ICBO Tutorial Introduction to Referent Tracking July 22, 2009 112 Norton Hall, UB North Campus Werner CEUSTERS Center of Excellence in Bioinformatics.

ICBO Tutorial Introduction to Referent Tracking July 22, 2009 112 Norton Hall, UB North Campus
Werner CEUSTERS Center of Excellence in Bioinformatics and Life Sciences Ontology Research Group University at Buffalo, NY, USA (corrected version: August 10, 2009)

Short personal history
? 1977 2006 Short personal history 2004 1989 1992 2002 1995 1998 1993

House keeping rules Feel free to ask clarifications at any time if you don’t understand something I just said (but not more than three slides earlier); Please do not interrupt me if you ‘just’ disagree with something I say until: near beginning of the break, near end of the tutorial; Everybody in the audience may sleep except those students who are here for credit, I’ll test them redundancy in my slides serves thus a purpose: to help them !

Tutorial overview Setting the scene: a rough description of what Referent Tracking is and why it is important Review the basics of BFO relevant to RT The crucial distinction between representations and what they represent Implementation of RT systems Examples of use

Prologue: Referent Tracking: What and Why ?

When did Weiss kill Senator Long ?
time Carl Weiss’ living Weiss’ shooting of Long Bodyguards’shooting of Weiss Long’s pathological body reactions Weiss’s path. body reactions Senator Long’s living

What is Referent Tracking ?
A paradigm under development since 2005, based on Basic Formal Ontology, designed to keep track of relevant portions of reality and what is believed and communicated about them, enabling adequate use of realism-based ontologies, terminologies, thesauri, and vocabularies, originally conceived to track particulars on the side of the patient and his environment denoted in his EHR, but since then studied in and applied to a variety of domains, and now evolving towards tracking absolutely everything, not only particulars, but also universals.

‘The spectrum of the Health Sciences’
Turning data in knowledge ?

Source of all data Reality !

Ultimate goal of Referent Tracking
A digital copy of the world

Requirements for this digital copy
R1: A faithful representation of reality R2 … of everything that is digitally registered, what is generic  scientific theories what is specific  what individual entities exist and how they relate R3: … throughout reality’s entire history, R4 … which is computable in order to … … allow queries over the world’s past and present, … make predictions, … fill in gaps, … identify mistakes, ...

In fact … the ultimate crystal ball

I don’t want a cartoon of the world
The ‘binding’ wall How to do it right ? I don’t want a cartoon of the world

Distinction between Ontologies and Information Models
Ontologies should represent only what is always true about the entities of a domain (whether or not it is known to the person that reports), Information models (or data structures) should only represent the artifacts in which information is recorded. Such information may be incomplete and error-laden which needs to be accounted for in the information model rather than in the ontology itself.

Perfect ‘semantic’ tools are useless …
… if data captured at the source is not of high quality Prevailing EHR systems don’t allow data to be stored at acceptable quality level: No formal distinction between disorders and diagnosis Messy nature of the notions of ‘problem’ and ‘concern’ No unique identification of the entities about which data is stored Unique IDs for data-elements cannot serve as unique IDs for the entities denoted by these data-elements

Terminologies for ‘unambiguous representation’ ???
5572 04/07/1990 closed fracture of shaft of femur Fracture, closed, spiral 12/07/1990 Accident in public building (supermarket) 79001 Essential hypertension 0939 24/12/1991 benign polyp of biliary tract 2309 21/03/1992 47804 03/04/1993 Other lesion on other specified region 17/05/1993 298 22/08/1993 Closed fracture of radial head 01/04/1997 PtID Date ObsCode Narrative 20/12/1998 malignant polyp of biliary tract

5572 04/07/1990 closed fracture of shaft of femur Fracture, closed, spiral 12/07/1990 Accident in public building (supermarket) 79001 Essential hypertension 0939 24/12/1991 benign polyp of biliary tract 2309 21/03/1992 47804 03/04/1993 Other lesion on other specified region 17/05/1993 298 22/08/1993 Closed fracture of radial head 01/04/1997 PtID Date ObsCode Narrative 20/12/1998 malignant polyp of biliary tract If two different fracture codes are used in relation to observations made on the same day for the same patient, do they denote the same fracture ?

5572 04/07/1990 closed fracture of shaft of femur Fracture, closed, spiral 12/07/1990 Accident in public building (supermarket) 79001 Essential hypertension 0939 24/12/1991 benign polyp of biliary tract 2309 21/03/1992 47804 03/04/1993 Other lesion on other specified region 17/05/1993 298 22/08/1993 Closed fracture of radial head 01/04/1997 PtID Date ObsCode Narrative 20/12/1998 malignant polyp of biliary tract If the same fracture code is used for the same patient on different dates, can these codes denote the same fracture?

5572 04/07/1990 closed fracture of shaft of femur Fracture, closed, spiral 12/07/1990 Accident in public building (supermarket) 79001 Essential hypertension 0939 24/12/1991 benign polyp of biliary tract 2309 21/03/1992 47804 03/04/1993 Other lesion on other specified region 17/05/1993 298 22/08/1993 Closed fracture of radial head 01/04/1997 PtID Date ObsCode Narrative 20/12/1998 malignant polyp of biliary tract Can the same fracture code used in relation to two different patients denote the same fracture?

