided chest pain since April 24. The
  patient was in his usual state of health until
  April 24 when he experienced right-sided chest
  pain after 10 minutes of bicycling exercise at
  YMCA. He described the chest pain as a dull ache
  in the right side of his chest radiating
  posteriorly to the right scapular area. He rated
  the intensity as 7 out of 10. The chest pain
  lasted about 3 minutes and resolved with rest.
  That same night, the patient once again
  experienced right-sided chest pain while lying in
  bed right before he went to sleep. He describes
  the pain as right-sided chest pain with same
  radiation to posterior at an intensity of 6-7 out
  of 10. The chest pain lasted about 10 minutes and
  resolved spontaneously.

... it must be able to understand !
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What is understanding ?

• To understand something is to know what its
  significance is.
• What 'knowing significance' amounts to may be
  very different in different contexts thus
  understanding a piece of music requires different
  things of us than understanding a sentence in a
  language we are learning, for instance. It would
  be useful, then, for theorists to look at the
  different kinds of understanding that there are,
  and examine them in detail and without prejudice,
  rather than looking for the essence of
  understanding.
• (Tim Crane, philosopher of mind)
• The significance of a single sentence, too, can
  vary from context to context.

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The etymology of understanding

• understanding ? Latin substare
• literally to stand under
• Websters Dictionary (1961) understanding the
  power to render experience intelligible by
  bringing perceived particulars under appropriate
  concepts.
• particulars what is NOT SAID of a subject
  (Aristotle)
• substances this patient, that tumor, ...
• qualities the red of that patients skin, his
  body temperature, blood pressure, ...
• processes that incision made by that surgeon,
  the rise of that patients temperature,...
• concepts may be taken in the above definition
  as Aristotles universals what is SAID OF a
  subject
• Substantial concepts patient, tumor, ...
• Quality concepts white, temperature
• ...

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What is natural language understanding?

• NLU is constructing meaning from written
  language by which the degree of understanding
  involves a multifaceted meaning-making process
  that depends on knowledge about language and
  knowledge about the world.
• ( cf. reading comprehension by humans. )
• But then what is meaning

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Why are concepts not enough?

• Why must our theory address also the referents in
  reality?
• Because referents are observable fixed points in
  relation to which we can work out how the
  concepts used by different communities relate to
  each other
• Because only by looking at referents can we
  establish the degree to which concepts are good
  for their purpose.

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But why then this fixation on normative
concepts in Medical Informatics (standards) ?

• CEN/TC251 ENV 12264
• This ENV is applicable to the description of the
  categorial structure of systems of concepts
  supporting computer-based terminological systems,
  including coding systems, for health-care.
• concept unit of thought constituted through
  abstraction on the basis of properties common to
  a set of one or more referents
• BUT THEY NEVER IN FACT LOOK AT THE REFERENTS AT
  ALL!
• ISO/TC215/N142 Health informatics Vocabulary of
  terminology
• The purpose of this International Standard is to
  define a set of basic concepts required to
  describe and discuss formal representation of
  concepts and characteristics, for use especially
  in formal computer based concept representation
  systems.
• concept unit of knowledge created by a unique
  combination of characteristics
• THEY ARE ALREADY TWO LEVELS REMOVED FROM THE
  REFERENT!

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Why existing ontologies dont match OUR
needsfor a core ontology

• MeSH inconsistency in hierarchical relationships
• MedDRA no difference between concepts and terms
• UMLS integrates various source terminologies
  without taking different meanings of terms,
  different structures, different purposes, etc...
  into account
• SNOMED formal system, but lacks sufficient depth
  of the ontology
• GALEN very detailed ontology for some parts of
  healthcare but very poor coverage over healthcare
  as a whole. The ontology is independent from
  language as medium of communication (the ontology
  does not accept language as part of reality)
• ...

Most important all of them deal with alternative
realities or possible worlds and none is focused
on the referents in THIS world !
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Based on formal ontology
HAS-PARTIAL-SPATIAL-OVERLAP
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Example joint anatomy

• joint HAS-HOLE joint space
• joint capsule IS-OUTER-LAYER-OF joint
• meniscus
• IS-INCOMPLETE-FILLER-OF joint space
• IS-TOPO-INSIDE joint capsule
• IS-NON-TANGENTIAL-MATERIAL-PART-OF joint
• joint
• IS-CONNECTOR-OF bone X
• IS-CONNECTOR-OF bone Y
• synovia
• IS-INCOMPLETE-FILLER-OF joint space
• synovial membrane IS-BONAFIDE-BOUNDARY-OF joint
  space

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Linking external ontologies
48
Linguistic and domain ontologies
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From text to meaning
50
Mr Kovács analysed syntactically, and features
used to drive mapping.

• The Orth slot of a word gives its surface string.
• The append( ) operator joins together its
  arguments as a single string.

