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Cognitive Systems Research 11 (2010) 42–52

Towards an ontology for psychosis

Action editor: Ning Zhong

Jay (Subbarao) Kola a,*, Jonathan Harris b, Stephen Lawrie b, Alan Rector a,Carol Goble a, Maryann Martone c

a University of Manchester, Department of Computer Science, BioHealth Informatics Group, Oxford Road, Manchester M13 9PL, UKb University of Edinburgh, Division of Psychiatry, Kennedy Tower, Morningside Park, Edinburgh EH10 5HF, UK

c Department of Neurosciences, University of California, San Diego, CA 92093-0446, United States

Received 25 February 2008; accepted 4 August 2008Available online 2 September 2008

Abstract

There is a pressing need for data interoperability in neuroscience especially in mental health and psychiatric research. Heterogeneity ofdata in the domain is a combination of a plethora of assessment methods and two clinical classification systems with no formal method ofinterconversion. Ontologies with their formal logical basis have been successfully used to achieve interoperability in other fields of biol-ogy. We discuss the need for an ontology in the domain of psychosis and propose a methodology for building such an ontology. Weoutline the various factors that are important for building a unifying ontology and how this might serve as a good start for buildingbetter classification systems in psychiatry.� 2008 Elsevier B.V. All rights reserved.

Keywords: Psychosis ontology; Data integration; Interoperability; Classification systems; ICD-10; DSM-IV; Knowledge representation in psychiatry

1. Introduction

Integration of neuroscience research with clinical psy-chiatry is essential to obtain a holistic understanding ofpsychiatric disorders. The success of contemporaryresearch studies often depends on the ability to merge dataof different nature (e.g.: merging phenotype data (imagingand symptoms) with genotype data) and multi-centre stud-ies critically depend on the ability to successfully align databetween the various centres. Recently, ontologies havegained popularity as the means of achieving syntactic andsemantic interoperability of data (Amann, Beeri, Fund-ulaki, & Scholl, 2002; Kohler, Philippi, & Lange, 2003).Ontologies are the knowledge models that provide a con-

1389-0417/$ - see front matter � 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.cogsys.2008.08.005

* Corresponding author.E-mail addresses: jay.kola@manchester.ac.uk (Jay (Subbarao) Kola),

Jonathan.Harris@ed.ac.uk (J. Harris), S.Lawrie@ed.ac.uk (S. Lawrie),rector@cs.man.ac.uk (A. Rector), carole.goble@manchester.ac.uk (C.Goble), maryann@ncmir.ucsd.edu (M. Martone).

trolled vocabulary and semantics for describing and shar-ing data in a domain (data models). Ontologies havealready been successfully used by the bioinformatics com-munity for standardising reporting and comparison ofgenomic data (microarray data) by using the Gene Ontol-ogy (Ashburner et al., 2000) and the MGED Ontology(Stoeckert & Parkinson, 2003). The Biomedical InformaticsResearch Network (BIRN) project already provides anontology (BIRNLex) for integration of experimental datafrom studies, but it does not provide adequate coverageof the clinical domain of mental health1. As mental healthdisorders and psychotic illnesses represent a considerabledemand on health resources, maximizing the value of men-tal health research data by developing a domain-specificontology is potentially of great benefit. As ontologies com-prise machine-readable representations of the relationshipsbetween elements within a domain of knowledge, they mustcontain explicit assumptions regarding the meaning and

1 http://www.nbirn.net/tools/browse_tools_alphabetical.shtm.

Jay (Subbarao) Kola et al. / Cognitive Systems Research 11 (2010) 42–52 43

function of their vocabularies. The subjective nature ofclinical diagnoses and symptom identification is problem-atic for the development of an ontology, as is the fact thatthe same symptoms can be associated with different formaldiagnoses and treatment recommendations. Anotherimportant obstacle to data interoperability in mentalhealth research is the use of different assessments, scalesand structured interviews for measuring and recordingsymptoms and assisting in diagnosis. (e.g.: PANSS2

PSE,3 SCID,4 OPCRIT5). These are useful for gaining acomposite picture of an individual but contain diverseterms that often overlap semantically and have varyingdegrees of relevance and use in research terms. A furtherobstacle is that whilst psychiatric diagnoses are oftenrecorded as DSM IV6) and ICD 107 concepts, there is noproven means of inter conversion from one system toanother. Though the DSM-IV and ICD-10 are betteraligned now than ever before, there are differences in theirstructures and in some cases the criteria as discussed lateron in the paper. Nevertheless, the existence of appropriatereference terminologies and exchange standards providesreliable ways of defining disorders and communicatingabout them in mental health studies and clinical practice.

In this paper we describe the use cases and methodologyfor developing an ontology for mental health by using psy-chosis as an exemplar. Section 2 describes the need for anontology in psychosis and the role of such an ontology inclinical practice and research. We provide a general intro-duction to ontologies in Section 3. Issues in capturingknowledge in psychiatry and implications this has on devel-oping a formal ontology are discussed in Section 4. Wethen propose a methodology for developing an ontologyin psychosis in Section 5 and discuss known problems withthe domain and existing systems (Section 6). This article isbased on past work (Ure et al., 2007) and preliminary workdone under the PsyGrid (Ainsworth, Harper, Juma, &Buchan, 2006) and the NeuroGrid (Geddes, Lloyd, Simp-son, Rossor, & Fox, 2005) projects.