5572 04/07/1990 closed fracture of shaft of femur Fracture, closed, spiral 12/07/1990 Accident in public building (supermarket) 79001 Essential hypertension 0939 24/12/1991 benign polyp of biliary tract 2309 21/03/1992 47804 03/04/1993 Other lesion on other specified region 17/05/1993 298 22/08/1993 Closed fracture of radial head 01/04/1997 PtID Date ObsCode Narrative 20/12/1998 malignant polyp of biliary tract Can two different tumor codes used in relation to observations made on different dates for the same patient, denote the same tumor ?

5572 04/07/1990 closed fracture of shaft of femur Fracture, closed, spiral 12/07/1990 Accident in public building (supermarket) 79001 Essential hypertension 0939 24/12/1991 benign polyp of biliary tract 2309 21/03/1992 47804 03/04/1993 Other lesion on other specified region 17/05/1993 298 22/08/1993 Closed fracture of radial head 01/04/1997 PtID Date ObsCode Narrative 20/12/1998 malignant polyp of biliary tract Do three references of ‘hypertension’ for the same patient denote three times the same disease?

Can the same type of location code used in relation to three different events denote the same location? Terminologies for ‘unambiguous representation’ ??? 5572 04/07/1990 closed fracture of shaft of femur Fracture, closed, spiral 12/07/1990 Accident in public building (supermarket) 79001 Essential hypertension 0939 24/12/1991 benign polyp of biliary tract 2309 21/03/1992 47804 03/04/1993 Other lesion on other specified region 17/05/1993 298 22/08/1993 Closed fracture of radial head 01/04/1997 PtID Date ObsCode Narrative 20/12/1998 malignant polyp of biliary tract

How will we ever know ? PtID Date ObsCode Narrative 5572 04/07/1990
closed fracture of shaft of femur Fracture, closed, spiral 12/07/1990 Accident in public building (supermarket) 79001 Essential hypertension 0939 24/12/1991 benign polyp of biliary tract 2309 21/03/1992 47804 03/04/1993 Other lesion on other specified region 17/05/1993 298 22/08/1993 Closed fracture of radial head 01/04/1997 PtID Date ObsCode Narrative 20/12/1998 malignant polyp of biliary tract

The problem in a nutshell
Generic terms used to denote specific entities do not have enough referential capacity Usually enough to convey that some specific entity is denoted, Not enough to be clear about which one in particular. For many ‘important’ entities, unique identifiers are used: UPS parcels Patients in hospitals VINs on cars …

Fundamental goals of ‘our’ Referent Tracking
explicit reference to the concrete individual entities relevant to the accurate description of some portion of reality, ... Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform Jun;39(3):

Method: numbers instead of words
Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity 235 78 5678 321 322 666 427 Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform Jun;39(3):

Fundamental goals of ‘our’ Referent Tracking
Use these identifiers in expressions using a language that acknowledges the structure of reality e.g.: a yellow ball: #1: the ball #2: #1’s yellow Then not: ball(#1) and yellow(#2) and hascolor(#1, #2) But: instance-of(#1, ball, since t) instance-of(#2, yellow, since t) inheres-in(#1, #2, since t) Strong foundations in realism-based ontology

Codes for ‘types’ AND identifiers for instances
5572 04/07/1990 closed fracture of shaft of femur Fracture, closed, spiral 12/07/1990 Accident in public building (supermarket) 79001 Essential hypertension 0939 24/12/1991 benign polyp of biliary tract 2309 21/03/1992 47804 03/04/1993 Other lesion on other specified region 17/05/1993 298 22/08/1993 Closed fracture of radial head 01/04/1997 PtID Date ObsCode Narrative 20/12/1998 malignant polyp of biliary tract IUI-001 IUI-003 IUI-004 IUI-005 IUI-007 IUI-002 IUI-012 IUI-006 7 distinct disorders

‘Principles for Success’
Evolutionary change Radical change: Principle 6: Architect Information and Workflow Systems to Accommodate Disruptive Change Organizations should architect health care IT for flexibility to support disruptive change rather than to optimize today’s ideas about health care. Principle 7: Archive Data for Subsequent Re-interpretation Vendors of health care IT should provide the capability of recording any data collected in their measured, uninterpreted, original form, archiving them as long as possible to enable subsequent retrospective views and analyses of those data.NOTE Willam W. Stead and Herbert S. Lin, editors; Committee on Engaging the Computer Science Research Community in Health Care Informatics; National Research Council. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions (2009)

‘Principles for Success’ (continued)
The NOTE: ‘See, for example, Werner Ceusters and Barry Smith, “Strategies for Referent Tracking in Electronic Health Records” Journal of Biomedical Informatics 39(3): , June 2006.’ Willam W. Stead and Herbert S. Lin, editors; Committee on Engaging the Computer Science Research Community in Health Care Informatics; National Research Council. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions (2009)

Words, words, words, … A paradigm under development since 2005,
based on Basic Formal Ontology, designed to keep track of relevant portions of reality and what is believed and communicated about them, enabling adequate use of realism-based ontologies, terminologies, thesauri, and vocabularies, originally conceived to track particulars on the side of the patient and his environment denoted in his EHR, but since then studied in and applied to a variety of domains, and now evolving towards tracking absolutely everything, not only particulars, but also universals.