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Conclusions

• Understanding a message comes down to
  identifying what parts of that message correspond
  to reality, and what parts are expressions of
  what doesnt exist.
• If a machine has to understand, it must be based
  on algorithms that use a realist ontology that
  takes the world, language(s) and the relationship
  amongst them, properly into account.
• The medical informatics community (specifically
  that part dealing with concept systems) must
  become aware that most current approaches confuse
  what is real with what is thought to be real.

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The Reference Ontology Community

• IFOMIS (Leipzig)
• Laboratories for Applied Ontology (Trento/Rome,
  Turin)
• Foundational Ontology Project (Leeds)
• Ontology Works (Baltimore)
• Ontek Corporation (Buffalo/Leeds)
• Language and Computing (LC) (Belgium/Philadelphia
  )

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Domains of Current Work

• IFOMIS Leipzig Medicine, Bioinformatics
• Laboratories for Applied Ontology
• Trento/Rome Ontology of Cognition/Language
• Turin Law
• Foundational Ontology Project Space, Physics
• Ontology Works Genetics, Molecular Biology
• Ontek Corporation Biological Systematics
• Language and Computing Natural Language
  Understanding

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Testing the BFO/MedO approach

• collaboration with
• Language and Computing nv (www.landcglobal.be)

55

• LCs Semantic Indexing for Smart Information
  Retrieval and Extraction solution allows
  companies to more efficiently and accurately
  manage and retrieve documents. LC also offers
  solutions for information analysis, document
  mining, information extraction, and coding.

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LC Technology

• FreePharma, LCs natural language analyzer for
  converting free text (spoken or typed)
  prescription and pharmacology information into
  XML.
• FastCode, LCs automated clinical coding
  product for translation of free text strings into
  ICD, SNOMED, MedDRA, etc.
• LinKBase, the largest formal medical knowledge
  base in the world, representing medicine in such
  a way that it is understandable for a computer.
• LinKFactory, LCs product suite for developing
  and managing large formal multilingual
  ontologies.

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LC statistical technology

• unearthed errors in SNOMED via pattern-recognition
  of semantic connections

58
The Project

• collaborate with LC to show how an ontology
  constructed on the basis of philosophical
  principles can help in overhauling and validating
  the large terminology-based medical ontology
  LinkBase used by LC for NLP

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LC

• LinKBase worlds largest terminology-based
  ontology
• with mappings to UMLS, SNOMED, etc.
• LinKFactory suite for developing and managing
  large terminology-based ontologies

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LinKBase

• BFO and MedO designed to add better reasoning
  capacity
• by tagging LinKBase domain-entities with
  corresponding BFO/MedO categories
• by constraining links within LinKBase according
  to the theory of granular partitions

61
Three levels of ontology

• 1) formal ontology, seeks the construction of a
  framework of the categories object, event,
  whole, part employed in every domain,
• 2) domain ontology, a top-level system applying
  the structure of formal ontology to a particular
  domain, such as medicine or genetics,
• 3) terminology-based ontology, a very large,
  lower-level system dealing with the complete
  terminology of a given domain.

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LCs long-term goal

• Transform the mass of unstructured patient
  records into a gigantic medical experiment

63
IFOMISs long-term goal

• Build a robust high-level BFO-MedO framework
• THE WORLDS FIRST INDUSTRIAL-STRENGTH PHILOSOPHY
• which can serve as the basis for an
  ontologically coherent unification of medical
  knowledge and terminology

64
The solution

• ONTOLOGY!
• But what does ontology mean?

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Example The Gene Ontology (GO)

• hormone GO0005179
• digestive hormone GO0046659
• peptide hormone GO0005180 adrenocorticotrop
  in GO0017043 glycopeptide hormone
  GO0005181 follicle-stimulating hormone
  GO0016913
• subsumption (lower term is_a higher term)

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as tree

• hormone
• digestive hormone peptide hormone
• adrenocorticotropin
  glycopeptide hormone
• 
  follicle-stimulating hormone

67
GO

• is very useful for purposes of standardization in
  the reporting of genetic information
• but it is not much more than a telephone
  directory of standardized designations organized
  into hierarchies

68
First Problem

• can in practice be used only by trained
  biologists (know how)
• whether a GO-term stands in the subsumption
  relationship depends on the context in which the
  term is used
• (for example on the type of organism)

69
Second Problem

• GDB a gene is a DNA fragment that can be
  transcribed and translated into a protein
• GenBank a gene is a DNA region of biological
  interest with a name and that carries a genetic
  trait or phenotype
• GO uses gene in its term hierarchy,
• but it does not tell us which of these
  definitions is correct

70
GO

• has no robust formal organization
• no capability to be aligned with systems which
  would have the power to use it to reason with
  genetic information

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SNOMED RT (2000)

• already has description logic definitions
• but it also has some bad coding, which derives
  from failure to pay attention to ontological
  principles
• e.g.
• both testes is_a testis

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How resolve such incompatibilities?

• enforce terminological compatibility via
  standardized term hierarchies, with standardized
  definitions of terms

73
Problem People are lazy

• Half the pages on Geocities are called Please
  title this page

74
Problem People are stupid

• The vast majority of the Internet's users
• (even those who are native speakers of English)
• cannot spell or punctuate
• Will internet users learn to accurately tag
  their information with whatever hierarchy they're
  supposed to be using?