2. The need for an ontology

In psychiatric and clinical research, there is continuallyemerging evidence linking clinical features of psychosisand psychotic disorders with neuroanatomical and cogni-tive abnormalities (Lawrie & Abukmeil, 1998; Antonovaet al., 2005). Also, with the increasing use of genetic datain research, it is likely that future studies will want to aligngenotype data from one study with phenotype data (symp-

2 Positive and Negative Syndrome Scale, (Kay, Fiszbein, & Opler, 1987).3 Present State Examination (Wing, Cooper, & Sartorius, 1974).4 Structured Clinical Interview for DSM Disorders (First, Spitzer,

Gibbon, & Williams, 1995).5 Operational Criteria Checklist for Psychotic and Affective Illness

(Mcguffin, Farmer, & Harvey, 1991).6 Diagnostic and Statistical Manual of Mental Disorders (DSM, 19947 International Classification of Diseases, 10th Revision [http://

www.who.int/classifications/icd/en/index.html].

toms and imaging) from another different study. An ontol-ogy that would facilitate data sharing would increase thestatistical power and validity of findings thereby enhancingour understanding of psychosis and psychotic disorders. Ifthis were achieved, knowledge of prediction, prognosis andrecovery in mental illness should be greatly enhanced. Datainteroperability is key to unifying findings from varioussub-specialities of neuroscience and achieving a holisticunderstanding of disease processes and also in optimisingpatient care. However there remain many issues that needto be addressed before data interoperability can beachieved in mental health. Before we discuss the actualissues that affect interoperability in mental health, it willbe useful to discuss the high level problems in achievinginteroperability. Wache et al. (2001) offer a good overviewof the interoperability problem.

Data interoperability:

(i) Technical level,(ii) Information level.

(a) Structural heterogeneity of data.(b) Semantic heterogeneity of data.

They state that in order to achieve data interoperability,we need to ensure that it happens at the technical level (soall the necessary databases and component systems caninteract at a technical level) and also at the informationlevel. Information interoperability is achieved when infor-mation between systems can be moved around without los-ing the context and meaning of the data. In order toachieve Information interoperability, we need to addressthe heterogeneity of data between the different systems.Heterogeneity of data can be of at least two types:

(i) Structural heterogeneity where the data are differentbecause the schemas used to store the data are different(though the content itself might not be heterogenous).(ii) Semantic heterogeneity occurs when data in differentsystems is captured using:

(a) Different levels of granularity.(b) Different measures/scales.(c) Different labels/names to represent the same

entity.

When applied to mental health, issues at the level oftechnical interoperability can be solved by the use of Gridenabled services. However information Level interoperabil-ity is harder to achieve. This is especially the case when dif-ferent scales of assessment (e.g. PSE, PANSS, SCID, andOPCRIT) are used to record data leading to structural het-erogeneity of data. Differences in the levels of granularitybetween assessment scales and also the clinical classifica-tions lead to semantic heterogeneity of data. In order toachieve interoperability, issues in two important areas ofmental health need to be addressed. These areas are scalesof assessment (assessment scales) and concept definitions(classification systems).

8 NOS stands for ‘Not Otherwise Specified’.

44 Jay (Subbarao) Kola et al. / Cognitive Systems Research 11 (2010) 42–52

2.1. Scales of assessment

Different scales for quantifying and recording symptoms(e.g. PSE, PANSS, SCID, and OPCRIT) have been tradi-tionally used in psychiatry (from here on referred to as Psy-chiatry Assessment Forms). These Psychiatry AssessmentForms offer the means to standardise the type of data col-lected in a clinical encounter and to reach a diagnosis. Inthe case of Structured Clinical Interview for DSM Disorder(SCID), this is reflected in the name and the way the actualPsychiatry Assessment Form is structured. Similarattempts at structured data entry for a clinical encounterare gaining renewed interest in other medical specialities.The openEHR Archetypes (Beale, 2002) and the HL-7 Clin-ical Document Architecture (CDAs) (Dolin et al., 2006) areexamples of similar attempts in the medical informaticsdomain. Archetypes are models of a domain concept,expressed using constraints on instance structures of anunderlying reference model (reference). For example, anArchetype for recording tendon reflexes might represent‘Knee Jerk Reflex’ as being ‘Present’, ‘Absent’, or ‘Brisk’.This is similar to how PANSS captures information about‘Delusions’ being either ‘Absent’ or ‘Present’ with fivegrades of severity in between. The distinction betweenArchetypes and Psychiatry Assessment Forms is that thelatter are in some cases used to reach a diagnosis directlyand might have some heuristics built into them.

