=Paper=
{{Paper
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|title=Realist Representation of the Medical Practice: an Ontological and Epistemological Analysis
|pdfUrl=https://ceur-ws.org/Vol-776/ontobras-most2011_paper6.pdf
|volume=Vol-776
|dblpUrl=https://dblp.org/rec/conf/ontobras/AndradeA11
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==Realist Representation of the Medical Practice: an Ontological and Epistemological Analysis==
https://ceur-ws.org/Vol-776/ontobras-most2011_paper6.pdf
Realist representation of the medical practice: an
ontological and epistemological analysis
André Q. Andrade1, Maurício B. Almeida1
1
Escola de Ciência da Informação – Universidade Federal de Minas Gerais (UFMG)
Av. Antônio Carlos, 6.627 – CEP 31270-901 - Belo Horizonte - MG – Brazil
andradeaq@ufmg.br, mba@eci.ufmg.br
Abstract. Realist ontologies organize knowledge by strict adherence to
philosophical principles, ensuring robustness and coherence. According to
those principles, only entities empirically verifiable can be represented. Our
study aimed to analyze medical records to evaluate which kinds of entities
should be represented for physicians. We classified the entities and found
several entities that cannot be represented in realist ontologies. After due
analysis, results suggest that a categorization that distinguishes reality from
medical knowledge about reality and observations under both of them are
useful to describe entities present in medical records.
1. Introduction
Information structuring in electronic health records (EHR) is essential for the
development of health applications, due to its ability to exchange information between
different applications and healthcare professionals. Structured records are amenable to
use in several situations, such as: a) scientific discoveries; b) use of recorded data by
other professionals; c) healthcare facility management and quality control; d) prevention
of epidemics and health policy development.
System interoperability (the capacity of communication between systems without
human intervention) requires shared semantics of terms used in both systems. Recently,
the use of ontologies for semantic representation is being studied in several domains,
like the biomedicine [Rubin et al. 2008]. Particularly, the development and wide
adoption of the realist stance for ontology creation allows for an explicit, stable and
language independent vocabulary definition, which promotes communication without
ambiguities [Smith and Ceusters 2010].
Even though such methodology aims to describe scientific knowledge, such as gene and
protein biological functions [Hill et al. 2008], it actually limits the representation of
natural language terms that have no direct referent in the world. This “non-ontological”
terminology is important to clinical records [Stenzhorn et al. 2008]. For instance, a
clinician may use the term “hepatitis” to refer to a real hepatitis, but can also refer to a
clinical suspicion (that the patient may have the disease), or to a preventive action (like
vaccine prescription). In this paper we generically refer to those terms as
epistemological [Bodenreider et al. 2004].
Besides, Schulz and colleagues [2009] argue that the attempt to code probabilistic and
default knowledge using ontologies is likely to create incorrect models. In fact, the
realist approach seems incapable to represent statements that are not universally true,
such as “suspected fever”, “past history of fever” and “no fever”.
73
This research aims to evaluate real medical records, to analyze which entities are
amenable to representation by the so-called realist ontologies, as defined by [Smith
2006]. The description of medical record entities by ontological and epistemological
principles, part of an ongoing research project, is being used to create a set of
procedures that will guide the analysis and create a generic framework that will improve
understanding of medical systems specificities.
This paper is structured as follows. In section 2 we describe the advantages and
limitations of realist ontologies for medical knowledge representation. In section 3, we
present a critical evaluation regarding the relationship of formal ontologies and clinical
reasoning. In section 4 we present the methodology used, aimed at identifying
information contained in real medical records. In section 5 we present results, in section
6 we discuss the results and in section 7 we present our final remarks.
2. Ontologies
Ontologies are being used in large scale in varied domains like architecture, geography,
[Bittner 2010], medicine and biology [Bittner and Donnelly 2007], whether as support
for legacy classification systems, or as way of adequately representing a domain. In the
following sections we describe applications in biomedical ontologies (section 2.1), as
well foundational principles of realist ontologies, widely used in biomedicine (section
2.2).