Therefore: Part 1: the Basics No (good) Referent Tracking without (good) Realism-based Ontology

Basic axioms There is an external reality which is ‘objectively’ the way it is; That reality is accessible to us; We build in our brains cognitive representations of reality; We communicate with others about what is there, and what we believe there is there. Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, Biomedical Ontology in Action, November 8, 2006, Baltimore MD, USA

What is there ? some universal some particular
The parts of BFO relevant for Referent Tracking (1) some universal instanceOf … some particular

The shift envisioned From: To (something like):
‘this person is a 40 year old patient with a stomach tumor’ To (something like): ‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … …

denotators for particulars
The shift envisioned From: ‘this man is a 40 year old patient with a stomach tumor’ To (something like): ‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … … denotators for particulars

denotators for appropriate relations
The shift envisioned From: ‘this man is a 40 year old patient with a stomach tumor’ To (something like): ‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … … denotators for appropriate relations

denotators for universals or particulars
The shift envisioned From: ‘this man is a 40 year old patient with a stomach tumor’ To (something like): ‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … … denotators for universals or particulars

something I’ll come to later
The shift envisioned From: ‘this man is a 40 year old patient with a stomach tumor’ To (something like): ‘this-1 on which depend this-2 and this-3 has this-4’, where this-1 instanceOf human being … this-2 instanceOf age-of-40-years … this-2 qualityOf this-1 … this-3 instanceOf patient-role … this-3 roleOf this-1 … this-4 instanceOf tumor … this-4 partOf this-5 … this-5 instanceOf stomach … this-5 partOf this-1 … … something I’ll come to later

Relevance: the way RT-compatible systems ought to interact with representations of generic portions of reality instance-of at t #105 caused by

What is there ? some universal some particular
The parts of BFO relevant for Referent Tracking (1) for every universal there is or has been at least one instance some universal instanceOf … entities on either site cannot ‘cross’ this boundary every particular is an instance of at least one universal some particular

My terminology (1) ‘entity’: ‘instance’:
denotes either a universal or a particular ‘instance’: denotes a particular to which I refer in the context of some universal: If A instanceOf B … then ‘B is a universal’ ‘A is a particular’ ‘A is an instance’

My terminology (1) do not denote isa !!!
‘entity’: denotes either a universal or a particular ‘instance’: denotes a particular to which I refer in the context of some universal: If A instanceOf B … then ‘B is a universal’ ‘A is a particular’ ‘A is an instance’ do not denote isa !!!

My terminology (2) ‘entity’: ‘instance’:
denotes either a universal or a particular ‘instance’: denotes a particular to which I refer in the context of some universal: If A instanceOf B … then ‘B is a universal’ ‘A is a particular’ ‘A is an instance’ ‘denotes’: (roughly for now) a relation between an entity and a representational construct (sign, symbol, term,…) such that the latter stands for the former in descriptions about reality.

What is there ? some universal ? some particular
The parts of BFO relevant for Referent Tracking (1) some universal ? instanceOf … some particular

The parts of BFO relevant for Referent Tracking (2)
What is there ? The parts of BFO relevant for Referent Tracking (2) some continuant universal some occurrent universal instanceOf at t instanceOf some continuant particular some occurrent particular

The importance of temporal indexing
benign tumor malignant tumor stomach instanceOf at t2 instanceOf at t1 instanceOf at t2 instanceOf at t1 partOf at t1 this-4 this-1’s stomach partOf at t2

Use of the CEN Time Standard for HIT

Things do change indeed
child adult vampire person t Living creature animal caterpillar butterfly

The continuants relevant for Referent Tracking
spatial region independent continuant dependent continuant specifically dependent continuant generically dependent continuant material object (corrected version: SDC and GDC were interchanged) site information content entity … terminology ontology

My terminology (3) ‘ontology’: ‘terminology’:
denotes an information artifact whose representational elements denote universals - either directly or indirectly - and whose structure is intended to mimic the structure of reality ‘terminology’: denotes an information artifact whose representational elements are terms from some language that are defined in terms of other terms and that are structured independent of the structure of reality

MeSH-2008: give me 666 reasons why this is not an ontology under my terminology.
Wolfram Syndrome All MeSH Categories Diseases Category Nervous System Diseases Cranial Nerve Diseases Optic Nerve Optic Atrophy Optic Atrophies, Hereditary Eye Diseases Eye Diseases, Hereditary Optic Nerve Diseases Male Urogenital Diseases Urologic Diseases Kidney Diseases Diabetes Insipidus Female Urogenital Diseases and Pregnancy Complications Neurodegenerative Diseases Heredodegenerative Disorders, Nervous System

What would it mean if used in the context of a patient ?
??? Wolfram Syndrome All MeSH Categories Diseases Category Nervous System Diseases Cranial Nerve Diseases Optic Nerve Optic Atrophy Optic Atrophies, Hereditary ??? Eye Diseases Eye Diseases, Hereditary Optic Nerve Diseases Male Urogenital Diseases Urologic Diseases Kidney Diseases Diabetes Insipidus Female Urogenital Diseases and Pregnancy Complications Neurodegenerative Diseases Heredodegenerative Disorders, Nervous System … has has

Snomed CT (July 2007): Why not an ontology ?