However both Archetypes and Assessment forms havecommon failings which have been highlighted by recentwork in Archetypes (Qamar & Rector, 2007; Qamaret al., 2007). These include:

(i) Ambiguous labels for concepts.(ii) Use of different levels of granularity for concepts.

(iii) Appropriate mapping to a common referenceterminology.

(iv) Lack of proper semantics in the models.

For examples, ‘Delusions’ are graded/scored on a scaleof 0-6 on the PANSS whereas they are graded/scored from0 to 2 on the PSE. The PSE also has around 20 types of‘Delusions’, while there are around eight correspondingconcepts in the SCID. The PANSS on the other hand pro-vides a single term for delusions and separate terms forsymptoms which maybe considered subtypes of ‘Delusion’.

Many of these issues arise due to the lack of commonsemantics between the terminology and data models(Qamar & Rector, 2007; Qamar et al., 2007). Ontologieshave been proposed to offer these unifying semanticsbetween data models and terminology models (Rector,Marley, & Qamar, in press).

2.2. Definitions of concepts

A second and equally important obstacle to data inte-gration in psychiatry is the lack of consensus on the mean-ing of terms. ICD-10 and DSM-IV are the two major

classification systems in the domain. Though ICD-10 andDSM-IV are much better aligned than their precursors,they do not always agree on the meaning of terms usedto label disorders and their classifications. For example,the classification hierarchies of Schizophrenia in ICD-10and DSM-IV are different.

Listing 1: The sub-types of Schizophrenia in DSM-IV

Schizophrenia in DSM-IV

–Disorganized type (295.1)–Catatonic type (295.2)–Paranoid type (295.3)–Residual type (295.6)–Undifferentiated type (295.9)

Listing 2: The sub-types of Schizophrenia in ICD-10

Schizophrenia in ICD-10

–Paranoid Schizophrenia (F20.0)– Hebephrenic Schizophrenia (F20.1)–Catatonic Schizophrenia (F20.2)–Undifferentiated Schizophrenia (F20.3)–Post-schizophrenia depression (F20.4)–Residual Schizophrenia (F20.5)–Simple Schizophrenia (F20.6)–Other Schizophrenia (F20.8) � Cenesthopathic schizophrenia� Schizophreniform:

disorder NOSpsychosis NOS

–Schizophrenia, unspecified (F20.9)

At a first glance, there seem to be some one to onematches between the ICD-10 and DSM-IV hierarchies ofSchizophrenia as shown in Table 1. However, not all map-pings are straightforward mappings of labels. For example,Hebephrenic Schizophrenia (F20.1) and Disorganized type(295.1) can only be mapped using some domain knowledge.

Some of the categories in ICD-10 have no obviousequivalents in DSM-IV. It is important to note that OtherSchizophrenia (F20.8) is different from Schizophrenia,unspecified (F20.9) and that in fact, Other Schizophrenia(F20.8) has a type Schizophreniform Disorder NOS8 whichdoes not include Brief Schizophreniform Disorders (F23.2).The DSM-IV on the other hand clearly separates Schizo-phrenia from Schizophreniform disorders. Similarly Psy-chotic Disorder NOS that is a type of OtherSchizophrenia (F20.8) in the ICD-10 is completely sepa-rated from Schizophrenia in the DSM-IV. There are alsoseem to be concepts in the ICD-10 like Simple Schizophre-nia (F20.6) and Cenesthopathic schizophrenia which donot have any equivalents in the DSM-IV. Another interest-ing case of incongruent hierarchies in the ICD-10 is Post-schizophrenia depression (F20.4) which clearly is a Depres-sive episode (F.32) and should not really be a type ofSchizophrenia. The ICD-10 does state: ‘If the patient no

Table 1Mappings between Schizophrenia types in ICD-10 and DSM-IV

ICD-10 concept DSM-IV concept

ParanoidSchizophrenia(F20.0)

Paranoid type (295.3)

CatatonicSchizophrenia(F20.2)

Catatonic type (295.2)

ResidualSchizophrenia(F20.5)

Residual type (295.6)

UndifferentiatedSchizophrenia(F20.3)

Undifferentiated type (295.9)

HebephrenicSchizophrenia(F20.1)

Disorganized type (295.1) (historically DisorganizedSchizophrenia has been called HebephrenicSchizophrenia)

Jay (Subbarao) Kola et al. / Cognitive Systems Research 11 (2010) 42–52 45

longer has any schizophrenic symptoms, a depressive epi-sode should be diagnosed (F32.–). If schizophrenic symp-toms are still florid and prominent, the diagnosis shouldremain that of the appropriate schizophrenic subtype(F20.0–F20.3)’.

However it still leaves this entity as a type of Schizo-phrenia without clarifying that this might in fact be a singlediagnoses of Depressive episode (F32) or Schizophrenia(F20.0–3) with concurrent Depressive episode (F32) whichis in fact two diagnoses. In ontological terms, such a diag-nosis is a type of both a Depressive episode (F32) and atype of Schizophrenia (F20.0–3). Problems with usingmono-axial classification like the ICD and use of conceptslike ‘NOS’ has been well documented in the medical infor-matics domain in Cimino’s classic paper (Cimino, 1998).