2.1. Biomedical ontologies
Ontologies have been successfully used in the biology and medical domain around the
world. Several initiatives were gathered in the Open Biomedical Ontologies Foundry
(OBO), a repository of accessible, interoperable ontologies, described in uniform syntax
and unequivocal identification [Smith et al. 2007]. Considering the OBO group, some
ontologies are worth mentioning, due to innovation and intense use in scientific
research. Among them, the Gene Ontology, an ontology that describes basic
characteristics of genes; the Foundational Model of Anatomy1, which describes the
prototypical human anatomy; the Cell-Type Ontology, which describes cell types from
some living; the Protein Ontology2, which describes the relationship between proteins
and classes that represent protein evolution; and the Chemical Entities of Biological
Interest3.
Besides these big foundational ontologies, several others are still under evaluation and
available at OBO, such as the Disease Ontology4 [Cowell and Smith 2010], the
Ontology for Biological Investigations [Brinkman et al. 2010], the Ontology for General
Medical Science5 [Scheuermann et al. 2009].
1
http://fma.biostr.washington.edu/
2
http://pir.georgetown.edu/pro/
3
http://www.ebi.ac.uk/chebi
4
http://do-wiki.nubic.northwestern.edu/index.php/Main_Page
5
http://code.google.com/p/ogms/
74
2.2. Realistic ontologies
The term “realism” in Philosophy is widely used and controversial [Miller 2010]. We
have to emphasize that realism, while philosophical discipline, can disclose different
flavours. Indeed, there are issues under unending debate among people which declare
themselves as being realists. Defining universals, a main tenet of realism, is an example
of issue on which there is no agreement [MacLeod 2005]. In this paper, we take the
“ontological realism” as a methodology for ontology development – said “realist
ontologies” – based on principles of the philosophical realism. It is a methodology
widely used in biomedicine [Baker et al. 1999][Grenon et al. 2004] grounded at the
following generic tenets: [Munn and Smith 2008]: i) there is a real world; ii) the reality
in which we live in is part of this world; iii) we are capable of knowing the world and
reality, even if just in a approximate way.
One of the assumptions of the ontological realism is the theory of universals, which
states that in reality there are particular and universal entities. Particulars are entities
described by the observation of the real world, e.g. a clinic or a laboratory. Universals
represent that which is common to every correspondent particular - e.g. the
characteristic of having a head that is common to every human being – which is
invariant in reality [Smith 2004][Smith 2006]. Since ontological realism is based on
reality and proposes that the best way to describe it is through science, universals are
those entities chosen to be used in the formulation of scientific theories.
According to the ontological realism, the unrestricted creation of classes to represent
every possible entity leads to inconsistencies. Classes are human creations – e.g. every
human being that is a man and likes swimming – and may be interpreted in different
ways [Munn and Smith 2008]. To avoid that, the realist methodology restricts the
possible classes to those defined by the scientific community. However, the precise
distinction between universals and classes is not always trivial. While universals are
grouped by what they are, classes are grouped by how they are [Smith e Ceusters,
2010].
The realist methodology uses a upper-level ontology to organize universals with a top-
down approach. Examples of upper-level ontologies are the BFO [Grenon et al. 2004],
DOLCE, the SUMO, among others. In the BFO, adopted in the ontological realism
stance, we can find structuring divisions made by generic universals called continuants
and occurrents. This division is based on the notion of SNAP and SPAN [Grenon et al.
2004]. SPAN entities, called occurrent or perdurants, are universals that posses a
determined beginning and end, and encompass process (e.g. “the life of an organism”)
and spatiotemporal regions (e.g. “the eighties”). SNAP entities, also called continuants
or endurants, are universals for particular that maintain their identities through time (e.g.
a “human being”). Continuants may be dependent (e.g. “the color of an object”),
independent (e.g. “a table”) or spatial regions (e.g. a “point”). To explain the different
treatments for high-level entities in other ontologies abovementioned is beyond the
goals of the present paper.
The use of the same upper-level ontology as starting point to create domain ontologies
increases the chance that its universals are compatible and, therefore, the chance that
they are amenable to integration.
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3. The limitations of realist ontologies for representing medical practice
The extension of realist biomedical ontologies to the medical practice is an alternative
for medical information organization. However, considering institutions of most
countries, medical documentation is usually made of barely structured documents,
sometimes even handwritten, containing heterogeneous information. Even so, the
medical record is an essential work tool for the clinician. The record is used for medico-
legal reasons, as a tool to support care plan creation and as a support to find information
required for clinical decision-making.