Cause: coding / classification confusion
‘A patient with a fractured nasal bone’ means the same thing as ‘A patient with a broken nose’ means the same thing as ‘A patient with a fracture of the nose’ note: doesn’t say what it means

Cause: coding / classification confusion
A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose = A patient with a fractured nasal bone A patient with a broken nose A patient with a fracture of the nose =

The occurrents relevant for Referent Tracking
spatiotemporal region temporal region process contiguous temporal region scattered temporal region history time instant time interval

PtoU: instanceOf, lacks,
Sorts of relations UtoU: isa, partOf(UU), … U1 U2 PtoU: instanceOf, lacks, denotes(PU)… PtoP: partOf, denotes, … P1 P2

Putting the pieces together: what is there to track?
dependent continuant material object spacetime region spatial region temporal region history instanceOf t t t my 4D STR occupies my life participantOf at t projectsOn at t projectsOn some spatial region some quality me some temporal region located-in at t … at t

Part 2: Let’s get more serious about ‘representation’ (in general)
Beware !!! Colors don’t really matter but in what follows I used them in different ways than before.

‘Marriage’ … marriage of Bill and Hillary marriage human being Bill
createdBy … marriage of Bill and Hillary instanceOf marriage husbandIn spouseIn human being Bill Clinton husbandOf instanceOf Hillary Clinton spouseOf instanceOf

Time and the Bill-Hillary marriage: what about the various some t’s ?
createdBy at some t … exists at some t exists at some t marriage of Bill and Hillary at some t instanceOf marriage exists at some t husbandIn at some t at some t exists at some t spouseIn human being at some t Bill Clinton husbandOf at some t instanceOf Hillary Clinton spouseOf exists at some t at some t at some t exists at some t instanceOf

Representation of the Bill-Hillary marriage
createdBy at some t … exists at some t ‘ , exists at some t ‘marriage of Bill and Hillary’ at some t instanceOf ‘marriage’ exists at some t ‘ , husbandIn ‘ , at some t at some t exists at some t spouseIn ‘human being’ at some t ‘Bill Clinton’ husbandOf at some t instanceOf ‘Hillary Clinton’ spouseOf exists at some t at some t at some t exists at some t instanceOf

Representation and what it is about
? at some t

Representations as first order entities (1)
?1 at some t isa instanceOf ?3 at some t instanceOf ?2 isa

Representations as first order entities (2)
L1 about R ontology about

Two sorts of representations
L1 R L2 L3 symbolizations beliefs ‘about’

Three levels of reality

Diseases : L1  Diagnoses L2/L3
Diagnosis: A configuration of representational units; Believed to mirror the person’s disease; Believed to mirror the disease’s cause; Refers to the universal of which the disease is believed to be an instance. Disease isa Pneumococcal pneumonia Instance-of at t1 #78 John’s portion of pneumococs #56 John’s Pneumonia caused by

Some motivations and consequences (1)
The same diagnosis can be expressed in various forms. Disease isa caused by Instance-of at t1 #56 #78 Pneumonia caused by Portion of pneumococs #56 #78 Pneumococcal pneumonia caused by Instance-of at t1

A diagnosis can be of level 2 or level 3, i.e. either in the mind of a cognitive agent, or in some physical form. Allows for a clean interpretation of assertions of the sort ‘these patients have the same diagnosis’:  The configuration of representational units is such that the parts which do not refer to the particulars related to the respective patients, refer to the same portion of reality.

Distinct but similar diagnoses
Pneumococcal pneumonia Instance-of at t1 Instance-of at t2 #78 John’s portion of pneumococs #56 John’s Pneumonia #956 Bob’s pneumonia #2087 Bob’s portion of pneumococs caused by caused by

Allows evenly clean interpretations for the wealth of ‘modified’ diagnoses: With respect to the author of the representation: ‘nursing diagnosis’, ‘referral diagnosis’ When created: ‘post-operative diagnosis’, ‘admitting diagnosis’, ‘final diagnosis’ Degree of the belief: ‘uncertain diagnosis’, ‘preliminary diagnosis’

Important to differentiate between Lexical, semantic and ontological relations
‘gall’ ‘gallbladder’ ‘urinary bladder’ ‘urine’ ‘urinary bladder inflammation’ ‘gallbladder ‘inflammation’ gall gall bladder bladder inflammation urine cystitis biliary cystitis gallbladder urinary

The three levels applied to diabetes management
Generic Specific 3. Representation ‘person’ ‘drug’ ‘insulin’ ‘W. Ceusters’ ‘my sugar’ 2. Beliefs (knowledge) DIAGNOSIS INDICATION my doctor’s work plan diagnosis 1. First-order reality me my blood glucose level my NIDDM my doctor my doctor’s computer MOLECULE PERSON DISEASE PATHOLOGICAL STRUCTURE PORTION OF INSULIN DRUG

The three levels applied to C2
Basic Formal Ontology Generic Specific Referent Tracking 3. Representation ‘weapon’ ‘person’ ‘tank’ ‘John Doe’ ‘Enola Gay’ 2. Beliefs (knowledge) GOAL John Doe’s plan SACEUR’s strategy ATTACK STRATEGY 1. First-order reality Private John Doe building PERSON John Doe’s platoon John Doe’s gun WEAPON CORPSE TANK SOLDIER Tank with serial number TH1280A44V

Terminology is too reductionist
What concepts do we need? How do we name concepts properly?

And often confuse L3 with L1
‘Head’ in the NCIT

The power of realism in ontology design
Reality as benchmark ! 1. Is the scientific ‘state of the art’ consistent with biomedical reality ?

Reality as benchmark ! 2. Is my doctor’s knowledge up to date?

Reality as benchmark ! 3. Does my doctor have an accurate assessment of my health status?

Reality as benchmark ! 4. Is our terminology rich enough to communicate about all three levels?

Reality as benchmark ! 5. How can we use case studies better to advance the state of the art?