In fact, concepts which belong to more than one concepttype are common in medicine. For example Bacterial Pneu-monia is both a type of Lung Disease and also an Infec-tious Disease, because it has a focus of the lung and iscaused by some bacterium which is a type of an infectiousorganism. If this information were to be split down into aset of simple statements, it would be represented as

Listing 3: A list of simple statements about BacterialPneumonia

(i) Lung Disease is a disease with has a focus of Lung(ii) Pneumonia is a type of a disease with a focus of

Lung(iii) Infectious Disease is a disease caused by an infec-

tious organism(iv) Bacterial Pneumonia is a type of Pneumonia

caused by a Bacterium(v) Bacterium is a type of an Infectious Organism

9 http://www.w3.org/

It seems obvious from the above statements that bacte-rial pneumonia is both a lung disease and an infectious dis-ease. The same statements can be represented using firstorder logic and a computer can be used to deduce a similarconclusion and check the validity of classifying bacterialpneumonia along two different axes (and in the original

example of Post Schizophrenia Depression classifying itunder both Depression and Schizophrenia). Given the largenumber of terms used to describe psychiatric disorders, itmakes more sense to use a logic driven, mechanical processto maintain, extend and check the validity of knowledge.

However, this will require that the necessary domainknowledge be first captured in the form of logical state-ments that a machine can interpret. Knowledge modellingis the process of describing knowledge in a given domain asformal models, with entities in the domain being describedas concepts with relationships between them. In order tocapture the specifics of a given domain and to capturethe relationship between entities, several knowledge-model-ling languages have been described (DAML + OIL (Hor-rocks & Patel-Schneider, 2001), OWL (Patel-Schneider,Hayes, & Horrocks, 2004), GRAIL (Rector et al., 1993),etc.). In the next section, we will briefly introduce the basicaspects of knowledge modelling in medicine.

3. Knowledge modeling and ontologies

Medicine as a domain is rich in semantic relationshipsbetween entities and it often needs expressivity in modelingknowledge. Web Ontology Language (OWL) the WorldWide Web Consortium W3C9 recommended standard forrepresenting semantic links and knowledge is best suitedto capture these complex relationships. Knowledge model-ing requires specialised modeling environment and themost popular knowledge-modeling environment is Protege(Noy et al., 2001). Protege makes it easy to capture themulti directional and complex relations between medicalentities in an intuitive way. Protege OWL (Knublauch,Fergerson, Noy, & Musen, 2004) is an extension to theProtege environment that allows knowledge modelers tocapture knowledge in the OWL framework, which lendsa formal logical basis to the model. The particular groupof first order logic that powers one variant of OWL iscalled Description Logic (DL) (Baader, Calvanese, McGin-ness, Nardi, & Patel-Schneider, 2003) and this variant ofOWL is called OWL-DL. Integration of Description Log-ics into OWL allows encapsulation of rules (in the formof inferences, sub-sumption, etc.) in the OWL ontology.At this point, it is important to understand the generalarchitecture of a DL based OWL Ontology.

3.1. Features of an OWL-DL ontology

A formal ontology has a vocabulary that is defined interms of ‘concepts’ and ‘roles’ which define the relation-ships between the concepts.

Primitive concepts: These are the atomic building blocksof an ontology. These are often grouped together ashierarchies.

Fig. 2. The hierarchy of properties in the OWL ontology.

Fig. 1. The hierarchy of concepts in the OWL ontology.

Fig. 4. The description for ‘Infectious Disease’.

Fig. 3. The description for ‘Lung Disease’.

Fig. 5. The description for ‘Pneumonia’.

Fig. 6. The description for ‘Bacterial Pneumonia’.

46 Jay (Subbarao) Kola et al. / Cognitive Systems Research 11 (2010) 42–52

Properties: These represent roles that are then used todefine relationships between primitive concepts.Defined concepts: These are complex descriptions builtusing primitive concepts and properties.Restrictions: These are property–concept pairs qualifiedby logical attributes like ‘some’ and ‘only’, e.g.: ‘hasfocus’ some ‘Lung’.Axioms: These are statements or assertions about namedconcepts in the ontology. For example the statementthat a Concept A is related to a Concept B via a prop-erty ‘is type of’ is an axiom, e.g. A ‘is type of’ B.Reasoner: A service built into DL based ontologies,which allows ‘reasoning’ over an ontology. Reasoningusually consists of determining if a concept or descrip-tion is a child of another (sub-sumption). It also checksaxioms and descriptions in the ontology for logicalconsistency.

If all the statements about Bacterial Pneumonia in List-ing 3 were expressed in first order logic, using the ProtegeOWL Knowledge Editor, we would have the followinghierarchies and descriptions.