The realist ontology approach has been the target of many criticisms, which usually
argue against the proposal of universals as a sine qua non condition to the creation of
good ontologies [Merrill 2010a][Merrill 2010b] [Rector 2010] [Cimino 1998][Cimino
2006] [Dumontier and Hoehndorf 2010]. Such approaches for biomedical ontologies
emphasizes the importance of language, communication and medical reasoning and puts
under suspicion the obligation in considering [Merrill 2010a]. In many cases, such
approaches have been labeled as “epistemological”.
Conceptual approaches, a variant of idealism, are closer to medical everyday language,
since they use terms not referenced in reality which are commonly present in new and
yet not fully comprehended clinical situations. In the medical practice, diagnoses are
usually presumptive and based on incomplete data, making it difficult to identify a
particular and the corresponding universal. In fact, statements in such context are
constantly revised and do represent truths, but the physicians grounded opinion.
Realist-oriented researchers argue that the creation of ontologies around concepts is
based on language and, therefore, is subject to ambiguities and differences of
understanding and interpretation by different individuals [Smith 2006]. These
researchers consider that ontologies are artifacts made for use by computers and that
any natural language-derived ambiguity harms interoperability efforts. This is
particularly important in natural sciences representation such as biology in which,
despite the enormous volume of data, there is consistency in observation by different
institutions. Also in medicine, anatomical and physiological statements are consensual
when attributed to universals.
This is made clear by comparing the statements “AIDS is spreading quickly through
Asia” and “AIDS is caused by the HIV”. The term AIDS in the former is a class, while
it correspond to a universal in the latter. Classes are arbitrary sets and can result in
representation that cannot be understood and interpreted. By restricting the ontological
commitment to reality as described by science, the ontological realism promotes
consensus.
Another relevant aspect to be considered is the distinction between ontology and
epistemology. Epistemology is the study of how cognoscent beings come to the truth
about some event in reality. The difference between the terms can be shown by
evaluating how entities are defined in ontology and in epistemology. Ontology is about
an object, process, event, whole, part, determination, dependence, composition, etc.
Epistemological statements are about the way we know things and is about belief, truth,
probability, confirmation, knowledge and its variations [Poli 2010]. While ontology is a
theory of things, epistemology is a theory of knowledge.
76
The interdependence between the existence of an entity and the knowing about it
frequently blurs the distinction between ontology and epistemology. Bodenreider and
colleagues classify epistemological terms usually identified in biomedical terminologies
in four categories [Bodenreider et al. 2004]:
Terms containing classification criteria: terms that do not represent universals, but
that intend to convey information. For example, the distinction between “febrile
seizure” and “afebrile seizure” is not a distinction between characteristics of the
seizure itself, but conveys information about probable cause and prognosis.
Terms reflecting detectability, modality, uncertainty, and vagueness: since complete
understanding of a clinical situation is very difficult, physician usually express this
incomplete knowledge of the patient condition by modal and approximate
statements. E.g. “possible cancer”, “probable cancer”, “unspecified chest pain”.
Terms created in order to obtain a complete partition of the domain: contain terms
that intend to encompass entities not described by other classes. E.g. “Other” and
“Pneumonia not otherwise specified”.
Issues related to normality and to fiat boundaries: terms that intend to convey
instructions about how the information should be interpreted, not about the entity
itself. E.g. “normal height”, “enlarged liver”. It is important to point out that part of
the medical knowledge is based on historical events which had an almost arbitrary
definition of normality [Vickers et al. 2008].
The fact that clinical observations are necessary is not opposed to the realist
methodology: information about opinions are fundamentally different from information
about objects [Munn and Smith 2008] and both have a place in an descriptive ontology6.
However, the medical practice requires the recording of information of both natures,
named here ontological and epistemological, including impressions, plans, suggestions,
etc. We intend to pursue this issue while searching for a complementary approach that
helps in understanding the medical reality.