Central mechanism in RT: ‘denotation’
Something like a marriage between an L3-entity and an L1-entity createdBy createdBy … … this particular denotation marriage of Bill and Hillary hasReference referentOf husbandIn spouseIn hasReferent referenceOf husbandOf denotes Bill Clinton Hillary Clinton ‘This green square’ denotedBy spouseOf

Denotation and time: some axioms
D cannot exist if S or R never existed D can continue to exist even when S does not exist anymore the existence of R and S are not sufficient for D to exist D ceases to exist when R ceases to exist … createdBy D … this particular denotation hasReference at t1 referentOf at t1 hasReferent at t1 referenceOf at t1 ‘This green square’ denotes at t1 S1 denotedBy at t1 R S

Denotators with distinct ‘meanings’
createdBy at … this other particular denotation createdBy at … A1 this particular denotation hasReference at t1 hasReferent at t1 S2 ‘This green square’ denotes at t1 S1

‘at’ as defined in CEN:TSHSP thus t2 is the ‘coContinuation’ of t1 S2
Changes in reality A2 createdBy at … this other particular denotation createdBy at … A1 this particular denotation ‘at’ as defined in CEN:TSHSP thus t2 is the ‘coContinuation’ of t1 hasReference at t2 hasReferent at t2 S2 ‘This green square’ denotes at t2 S1 S1 (imagine S1 turned red, yet still being that very same square on the very same spot)

Changes in representations
representationOf at t

Reality and representations
representationOf at t1 representationOf at t2

Reality and representations
representationOf at t1 gain in understanding representationOf at t2

Changes in SNOMED

Reality and representation: both in evolution
IUI-#3 O-#0 O-#2 Repr. O-#1 = “denotes” = what constitutes the meaning of representational units …. Therefore: O-#0 is meaningless

Reality versus representations, both in evolution
IUI-#3 O-#0 O-#2 L2 O-#1 Several types of mismatches between reality and representations

Mistakes, discoveries, being lucky, having bad luck
p3 IUI-#3 O-#0 O-#2 L2 O-#1

In John Smith’s Electronic Health Record: What are the possibilities ?
Changes over time In John Smith’s Electronic Health Record: At t1: “male” at t2: “female” What are the possibilities ? Change in reality: transgender surgery change in legal self-identification Change in understanding: it was female from the very beginning but interpreted wrongly Correction of data entry mistake: it was understood as male, but wrongly transcribed (Change in word meaning)

Part 3: Representation in Referent Tracking

Representations in Referent Tracking
Portion of Reality Relation Entity Configuration represents Universal Particular contains is about Non - referring Information content ent. particular corresponds - to denotes class Representation RT - tuple Representational unit Representations in Referent Tracking Defined class Extension Denotator … … CUI IUI UUI RUI … denotes denotes denotes

Extensions – Defined Classes

Further distinctions amongst PORs in RT

Referent Tracking System

Architecture of a Referent Tracking System (RTS)
RTS: system in which all statements referring to particulars contain the IUIs for those particulars judged to be relevant. Ideally set up as broad as possible: some metrics: % of particulars referred to by means of IUI % of HCs active in a region Geographic region functional region: defined by contacts amongst patients % of patients referred to within a region Services: IUI generator IUI repository: statements about assignments and reservations Referent Tracking ‘Database’ (RTDB): index (LSID) to statements relating instances to instances and classes

Referent Tracking System Components
Referent Tracking Software Manipulation of assertions about L1 Referent Tracking Datastore: IUI repository A collection of globally unique singular identifiers denoting particulars Referent Tracking Database A collection of assertions about the particulars denoted in the IUI repository Manzoor S, Ceusters W, Rudnicki R. Implementation of a Referent Tracking System. International Journal of Healthcare Information Systems and Informatics 2007;2(4):41-58.

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.

Elementary RTS tuple types (1.0)

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 labeling it with a UUID. ‘cognitive agent’: A person; An organization; A device or software agent, e.g. Bank note printer, Image analysis software.

Criteria for IUI assignment (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 Not always a 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)
May not have already been assigned a IUI. Morning star and evening star Himalaya Multiple sclerosis 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

Assertion of assignments
IUI assignment is an act of which the execution has to be asserted in the IUI-repository: Di = <IUId, Ai, td> (1.0) IUId IUI of the registering agent Ai the assertion of the assignment < IUIp, IUIa, tap> IUIa IUI of the author of the assertion IUIp IUI of the particular tap time of the assignment td time of registering Ai in the IUI-repository Neither td or tap give any information about when # IUIp started to exist ! That might be asserted in statements providing information about # IUIp .

D-tuples 2.0: dealing with mistakes
Validity and availability of information Tuple name Attributes Description D-tuple < IUId, IUIA, td, E, C, S > The particular referred to by IUId registers the particular referred to by IUIA (the IUI for the corresponding A-tuple) at time td. E is either the symbol ‘I’ (for insertion) or any of the error type symbols as defined in [1]. C is the reason for inserting the A-tuple. S is a list of IUIs denoting the tuples, if any, that replace the retired one. A D-tuple is inserted: to resolve mistakes in RTS, and whenever a new tuple other than a D-tuple is inserted in the RTS. [1] Ceusters W. Dealing with Mistakes in a Referent Tracking System. In: Hornsby KS (eds.) Proceedings of Ontology for the Intelligence Community 2007 (OIC-2007), Columbia MA, November 2007;:5-8.