Figs. 1 and 2 show the hierarchy of ‘concepts’ and ‘prop-erties’ in the ontology.10 A ‘is a type of’ relationship is rep-resented as a parent and a child relationship (e.g.Pneumonia is a type of a Disease). We then describe ‘LungDisease’, ‘Infectious Disease’, ‘Pneumonia’ and ‘BacterialPneumonia’ in Description Logic as shown in Figs. 3–6,respectively.11

We then use the Reasoner to infer that Bacterial Pneu-monia is both a type of Infectious Disease and also a LungDisease as shown in Fig. 7.

If psychiatric concepts were defined in these terms usingformal logic, it might be possible to clarify some issues withconcepts like Post-Schizophrenia Depression. It would alsobe possible to formally and systematically check such clas-sification for syntactic and semantic inconsistencies. This inturn would reduce the ambiguity associated with terms/labels in various classifications and provide a means ofaligning different classification systems. However it isimportant to remember that language is flexible and logicis rigid. Therefore in defining psychiatric concepts using

10 These screenshots were generated using Protege OWL KnowledgeEditor (Knublauch et al., 2004)11 These DL descriptions are rendered in Manchester OWL Syntax which

is more user friendly than formal logic syntax (Horridge et al., 2006).

Fig. 7. The inferred hierarchy for ‘Bacterial Pneumonia’ generated by theReasoner showing it as a type of both ‘Lung Disease’ and ‘InfectiousDisease’.

Fig. 8. The importance of PANSS symptoms to the diagnosis ofSchizophrenia as rated by three professional groups.

Fig. 9. The importance of PANSS symptoms to the diagnosis of BipolarDisorder as rated by three professional groups.

Jay (Subbarao) Kola et al. / Cognitive Systems Research 11 (2010) 42–52 47

logic, it is important to capture the meaning of a conceptadequately before it can be represented logically. In thenext section we highlight the issues involved in capturingthe meaning of concepts and terms in psychiatry.

4. Knowledge acquisition and disambiguation

Shirky argues that ontologies that work well contain‘stable entities’ with ‘clear edges’.12 In this context, thereare important features of psychosis and psychotic disorderswhich make this domain particularly problematic for devel-oping an ontology. Although the ICD-10 and DSM-IVcontain specified criteria describing symptoms and disor-ders, both classification systems acknowledge that clear,discrete classification of symptoms and disorders is not cur-rently not possible. The ICD-10 states that mental disorderis ‘not an exact term’, although is generally used ‘...to implythe existence of a clinically recognisable set of symptoms orbehaviours associated in most cases with distress and withinterference with personal functions.’ (WHO, 1993). TheDSM-IV states that ‘there is no assumption that each cat-egory of mental disorder is a completely discrete entity withabsolute boundaries’; and states that the term ‘psychosis’has had many definitions in the past with the broadestbeing unable to meet the demands of everyday life andthe narrowest: delusions or hallucinations without insight(APA, 1994 and 2000).

Such freedom of interpretation inhibits meaningful spec-ification in an ontology. Also, agreed terms are a prerequi-site to meaningful association with other data sources fromclinical research. In the early stages of ontology develop-ment, we therefore sought to identify clinical features com-monly considered as most prominent and characteristic ofpsychosis and psychotic disorders as represented in estab-lished classification systems and measurement scales. Indoing so, we sought also to gain further understanding ofthe specification and scope required for the ontology.

In a small pilot study, we used established knowledgeelicitation techniques such as card-sorting (described in(Hayes et al., 2005)) and specially-designed questionnairesto help us identify user perceptions, assumptions andunderstanding of the domain and to synthesize areas ofcommon agreement and understanding. More specifically,we intended to find preliminary answers to questions suchas: how do groups from different professional disciplinesperceive symptoms and disorders represented in differentdata sources? Are there common concepts and ways ofdescribing? How many different levels of description arethere? How do different groups and individuals discrimi-nate between symptoms and disorders? In seeking tounderstand user expectations, opinions and perspectiveswe intended to gain an understanding of required specifica-tions and optimal granularity for a clear, streamlinedontology of psychosis.

12 http://www.shirky.com/writings/ontology_overrated.html.

Figs. 8 and 9 show the rated importance of symptomsand diagnostic terms from the PANSS, according to threegroups of professionals. From a list of 30 PANSS symp-toms written on cards, groups of individuals from the sameprofessional background had selected symptom cards theyconsidered applicable and rated them in order of impor-tance for the diagnosis of Schizophrenia and Bipolar Dis-order, respectively. The results of this exercise highlight anumber of issues. Between groups, there were differencesin the distribution of symptoms considered applicable toeach disorder and in their rated importance. Also, groupsappeared to more often agree on symptoms with highimportance ratings in each disorder than on those ratedas less important. For example, hallucinations were consid-ered by all groups as important to schizophrenia and delu-sions as important to bipolar disorder. Moreover, specificsymptoms were commonly rated as important in each dis-order. For example, ‘paranoid’ and ‘delusions of grandeur’,both types of delusion, were rated as characteristic ofschizophrenia and bipolar disorder, respectively. This indi-

48 Jay (Subbarao) Kola et al. / Cognitive Systems Research 11 (2010) 42–52

cates that despite the classification of ‘delusion’ being theimportant defining feature of psychosis, particular delusionsubtypes may be more characteristic.