4. Methodology
This ongoing research objective is to evaluate the representation of health
information in real medical records, through the use of realist ontologies. We intend to
determine its limitations and propose new ways of representing non-ontological
information. For example, administrative data, which at first had no counterpart in
realist reference ontologies, has to be represented through the creation of other
ontologies for dealing with such entities, like the Information Artifact Ontology [IAO
2011]. The methodology is composed by the following steps:
1. Record creation based on real clinical cases: The analysis must consider the way
health professionals record medical events. We studied two complete records,
created by two Internal Medicine specialists, based on common presentations of
real patients. No identification data was recorded, such as name, age,
6
“concerns the collection of such prima facie information on types of items either in some specific
domain of analysis or in general”[Poli 2010, pg.2]
77
geographical location, health facility, dates and identification and contact
numbers, according to recommendations by [Meystre et al. 2010].
2. Transcription of records for information identification: In order to identify
information unities, a domain expert transcribed the records in sentential
fragments. The domain expert was asked to identify the reason for recording
those entities and the information that is being conveyed by the representation.
The transcription used the principles of logic and controlled languages described
[Fuchs et al. 2005][Fuchs et al. 1999] , which allowed clear identification of
entities recorded in natural language, outside the particular context in which the
event took place [Vickers et al. 2008]. Since the objective of this paper is to
analyze the content of the text, syntactical and markup aspects pertinent to
automatic processing are omitted. We hereafter call those information unities as
entities, despite their physical existence.
3. Analysis and classification of the record’s information items, according to
ontological realism guidelines: The information entities were analyzed according
to the tenets of the ontological realism [Grenon et al. 2004] , to verify if they
were suitable to ontological representation. This analysis was guided by pre-
established criteria aimed to classifying the entity in some upper BFO class.
Some examples can be found in table 1. Each entity was tested against the set
criteria, respecting the BFO class hierarchy. E.g. the first test separates entities
in continuants and occurrent; after this distinction, specific criteria are used for
each class. The entities that don’t belong to any BFO class are analyzed
according to realist principles and their use in everyday medical practice. We
selected some cases for further discussion, presented in section 6.
Table 1. Distinction between continuants (EMT) and occurrents (ECT)
Distinction Entities that maintain their identity Entities that change through time
(I) through time (EMT) (ECT)
a) The entity exists completely in any a) The entity unfolds through a period
Characteristics given period of time in which it is of time.
present
b) The entity has no temporal parts.
5. Results
The records analyzed represent outpatient visits. The first one describes the consultation
of a patient with an unexplained chest pain and the second a post-discharge consultation
due to dyspnea. The records make use of routine record organization, such as
“Complaint”, “History”, “Physical examination”, etc. In table 2 we present a small
extract of one of the documents. Partial results can be seen below in Table 3:
Table 2. Extract of an outpatient record of a fictitious patient
QP: Chest pain and abdominal pain.
HMA: Six months ago, the patient felt severe precordial pain in addition to nausea and dyspnea. She
attempted medical care in the Hospital X, where received isordil + AAS 300mg. Enzimes: CKT 262
CKMB 30. She was not aware of previous pathologies. It was prescribed: Captopril, HCTZ e AAS.
Last month, the patient felt severe pain again and sought for medical care in a different place. Then, it
78
was prescribed: Losartan, AAS, Sinvastatina e Nebilet.
She sought for medical care in other occasions because of the precordial pain. In addition to the
medicine mentioned, she uses Metoprolol - 100 mg 12/12 h.
She reports diffuse and intermittent abdominal pain, which becomes worse in case of stress. It is not
related with bowel movement alterations. She also reports rare burning epigastric pain that improves
with water drinking.
Table 3. Example of mapped and non-mapped entities to realist ontologies
1- Aspects that represent entities IN REALITY (some examples)
Continuant Occurrent
-Chest pain -Were prescribed
-Abdominal pain, Precordial pain, Epigastric pain -Makes use
-Nausea, Dyspnea -Bowel movements
-Enzyme -Moment of first occurrence of pain (six months)
-Captopril, Losartan -Moment of re-incidence of pain (one month ago)
2- Aspects that represent useful constructs for medical practice NOT empirically verifiable
-Severe (precordial) heavy pressure (pain) -Diffuse and intermittent (abdominal pain)
-Rare burning (epigastric pain)
3- Aspects that represent observations ABOUT reality (not reality itself)
-CKT 262
-CKMB 30
-Left ventricle ejection fraction: 68%
4- Aspects that represent observations ABOUT the physician understanding of the clinical situation
(not about reality)
-Previous consultations and prescriptions -Previous diseases
-Not related to bowel movement alterations - (Diffuse and intermittent abdominal pain) that
worsens with stress
- (Rare burning epigastric pain) that improves
with water drinking
6. Discussion
The medical record is a complex document used for several purposes in healthcare
processes. According to the Brazilian Medical Council, it is “a single document made of
a set of recorded information, signs and images, created after (events) about the patient
health and care provided, of a legal, private and scientific character, that allows
communication between the multi-professional team and continuity of care provided to
the individual” [Conselho Federal de Medicina 2002, art 1º]. To live up to those
expectations, the professional uses the flexibility of natural language expressions to
represent the clinical situation, his clinical reasoning process and the relevant context of
the health event.