Types of matches and mismatches

Management of the IUI-repository
Adequate safety and security provisions Access authorisation, control, read/write, ... Pseudonymisation Deletionless but facilities for correcting mistakes. Registration of assertion ASAP after IUI assignment (virtual, e.g. LSID) central management with adequate search facilities.

PtoP statements - particular to particular
ordered sextuples of the form Ri = <IUIa, ta, r, o, P, tr> IUIa is the IUI of the author of the statement, ta a reference to the time when the statement is made, r a reference to a relationship (available in o) obtaining between the particulars referred to in P, o a reference to the ontology from which r is taken, P an ordered list of IUIs referring to the particulars between which r obtains, and, tr a reference to the time at which the relationship obtains. P contains as much IUIs as required by the arity of r. In most cases, P will be an ordered pair such that r obtains between the particular represented by the first IUI and the one referred to by the second IUI. As with A statements, these statements must also be accompanied by a meta-statement capturing when the sextuple became available to the referent tracking system.

PtoU statements – particular to universal
Ui = <IUIa, ta, inst, o, IUIp, u, tr> IUIa is the IUI of the author of the statement, ta a reference to the time when the statement is made, inst a reference to an instance relationship available in o obtaining between p and cl, o a reference to the ontology from which inst and u are taken, IUIp the IUI referring to the particular whose inst relationship with u is asserted, u the universal in o to which p enjoys the inst relationship, and, tr a reference to the time at which the relationship obtains.

Ni=< IUIa, ta, ntj, ni, IUIp, tr, IUIc>
PtoN-statements Ni=< IUIa, ta, ntj, ni, IUIp, tr, IUIc> The person referred to by IUIa asserts at time ta that ni is the name of the nametype ntj that designates in the context IUIC in the real world the particular referred to by IUIp at tr. This template will further be referred to as PtoN template. Would assert that “Werner” is my first name, and “Ceusters” is my last name.

U--tuples: “negative findings”
Relation Type of Negative F inding Examples % type C1 <p, u> * A particular is not related in a he denies abdominal pain; no alcohol abuse; 85.4 specific way to any instance of a no hepatosplenomegaly; he has no children, universal at some given time without any cyanosis C2 <p, u> A par ticular is not the instance of which ruled out primary hyperaldosteronism, 12.4 a given class at some given time nontender, in no apparent distress, Romberg sign was absent , no palpable lymph nodes C3 <p, p> A particular is not related to this record is not available to me; it is not 2.2 another partic ular in a specific the intense edema she had before; he has not way at some given time identified any association with meals. * ‘p’ ranges over particulars, ‘u’ over universals Ui = <IUIa, ta, r, o, IUIp, u, tr> The particular referred to by IUIa asserts at time ta that the relation r of ontology o does not obtain at time tr between the particular referred to by IUIp and any of the instances of the universal u at time tr

PtoCO statements: particular to concept code
Coi = <IUIa, ta, cbs, IUIp, co, tr> IUIa is the IUI of the author of the statement, ta a reference to the time when the statement is made, cbs a reference to the concept-based system from which co is taken, IUIp the IUI referring to the particular which the author associates with co, co the concept-code in cbs which the author associates with p, and, tr a reference to the time at which the author considers the association appropriate,

Interpretation of PtoCO statements
must be interpreted as simple indexes to terms in a dictionary. All that such a statement tells us, is that within the linguistic and scientific community in which cbs is used, the terms associated with co may - i.e. are acceptable to - be used to denote p in their determinative version.

A SNOMED-CT example <IUI-0945, 18/04/2005, SNOMED-CT v0301, IUI-1921, , forever> #IUI-0945: author of the statement #IUI-1921: the left testicle of patient #IUI-78127 : 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

Referent Tracking System Environment

Networks of Referent Tracking systems

Data store

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

MedtuityEMR Patient’s Encounter Document
< PtSession > PtsInfo m_ PtL astName ="John" m_PtDOB ="01/01/1985 /> PtVisitInfo m_PtTimeIn ="02/27/ :44 PM"> … Level1 m_TemplateName ="Fracture - femur" m_TemplateGUID="{ C66D 4B47 A055 CEA1A0A53C87} Item m_Text=”Examinatio n”> …. Level4 m _TemplateName =” ” m_Text=" strength of left foot plantar flexion is 3/5; strength of left foot dorsi flexion is 2/5 ; " m_GUID="{65B26952 81A1 4291 B2 6F 344EBFD2B56B}" / </ …… PtSessi on The information unit in EHR statement in MedtuityEMR application is a phrase that is generated as a result of the selection of a choice in the input screen.

Decomposing EHR Statements into Particulars
Information units in EHR statements are phrases. For each phrase, e.g. strength of left foot plantar flexion is 3/5, a list of templates containing references to defined classes and universals are stored in a database called Terms Configuration Database, describing the correct decomposition The decomposition of a phrase is based on our work described elsewhere*. U1: The universal Person DC1: MMT scale data value 3. DC2: defined class whose members are a persons’ left foot plantar muscle group DC3: defined class whose members are the disposition of persons’ right foot plantar muscle groups to attain a certain performance on the heel-rise test DC4: defined class of persons who perform members of DC5 DC5: defined class whose members are acts of assessing the performance of heel-rise tests. DC6: defined class whose members are acts of left foot heel test carried out by a person. U2: clinical encounter We have used the phrases as a building block of our application design. For the each phrase, e.g strength of left foot plantar flexion is 3/5, A list of templates containing references to defined classes and universals are stored in a database called Terms Configuration Database for each phrase, e.g. strength of left foot plantar flexion is 3/5, describing the correct decomposition The database is served as ontology of the MedtuityEMR application. The Terms Configuration Database is built by human expert who has done the decomposition. Our work related to the decomposition of a phrase into particulars is described in our other paper. *Rudnicki R., Ceusters W., Manzoor S and Smith B. What Particulars are Referred to in EHR Data? A Case Study in Integrating Referent Tracking into an Electronic Health Record Application. Accepted for American Medical Informatics Association 2007 Annual Symposium (AMIA 2007) Proceedings, Chicago IL, November 2007.