A secondary task in the card-sorting exercise instructedgroups to match any symptom terms they considered verysimilar or equivalent with those they had marked present ineach disorder. The aim of this task was to reduce terms tocore symptoms and to attempt to identify unessentialsymptoms. Fig. 10 shows terms considered equivalent ineach group and indicates that in addition to there beinggroup differences, equivalence of terms varied accordingto the disorder within which the symptom was included.For example, delusions were effectively delusions of gran-deur, but in Bipolar Disorder only.

Together these findings indicate individuals hold pre-conceptions and expectations about particular disordersand also suggest that knowing the diagnosis of an individ-ual may colour the way that an individual’s symptoms areperceived. This is particularly evident when contrastingindividuals who use diagnostic scales functionally to diag-nose disorders, and those who use them in a purelyresearch context. As different people working with clinicaldata may view this data differently, quantifiably enhancing

Fig. 10. Symptom terms from the PANSS considered as v

agreement and reducing individual variation may beimportant for an ontology with the purpose of creatinginteroperability between data sets.

5. Methodology for an ontology

In the previous sections we have highlighted the issues ofusing different measures for recording data (data models)and the lack of a unifying reference terminology (terminol-ogy models). A well-designed ontology, can address theseissues by providing an adequate link between the datamodels and the terminology models (Rector et al., in press).A well-designed ontology is easily extensible, localisableand maintainable. Though ontologies are often seen asrigid monolithic entities, it is essential that an ontologyhas all the characteristics listed above especially in the fieldof clinical research where new discoveries are being madeall the time and our understanding of the domain is fre-quently being updated. For this reason, the architectureof an ontology striving to uniformly characterize psychosisis as important as the content of the ontology. There are atleast two different levels at which data integration in psy-chosis needs to be achieved. The first is at the level of the

ery similar or equivalent by three professional groups.

Jay (Subbarao) Kola et al. / Cognitive Systems Research 11 (2010) 42–52 49

actual scales/templates being used to record the data. Thesecond is at the level of the codes or concepts being usedto represent the diagnoses of the patients.

As such a unifying ontology will need to reflect and han-dle integration at these two levels. If one were to describethe first level as a design level task, the second can bedescribed as a content level task. If the ontology modelwere split along these axes, we would have a ‘PhysicalDesign Model’, which reflects the need to integrate differentassessment scales, and a ‘Content Design Model’, whichneeds to unify the content over the entire domain ofpsychosis.

5.1. Design model

This describes the physical aspects of the ontology archi-tecture that determine how various data models are inte-grated. Physical Design Model refers to design leveldecisions that determine how the ontology itself is physi-cally split and interacts with the underlying data models/databases. In most data integration activities that useontologies, at least three different design models have beenidentified on the basis of the design of the actual ontologyand its relationship to the data models (Wache et al., 2001).

Case I: There is a single domain ontology that spans thecontent of all the data models. This global ontology henceneeds to contain all the terms with in each of the datamodels and needs to define unifying semantics for usebetween all the data models as shown in Fig. 11.

Fig.(Fig

(i) This has the advantage of offering a global coverageof the entire domain and hence the possibility ofdefining the semantics for most of the domain.

(ii) Though this method offers broad coverage of thedomain, it is very labour intensive to build a globalontology which is large enough to offer extensivecoverage of a domain. Also, this approach is idealwhen there are no existing data models, becausedata models can be then defined on the basis ofthe semantics in the global ontology.

11. Case I: Data Models are directly mapped to a Global Ontology.ures adapted from Wache et al. (2001).)

Foe

Case II: There are multiple ontologies that are defined atthe level of each different data model and capturethe semantics of the content adequately andaccurately. However, these multiple ontologiesneed not all contain the same terms, so cross-mappings (mappings between the different ontol-ogies need to be defined) as shown in Fig. 12before data instantiated by different data modelscan be integrated.

(i) Conceptually the easiest to generate at initiallybecause one simply renders the database schemainto an ontology and adds definitions asnecessary.

(ii) However, defining cross-mappings between themultiple ontologies might not always be easy.This might especially be the case when the ontol-ogies have different levels of granularities and usedifferent scales to represent similar entities.

Case III: This is similar to Case II except a shared/com-mon vocabulary is now used to cross-map andalign concepts in each of the intermediate ontol-ogies as shown in Fig. 13. This addresses theissue in Case II where there were no commonconcepts between the multiple ontologies.

(i) In practice this might be the easiest way to orga-nise content especially when there is a chance thatnew data sources might need to be added at regu-lar intervals.

(ii) However, identifying the scope of the commonvocabulary and aligning seemingly differentontologies may not always be an easy task.