In our research, we drew terms from records trying to fit them to constraints imposed by
realist ontologies. Then, we created two main sets: in the first one, we included the
entities that could be represented in realist ontologies; the second one gathers entities
that can not be represented in realist ontologies as we defined them in the context of this
paper (vide section 1) Terms that can be used in realist ontologies are presented as the
first group of table 3. Arguably, realism has been shown capable of representing
diseases, disorders [Scheuermann et al. 2009] and symptoms [Smith et al. 2009], as
evidenced by the Ontology for General Medical Science (OGMS). The existence of
diseases – defined as a “disposition (i) to undergo pathological processes that (ii) exists
in an organism because of one or more disorders in that organism” [Scheuermann et al.
79
2009, pg.3] is well known by medical science, and its representation is robust and
homogenous. Likewise, symptoms can be seen as body characteristics that a patient
experiences. In this case, we represent the body alteration considering its scientific
description. On the other hand, the diagnosis itself is not a patient attribute, but rather “a
conclusion of an interpretive process that has as input a clinical picture of a given
patient and as output an assertion (diagnostic statement) to the effect that the patient has
a disease of such and such a type.” [Scheuermann et al. 2009, pg.5])
We observed that the realist methodology in incapable of defining symptoms qualities.
In order to evaluate a patient, each symptom must be described according to its seven
characteristics [Bickley and Szilagyi 2009]: Location; Quality; Quantity or severity;
Timing; Setting in which it occurs; Remitting or exacerbating factors; Associated
manifestations. These characteristics can be classified in three groups, according to their
relation with realist ontologies.
Formal ontologies are capable of precise representation of symptom location and
temporality, through the description of body structures – organs and systems – or
spatiotemporal regions. This first group can be described by upper level classes of the
BFO, as continuants – independent continuants and spatiotemporal regions – and
occurrents – the temporal region occupied by the symptom and, eventually, the
symptom itself. For example, “chest pain” and nausea”. As stated in the Methodology
section, this analysis was based on the BFO, but different upper ontologies may suggest
different approaches. This is markedly true in the case of qualities [Masolo and Borgo
2005], defined by the BFO as “a specifically dependent continuant that is exhibited if it
inheres in an entity or entities at all (a categorical property)” [Basic Formal Ontology].
The second group of table 3, containing the characteristics of quality and
quantity/severity, describes attributes of the symptoms, its temporal evolution, qualities,
dispositions, functions and roles. The qualities refer to symptom types as described by
scientific knowledge of common clinical presentation of diseases. There are regional
and national variations of such typology, but classical symptom description is fairly
constant. For instance, the term “crushing pain” is commonly interpreted as a cardiac
originated pain. The quality “crushing” of the precordial pain has no direct and
unequivocal relation with the subjacent disorder, but the history of pain is similar in
patients with the same kind of disorder. In this case, we argue that the term is not a
realist universal, but can be described by concepts in a coherent fashion. The same
criteria applies to severity (“Severe pain”), which shows the same linguistic ambiguity
(how much is severe?). These terms must be described by non-ontological artifacts to
avoid reasoning and classification errors, since the distinction between types of pain
cannot be empirically verifiable – e.g. distinguishing a crushing from a heavy pressure
pain. Another example would be “diffuse abdominal pain”, which should not be treated
as a single ontological entity.
In the fourth group of table 3, we find the aspects referring to the situation in which the
symptom was experienced by the patient, according to the medical record description.