Decomposing EHR Statements into Particulars
Middleware component iterates through the XML document to retrieve the phrases. For each phrase, e.g. strength of left foot plantar flexion is 3/5, middleware contacts with Terms Configuration Database to retreive the list of templates containing references to defined classes and universals . U1: The universal Person DC1: MMT scale data value 3. DC2: defined class whose members are a persons’ left foot plantar muscle group DC3: defined class whose members are the disposition of persons’ right foot plantar muscle groups to attain a certain performance on the heel-rise test DC4: defined class of persons who perform members of DC5 DC5: defined class whose members are acts of assessing the performance of heel-rise tests. DC6: defined class whose members are acts of left foot heel test carried out by a person. U2: clinical encounter Middleware components iterates through the XML document to retrieve the phrases. For each phrase, e.g. strength of left foot plantar flexion is 3/5, middleware contacts with Terms Configuration Database to retreive the list of templates containing references to defined classes and universals .

RTS example graph

Part 4: Applications & Projects

eyeGENE (June …)

Ontology for Risks Against Patient Safety

Representing particular adverse event cases
Is the generic representation of the portion of reality adequate enough for the description of particular cases? Example: a patient born at time t0 undergoing anti-inflammatory treatment and physiotherapy since t2 for an arthrosis present since t1 develops a stomach ulcer at t3. 133 133

Anti-inflammatory treatment with ulcer development
IUI Description of particular Properties #1 the patient who is treated #1 member_of C1 since t2 #2 #1’s treatment #2 instance_of C #2 has_participant #1 since t2 #2 has_agent #3 since t2 #3 the physician responsible for #2 #3 member_of C4 since t2 #4 #1’s arthrosis #4 member_of C5 since t1 #5 #1’s anti-inflammatory treatment #5 part_of # #5 member_of C2 since t3 #6 #1’s physiotherapy #6 part_of #2 #7 #1’s stomach #7 member_of C6 since t2 #8 #7’s structure integrity #8 instance_of C8 since t #8 inheres_in #7 since t0 #9 #1’s stomach ulcer #9 part_of #7 since t3 #10 coming into existence of #9 #10 has_participant #9 at t3 #11 change brought about by #9 #11 has_agent #9 since t #11 has_participant #8 since t3 #11 instance_of C10 (harm) at t3 #12 noticing the presence of #9 #12 has_participant #9 at t3+x #12 has_agent #3 at t3+x #13 cognitive representation in #3 about #9 #13 is_about #9 since t3+x 134 134

Time line and dependencies (1)
the patient (#1) who is treated #1 #1’s stomach #7 #7’s structure integrity #8 structure integrity C8 At t0, the patient is born, and since that time, his stomach is part of him and a structure integrity (C8) inheres in it: #1 instance-of person since t0 #7 part-of #1 since t0 #8 instance_of C8 since t0 #8 inheres_in #7 since t0 135 135

the patient who is treated #1 #1’s stomach #7 #7’s structure integrity #8 structure integrity C8 #1’s arthrosis #4 underlying disease C5 At t1, the patient acquires arthrosis: #4 member_of C5 since t1 #4 inheres_in #1 since t1 136 136

the patient who is treated #1 subject of care C1 #1’s stomach #7 involved structure C6 #7’s structure integrity #8 structure integrity C8 #1’s arthrosis #4 underlying disease C5 #1’s treatment #2 act of care C3 #1’s physiotherapy #6 #1’s anti-inflammatory treatment #5 At t2, the patient consults #3 who starts treatment. It is then that the patient becomes a member of the class subject of care (C1) and his stomach a member of the class involved structure (C6) the physician responsible for #2 #3 care giver C4 137 137

Time line and dependencies …
the patient who is treated #1 subject of care C1 #1’s stomach #7 involved structure C6 #7’s structure integrity #8 structure integrity C8 #1’s arthrosis #4 underlying disease C5 #1’s treatment #2 act of care C3 #1’s physiotherapy #6 #1’s anti-inflammatory treatment #5 act under scrutiny C2 #1’s stomach ulcer #9 change brought about by #9 #11 harm C10 noticing #9 #12 cognitive representation in #3 about #9 #13 the physician responsible for #2 #3 care giver C4 138 138

Domotics and RFID systems
Avoiding adverse events in a hospital because of insufficient day/night illumination: Light sensors and motion detectors in rooms and corridors and representations thereof in an Adverse Event Management System (AEMS) What are ‘sufficient’ illumination levels for specific sites is expressed in defined classes, Each change in a detector is registered in real time in the AEMS, Action-logic implemented in a rule-base system, f.i. to generate alerts.