Given the different assessment forms (data models)already in use in mental health research, it seems moreappropriate to adopt an ontology design along the linesof Case III. In adopting this model, one would produce for-mal logical models of each of the individual data models(e.g.: PSE, SCID, and PANSS) as the first step. Cross-map-ping between each of these logical models would then beattempted resulting in systematic identification of mappingissues between these models. In a third step, these issueswould be addressed by defining appropriate alignments

ig. 12. Case II: Data Models are directly mapped to a correspondingntologies (OntA, OntB and OntC) which have cross-mappings betweenach other.

Fig. 13. Case III: Data Models are directly mapped to a correspondingontologies (OntA, OntB and OntC) which have cross-mappings betweeneach other and are also mapped to a larger Common Vocabulary.

13 Unpublished AAAI 2008 article (accepted workshop paper).

50 Jay (Subbarao) Kola et al. / Cognitive Systems Research 11 (2010) 42–52

and maps between the models where feasible and by iden-tifying a common shared vocabulary and logical modelwhen such alignment is not possible. The end result of thisapproach will be formal logical models for each data modeland a means for interconverting data between these datamodels. It is likely that in identifying and building thiscommon shared model, we will need to define concepts thathave been loosely defined before. This method offers theflexibility that new data models in the future can be addedin and mapped to the existing ontology by following thesame steps.

5.2. Content model

The Content Design Model refers to the actual Descrip-tion Logic expressions and constructs that are used todescribe the ontology. There are two important featuresthat need to be considered: (i) expressivity of the ontologyand (ii) granularity of the ontology. A sufficient level ofexpressivity is important to ensure that any concepts thatneed to be added in the future can be adequately expressedin terms of existing concepts. Sufficient level of granularityin defining concepts is necessary to ensure that instances atdifferent levels of granularity/detail in the data models canall be aligned to one concept in the ontology. Because thedegree of expressivity and granularity used in an ontologyvary with the use case, it is hard to define a Content Modelfor an ontology. A measure of quality of the ContentModel will be to determine the amount of effort requiredto add an instance of a new but slightly different datamodel to the ontology.

Another advantage of using an OWL based ontology isthe possibility of modularising the content of the ontology.Modularisation helps to conceptually separate out the con-tent (like anatomy and physiology) into individual ontolo-

gies. This has the advantage of promoting reuse ofontologies and is discussed in more detail by Rector et al.13

6. Issues with the domain

At a different tangent to the mechanics of building anontology is the issue of the role of classification systemsin psychiatry. An ontology is considered a higher level, for-mal, meta model of the domain. However, the domain itselfseems to be unsure of the role of existing classification sys-tems. Broome describes two existing approaches to classifi-cation; the ‘realist’ approach and the ‘pragmatist’ approach(Broome, 2006). The ‘realist’ approach views all psychiatricdisorders as being validated by a definitive essence that canbe genetic, neural, phenomenological or cognitive. The‘pragmatist’ views classification as fulfilling a certain pur-pose and that diseases themselves do not necessarily havea discrete essence though individuals with these diseasesmight share some generalisations that might have prag-matic benefits for diagnosis and treatment. In the absenceof definite etiological and pathophysiological basis for cer-tain psychiatric diseases, it is hard to define whichapproach existing classification systems take. For example,dementia of Alzheimer’s disease is known to have somepathophysiological basis, but can the same be said of hal-lucinations in Schizophrenia? There seem to be concurrentviews in the domain that classification systems need toreflect descriptions of on the entity similar to the rest ofmedicine and also include some information of about epis-temology and clinical course (Jablensky & Kendell, 2002;Ustun, Chatterji, & Andrews, 2002). Broome again pointsout that opinion seems divided among domain expertsabout what constitutes a psychiatric disease/disorder rang-ing from a pathophysiological entity (Wakefield & First,2003; Wakefield, 1992) to a socio-anthropological entity(Haslam, 2002; Cooper, 2004). Unfortunately, existingclassification systems do not deal with these higher-levelquestions and are often viewed as means of diagnosingand labelling diseases for record keeping. In proposing anontology for psychosis, we do not directly try to addressany of these higher level issues in the domain but we aimto reduce the confusion about the various labels used todescribe disorders and phenomenon in clinical practice.We start with essentially a reductionist view of the domainin that we try to describe domain in terms of few unambig-uous atomic concepts and then build on these concepts todescribe more complicated concepts in psychiatry. It islikely that in taking this approach we might identify areasof confusion or ambiguity in the domain that can serve as agood starting point for efforts to clarify existing classifica-tion systems. We are also aware that in the absence ofbetter alternatives, ICD-10, DSM-IV and PsychiatricAssessment Forms will continue to be used in clinicalpractice. We propose an ontology as a means of aligning

Jay (Subbarao) Kola et al. / Cognitive Systems Research 11 (2010) 42–52 51

data that continues to be collected and making it reusablebetween the various sub specialities of the domain andhope that it will serve as a good starting point for futureefforts at developing a better classification for clinical psy-chiatry/psychology.