75
Solutions in the medical domain

• Problem 1 People lie
• Problem 2 People are lazy
• Problem 3 People are stupid
• None of these is true in the world of medical
  informatics

76
Solutions in the medical domain

• Problem 1 People lie
• Problem 2 People are lazy
• Problem 3 People are stupid
• Achieve quality control via division of labour

77
Division of Labour

• 1. Clinical activities
• 2. Structured data representation
• 3. Software coding (e.g. for NLP)

78
Division of Labour

• 1. Clinical activities
• 2. Structured data representation
• 3. Software coding
• 4. Ontology building
• Use 4. to constrain 2. and 3.
• to achieve better data processing via quality
  control

79
Problem Multiple descriptions

• Requiring everyone to use the same vocabulary to
  describe their material is not always medically
  practicable

80
Clinicians

• often do not use category systems at all they
  use unstructured text
• from which usable data has to be extracted in a
  further step
• Why?
• Because every case is different, much patient
  data is context-dependent

81
David J. Rothwell(one of the two original
authors of SNOMED)

• The notion of a standard vocabulary and in
  particular a coding system to serve as the answer
  to the ills besetting adoption of an Electronic
  Medical Record is, in my view, quite wrong.
  Traditional coding schemes, SNOMED included, are
  a nineteenth century idea, that despite 100 years
  of effort have failed. There are narrowly defined
  areas where codes function well, but these areas
  must be precisely defined. e.g. ICD-O, Drugs,
  organisms. It is my belief that natural language
  is the "code" that, despite its difficulties, we
  must learn to work with to address the issues
  encountered in a medical record.

82
SARS

• is NOT
• Severe Acute Respiratory Syndrome
• it is THIS collection of instances of
• Severe Acute Respiratory Syndrome
• associated with THIS coronavirus and ITS mutations

83
BFO

• ontology not the standardization or
  specification of concepts
• (not a branch of knowledge or concept
  engineering)
• but an inventory of the types of entities
  existing in reality

84
BFO goal

• to remove ontological impedance by constraining
  terminology systems with good ontology

85
BFO not a computer application

• but a reference ontology
• (not a reference terminology
• in the sense of SNOMED)

86
Recall

• GDB a gene is a DNA fragment that can be
  transcribed and translated into a protein
• Genbank a gene is a DNA region of biological
  interest with a name and that carries a genetic
  trait or phenotype

87
Ontology

• fragment, region, name, carry, trait,
  type
• ... part, whole, function, inhere,
  substance
• are ontological terms in the sense of traditional
  (philosophical) ontology

88
UMLS Semantic Network

• entity event
• physical conceptual
• entity entity
• idea of concept

89

• Idea or Concept
• Functional Concept
• Qualitative Concept
• Quantitative Concept
• Spatial Concept
• Body Location or Region
• Body Space or Junction
• Geographic Area
• Molecular Sequence
• Amino Acid Sequence
• Carbohydrate Sequence
• Nucleotide Sequence

90
UMLS has ontological problems, too

• Idea or Concept
• Functional Concept
• Qualitative Concept
• Quantitative Concept
• Spatial Concept
• Body Location or Region
• Body Space or Junction
• Geographic Area
• Molecular Sequence
• Amino Acid Sequence
• Carbohydrate Sequence
• Nucleotide Sequence

91
Sachsen-Anhalt

• is an Idea or Concept

92
UMLS has ontological problems, too

• Idea or Concept
• Functional Concept
• Qualitative Concept
• Quantitative Concept
• Spatial Concept
• Body Location or Region
• Body Space or Junction
• Geographic Area
• Molecular Sequence
• Amino Acid Sequence
• Carbohydrate Sequence
• Nucleotide Sequence

93
Testing the BFO/MedO approach

• collaboration with
• Language and Computing nv (www.landcglobal.be)

94
The Project

• collaborate with LC to show how an ontology
  constructed on the basis of philosophical
  principles can help in overhauling and validating
  the large terminology-based medical ontology
  LinkBase used by LC for NLP

95
LC

• LinKBase worlds largest terminology-based
  ontology
• with mappings to UMLS, SNOMED, etc.
• LinKFactory suite for developing and managing
  large terminology-based ontologies

96
LinKBase

• BFO and MedO designed to add better reasoning
  capacity
• by tagging LinKBase domain-entities with
  corresponding BFO/MedO categories
• by constraining links within LinKBase according
  to the theory of granular partitions

97
LCs long-term goal

• Transform the mass of unstructured patient
  records into a gigantic medical experiment

98
IFOMISs long-term goal

• Build a robust high-level BFO-MedO framework
• THE WORLDS FIRST INDUSTRIAL-STRENGTH PHILOSOPHY
• which can serve as the basis for an
  ontologically coherent unification of medical
  knowledge and terminology

99
END

• http//ontologist.com
• http//ifomis.de