RT-based representation (1): IUI assignment
Reality level 1 #1: that corridor #2: that lamp #3: that motion detector #4: that light detector #5: that RFID reader #6: that patient with RFID #7 #8: that RFID reader #9: this elevator #10: 2nd floor of clinic B

RT-based representation (2): relationships
(Semi-)stable relationships: #1 instance-of ReM:Corridor since t1 #2 instance-of ReM:Lamp since t2 #2 contained-in #1 since t3 #6 member-of ReM:Patient since t4 #6 adjacent-to #7 since t4 #18 instance-of ReM:Illumination since t1 #18 inheres-in #1 since t1 … Semi-stable because of: lamps may be replaced persons are not patients all the time  keeping track of these changes provides a history for each tracked entity

RT-based representation (3): rule base *
Setting illumination requirements for lamp #2: #18 member-of ReM:Insufficient illumination during ty if tx part-of ReM:Daytime #y1 instance-of ReM:Motion-detection #y1 has-agent #3 at ty ty part-of tx #y2 instance-of ReM:Illumination measurement #y2 has-agent #4 at ty #y2 has-participant # at ty #y2 has-result imrz at ty imrz less-than 30 lumen else tx part-of ReM:Night time … endif * Exact format to be discussed with ReMINE partners

RT-based representation of events
Imagine #6 (with RFID #7) walking through #1 #2345 instance-of ReM:Motion-detection #2345 has-agent #3 at t4 #2346 instance-of ReM:RFID-detection #2346 has-agent #5 at t4 #2346 has-participant #7 at t4 … Here, the happening of #2345 fires the rule explained on the previous slide. If imrz turns out to be too low, that might invoke another rule which sends an alert to the ward that lamp #2 might be broken. #2346 might trigger yet another rule, namely an alert for imminent danger for AE with respect to patient #6

Making existing EHR systems RT compatible

Tracking versions of representations

Ways representational units do or do not refer
OE: objective existence; ORV: objective relevance; BE: belief in existence; BRV: belief in relevance; Int.: intended encoding; Ref.: manner in which the expression refers; G: typology which results when the factor of external reality is ignored. E: number of errors when measured against the benchmark of reality. P/A: presence/absence of term.

Revisioning beliefs

Comparing terminologies with reality as benchmark

Comparing ontology versions
Ceusters W. Applying Evolutionary Terminology Auditing to the Gene Ontology. Journal of Biomedical Informatics 2009;42:518–529.

Quality evolution of the Gene Ontology

Quality forecasting

Referent Tracking enabled Websites

Architecture

Some central ideas Informative websites are about portions of reality. If the latter change, so should the former. Synchronization should be auditable. Enforce responsibility of information providers and consumers, yet protect their integrity. Cross-fertilization with Information Artifact Ontology.

Some key insights Static versus dynamic pages;
Web pages usually keep their name (URL), yet undergo changes; ‘page’ versus ‘file’ Server file never ‘changes’: always replaced by a new file with the same name Changes to a file do not always involve changes to the propositional content; Requests to view a page do not lead the file on the server to be transmitted, but a new copy of it in each single case;

Entities to assign IUIs to
The content file of each page The content of each content file The propositional content of each content Each browser page Each checksum Each ontology and terminology used in RT-tuples Each RT-tuple (except D-tuples) The middleware component

Use of the CEN Time Standard for HIT

Tuple generations when adding a page

Tuple generations when updating a page

Tuple insertions: generating a browser page
A-tuples n IUIp IUIa tap Key 1 #24 #2 (EVENT("#24 assignment") has-occ AT TP(time-18)) #25 3 #27 (EVENT("#27 assignment") has-occ AT TP(time-20)) #28 9 #34 (EVENT("#34 assignment") has-occ AT TP(time-26)) #35 D-tuples n IUId IUIA td E C S Key 2 #2 #25 (EVENT("#25 inserted") has-occ AT TP(time-19)) I CE #26 4 #28 (EVENT("#28 inserted") has-occ AT TP(time-21)) #29 6 #30 (EVENT("#30 inserted") has-occ AT TP(time-23)) #31 8 #32 (EVENT("#32 inserted") has-occ AT TP(time-25)) #33 10 #35 (EVENT("#35 inserted") has-occ AT TP(time-27)) #36 12 #37 (EVENT("#37 inserted") has-occ AT TP(time-29)) #38 PtoP-tuples n IUIa ta r IUIo P tr Key 5 #2 (EVENT("#30 is asserted") has-occ AT TP(time-22)) MainContentCopyOf #022 #27, #12 (EPISODE("#30 is true") has-occ SINCE TI(time-20)) #30 7 (EVENT("#32 is asserted") has-occ AT TP(time-24)) InstigatorOf #24, #27 (EVENT ("#32 is true") has-occ AT TP(time-18)) #32 11 (EVENT("#37 is asserted") has-occ AT TP(time-28)) ChecksumOf #34, #27 (EPISODE("#37 is true") has-occ SINCE TI(time-26)) #37

ICBO Tutorial Introduction to Referent Tracking July 22, 2009 112 Norton Hall, UB North Campus Werner CEUSTERS Center of Excellence in Bioinformatics.

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ICBO Tutorial Introduction to Referent Tracking July 22, 2009 112 Norton Hall, UB North Campus Werner CEUSTERS Center of Excellence in Bioinformatics.

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Presentation on theme: "ICBO Tutorial Introduction to Referent Tracking July 22, 2009 112 Norton Hall, UB North Campus Werner CEUSTERS Center of Excellence in Bioinformatics."— Presentation transcript:

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