7. Discussion

Kendell and Jablensky identify some important desider-ata for assessing a classification in psychiatry; validity ofdiagnosis, clinical relevance, reliability, cognitive ease ofuse, applicability across settings and cultures and meetingthe needs of various users (Jablensky & Kendell, 2002).In describing the structural features of classifications, theyidentify two axes along which current classification systemsare structured: categories and dimensions. ‘Categorical

models’ are traditional representation models that arebased on causes, presentation, treatment, etc. of the disor-ders. Most medical classifications are based on this struc-ture. They suffer from the danger of encouraging adiscrete entity view of psychiatric disease that is not alwaystrue. ‘Dimensional Models’ however explicitly define quan-titative variation and grades of transition between disor-ders. Kendell and Jablensky argue that such models offerthe advantage of dealing with cases that satisfy diagnosticcriteria of more than one disorder or in the case of ‘sub-threshold’ conditions. This view is supported by Bedirhanet al. who state that familial predisposition to schizophre-nia is best demonstrated when the definition of Schizophre-nia is broadened to a notion of ‘schizotaxia’ rather than therestrictive criterion of 6 months of continuous illness(Ustun et al., 2002). However, categorical systems arelikely to continue in existence on the basis of their prag-matic benefit in clinical practice.

Recent advances in neuroscience at the genetic andmolecular level have also contributed to changes in percep-tions of classification systems in psychiatry. Bedirhan et al.argue how current descriptive classification systems do notreflect neurobiology, citing examples of how novel under-standing of Obsessive–Compulsive Disorder and hereditarylinkage between Schizophrenia and Bipolar Disorders(Ustun et al., 2002; Mineka, Watson, & Clark, 1998; Kauf-man & Charney, 2000; Kendler, Karkowski, & Walsh,1998). In cases such as these, novel neurobiologicaladvances could change our understanding of disordersand hence their classifications. However to enable theseadvances, it is important that researchers are able to linkdata from molecular, genetic and imaging studies with clin-ical data. Given the current state of disparity in data mod-els and the lack of any means to exchange data betweenclinical settings and basic research, it is unlikely that theseadvances will be easy to achieve. It is for this reason that aunifying ontology that allows data interoperability in thedomain is a more immediate concern than the more philo-sophical issues of ‘dimensionality of classifications’ and‘what a disorder means’. It is likely that the future versionsof the ICD and the DSM will address these issues. An

ontology developed along the lines described above wouldthen serve as a starting point for any future versions ofthese classification systems.

A well-designed OWL ontology can be classified alongvarious axes and hence need not necessarily adopt an‘essentialist’ or a ‘pragmatist’ view. There also exist a largenumber of other biomedical ontologies in the OWL formatin the public domain. It is possible that entities in the psy-chosis ontology can be linked to these existing ontologiesto infer new relationships or knowledge. For example, agene XYZ linked to Schizophrenia in the psychosis ontol-ogy could be mapped to a gene in the Gene Ontology (Ash-burner et al., 2000) that might have more informationabout its mechanism of action or gene products. This willlink the gene XYZ to the whole range of bioinformaticsresources that are in turn linked to the Gene Ontology.BIRNLex as described earlier attempts to provide a con-trolled vocabulary for representing concepts in neuroanat-omy, molecular species,subjective information, behaviouraland cognitive processes and experimental design, but it cur-rently does not include clinical psychiatry concepts. Indeveloping this ontology, we would bridge the gap betweenbasic neurosciences that BIRNLex supports and clinicalpsychiatry.

8. Conclusion

In order to bring basic neuroscience research closer toclinical psychiatry, it is important to move data fromresearch to clinical practice and vice versa. We havedescribed obstacles to interoperability at various levels.However, the existence of disparate and un-interchange-able data models (like PSE, PANSS, SCID, and OPCRIT)in clinical practice is an important obstacle in achievingdata interoperability. This problem is further worsenedby the use of two classification systems that are not alwaysinteroperable. In order to achieve syntactic and semanticinteroperability between data sets that have been encodedusing data models, we need a formal ontology. Thoughontologies have been successfully used in other disciplineslike bioinformatics, defining logical equivalents of psychi-atric entities is not always easy. In order to build such anunifying ontology, it is important to adopt good designprinciples at an early stage as outlined in Section 5. Sincethe word ‘ontology’ raises significant interest, we haveclearly outlined the use cases for the ontology. The pro-posed OWL ontology will attempt to serve as a unifyingontology for the domain of psychosis but will not directlyaddress higher level issues of ‘dimensionality’ and ‘defini-tion of disorders’ which seem to be at the heart of psychi-atry as discussed in Section 6. It is important to rememberthat such an ontology might serve as a logical approxima-tion of some psychiatric concepts due to the lack of consen-sus in the domain itself. We however believe that a well-designed ontology can serve as a good starting point inaddressing these high level issues in the domain and willalso serve as a useful to link to the various bioinformatics

52 Jay (Subbarao) Kola et al. / Cognitive Systems Research 11 (2010) 42–52

resources available thereby bringing clinical psychiatry clo-ser to neuroscience research.

Acknowledgements

This work was supported by funding by the MRC aspart of the NeuroGrid, the PsyGrid and NeuroPsyGridprojects. The authors would like to acknowledge the sup-port of Sackler Foundation.

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