The setting of occurrence describes the state of affairs at the moment when the symptom
was perceived, what the patient was doing, climate and environment conditions, events
that preceded the symptom, etc. Remitting or exacerbating factors describe entities that,
according to the patient’s or physician’s interpretation, changed the natural course of the
symptom. This interpretation may be motivated by previous knowledge (e.g. causes of
80
chest pain may be distinguished by their relation with physical exercise), temporal
coincidence or unjustified beliefs. Finally, associated manifestations may be represented
by any other symptom, or the absence of symptoms, as long as they aid medical
reasoning and the definition of a diagnosis. The representation of this group through
realist ontologies is mostly ambiguous. In the cited examples, the occurrent “drinking
water” and the “epigastric pain” intensity decrease are temporally related, but the
causality cannot be empirically determined. Rather, they reflect the understanding of the
situation, so that there is a belief that both entities are causally related.
Besides symptoms, several other entities were found, such as medications, laboratory
test results, physical examination findings, among others. Entities like life signs
measurements and lab test results do not directly refer to patient qualities, but to
observations about those qualities. For example, the CKMB (creatine phosphokinase
MB) refers to the enzyme blood concentration at the exact moment of blood sample
collection. It is, therefore, empirically verifiable. However, the value of the
measurement is arbitrarily determined (in this case, unity per liter) and does not refer to
the existence of the enzyme in the real world. Besides, this information is not analyzed
using logic operations, but used in a sequence of pre-established thinking rules,
according to clinical training. In this clinical case, the value 30 U/L is just above the
normal value (26 U/L) and, therefore, leads the physician to question the hypotheses of
myocardial infarction, suggested by the initial presentation of chest pain. The presence
of continua in the real world requires fiat delimitations, which are justified by pragmatic
reasons. [Schulz and Johansson 2007]. We argue that this information should be
distinguished from direct referents, since it refers to a representation of an observation
about reality. Moreover, it will be interpreted according to reasoning structure, not
according to the structure of reality itself.
Several solutions to this problem can be found. Despite conceptualism shortcomings,
the restrict use of concepts to represent epistemological information expands the scope
of those representations. To improve the meaning of concept and avoid misuse of the
term, we will use the definition put forward by [Klein and Smith 2010, pg.722]:
“concept should be used exclusively to refer (1) to the meaning of a corresponding
general term, this meaning being (2) unique and (3) agreed upon by responsible persons
in the given disciplinary field.”
The use of concepts to represent clinical information, though subject to inconsistencies,
is closer to language, since it represents term meaning and does not denote an entity
(universal or particular). The definition of each term can be made through formal
languages or natural language description, depending on the heterogeneity of
interpretations given to a term: e.g. concepts such as “up” and “down” are intuitive and
interpreted in a constant way, while the term “AIDS” requires precise explanation. The
relation between terms should be done through semantic relations “broader_than” and
“narrower_than”, considering the term meaning. Additionally, we can consider the
relation “related_to”, as proposed in the W3C Simple Knowledge Organization System
(SKOS) standard.
While the proposed typology encompasses real and epistemological entities, it still
needs improvement. The description of knowledge, thought as an attribute of the
cognoscent being, does not describe a real entity, but says something about it. For
instance, it is false to represent a “canceled surgery” as a “surgery”, for it never
81
happened. A solution is to represent the “canceled surgery” as an information artifact, a
plan that is about the “surgery”. In this case, the surgery will never come to be, but the
plan existed through a defined and verified temporal region [Schulz et al. 2010]. These
and other hypothetical entities, like instructions (“in case of recurrence, do X”) and
goals (“the patient should try to lose at least 2 kg with this diet within 2 months”) can be
placed in more than one category – it may be seen as a real information entity ABOUT
another not yet instantiated real entity, or as a mental model simulation on the part of
the physician, stating an algorithmic behavior in the form IF X THEN Y. These cases
make evident that further effort in refining the model must be done.
7. Final remarks
The proposed categorization suggest that understanding reality representation in four
levels – reality itself, the perception of reality by the being, and the recording of reality
[Smith et al. 2006] – makes clear the connection between representation methodologies.
This connection will be important for proper computerizing of medical records.
During this research project, we intend to expand this categorization in a framework
connecting ontological and non-ontological entities that promotes representation of
entities required in medical practice without compromising interoperability and
automatic inferences. We intend to explore information models such as the HL7 v3 and
the OpenEHR, since they offer a great opportunity to understand the relation between
concepts and ontological entities.
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