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Expert Systems with Applications 35 (2008) 991–1010

Expert Systemswith Applications

Computing context-dependent temporal diagnosis in complex domains

Jose M. Juarez *, Manuel Campos, Jose Palma, Roque Marin

Department of Information and Communication Engineering, Faculty of Computer Science, University of Murcia, Campus de Espinardo, 30100 Murcia, Spain

Abstract

Over the years, many Artificial Intelligence (AI) approaches have dealt with the diagnosis problem and its application in complexenvironments such as medical domains. Model-Based Reasoning (MBR) is one of the approaches that traditionally have tried to solvethis problem thanks to its capacity for modelling and reasoning. The consideration of the temporal dimension in these domains is a chal-lenging topic in MBR, especially if temporal imprecision is taken into account. Unfortunately, despite there being many successful MBRsystems, there are still two fundamental problems in their development at the aforementioned domains: (1) the degree of dependencybetween the model used and the domain; and (2) the reutilization of the systems when the domain changes.

First this paper proposes a set of basic requirements for the design of Knowledge-Based Systems that will help to solve the problem oftemporal diagnosis for environments of high conceptual complexity. From these principles and through a deep analysis of the variousapproaches present in AI we establish a generic framework that addresses both goals by integrating MBR and ontologies for domainknowledge representation in order to describe a intermediate model representation to facilitate the low dependency between the modeland the application domain.

Finally, this paper demonstrates the use of the framework by developing a diagnosis system within a real medical environment (Inten-sive Care Unit) with a step-by-step description of the process, from the architecture through to implementation.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Temporal diagnosis; Model-based reasoning; Knowledge-based systems; Artificial intelligence in medicine

1. Introduction

A great effort has been made over the last decades todevelop problem-solving methods for generic tasks (Chan-tler, Coghill, Shen, & Leitch, 1998; Minsky, 1975; Schreiberet al., 1999). In the particular case of the diagnosis task,one of the most fruitful and well-studied ones is themodel-based diagnosis (MBD) (Brusoni, Console, Terenzi-ani, & Dupre, 1998; Portinale, Magro, & Torasso, 2004;Struss, 1997).

These techniques have demonstrated their capacity tosolve the diagnosis task in many applications. However,the development of knowledge-based systems (KBSs) fordiagnosis is still a hard process in which difficulties arise.

0957-4174/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.eswa.2007.08.054

* Corresponding author. Tel.: +34 968367345; fax: +34 968364151.E-mail address: jmjuarez@um.es (J.M. Juarez).

This is particularly true in medical applications (Console& Torasso, 1991; Montani et al., 2003; Taylor, Fox, &Todd-Pokropek, 1997). The most important problem ofthese practical obstacles is, without doubt, the domaincomplexity, which makes the modelling difficult, andrequires advanced techniques to integrate behaviour mod-els and domain descriptions.

A second problem is the relevance of the context, inorder to make decisions in complex environments. Conse-quently, behaviour models must have the capacity of self-modification, depending on the subtle behaviour alterationsof the environment conforming each particular configura-tion of the contexts.

Finally, in the design of KBSs for diagnosis, a third fac-tor that must be taken into account is the importance of thetemporal dimension. Usually, it is necessary to arrangemechanisms to manage and model the time in order todescribe certain real life behaviours. This is especially

992 J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010

necessary when the behaviour to be modelled is of an evol-utive nature, like in many complex domains. In the litera-ture there is a wealth of temporal reasoning approaches(Kahn & Gorry, 1977; Pani, 2001) and representations(Allen, 1983; Shoham & McDermott, 1988; Vila, 1994),including those situations where time must be managedimprecisely (Dubois, Lang, & Prade, 1994; Marın &Navarrete, 2003). In any case, the inclusion of time in thediagnosis task unavoidably makes the KBS developmenteven more complex.

These three problems (knowledge domain representa-tion in MBD, contextual dependence, and temporal rea-soning) have been studied, and many proposals havebeen made in the past. Nowadays, however, there is a lackof generic architectures that allow the development ofKBSs based on models able to manage these three aspectsintegrally: (1) feasible and efficient temporal diagnosis; (2)management of complex domain concepts; (3) descriptionof models sensitive to contexts. We call this kind of prob-lems Context-Dependent Temporal Diagnosis (CDTD).We consider it necessary to provide generic solutions tosolve CDTD problems, not only from the knowledge levelbut also at the design level, in order to simplify the devel-opment of this complex applications sensibly.

In this work, we propose a generic architecture for thedesign of CDTD systems. This proposal is based on thedescription of the fundamental design requisites for thiskind of applications and the study of the most relevantalternatives in the literature to solve each of the problemscited. The main contribution of this study is the proposalof a general framework for the development of CDTDarchitectures. The demonstration of the suitability of thisframework is shown by the description of our experiencesin the development of a concrete architecture which appliesthis framework: the ACUDES architecture (Architecturefor intensive Care Unit DEcision Support). ACUDES isan architecture developed and implemented for decisionsupport assistance in the Intensive Care Unit (ICU). Thesuccess of the development of this system in this complexmedical domain demonstrates that the criteria selected todescribe the framework are correct.

The paper is divided in two main parts. The first part(Sections 2 and 3) analyses and describes the CDTDarchitectures. Section 2 describes the fundamental designrequisites of CDTD architectures and studies the alterna-tives available in the literature for domain conceptual mod-elling, temporal reasoning, context-dependent behaviouralmodelling, and the diagnostic problem. In Section 3 a gen-eral framework is proposed for the development of thesesystems. The second part (Sections 4–6, refsec.tbm, 8) pre-sents ACUDES as an example of CDTD architecture,describing each of its components and its particular adap-tation to the ICU: domain ontology, fuzzy temporal con-straint model, causal–temporal behaviour model,knowledge acquisition tools, and the abductive temporaldiagnostic process. Finally, the paper provides conclusionsfrom the results.

2. Design requisites of a CDTD architecture

This section describes the fundamental requisites that anarchitecture must fulfil in order to deal with the temporaldiagnosis problem in environments of high conceptualcomplexity.

In the design of KBS architectures centred on diagnosis,we have considered three fundamental problems which areinterrelated: the resolution of the temporal diagnosis, thecontext dependencies, and the management of high com-plex conceptual domains. On the one hand, these domainsrequire a detailed conceptual analysis, establishing whichare the most relevant elements, the relations amongst them,and their behaviour. This behaviour, including the tempo-ral one, must be considered by the diagnosis task in orderto obtain a domain consistent solution. On the other hand,the diagnosis task must perform the temporal managementas efficiently as possible, but taking into account that in thiskind of domains imprecise temporal descriptions can bemade. Additionally, the definition of an architecture of thisnature should be flexible enough to be applied to any con-crete domain that satisfies the demands described. Theseare the fundamental requisites:

1. Management of the conceptual complexity of the appli-cation domain.

2. Processing of imprecise temporal information.3. Definition of models that describe the behaviour depen-

dent on time.4. Establishment of mechanisms to make the behaviour

sensitive to contextual knowledge.5. To be able to cope with the inefficiency of hypothesis

generation of diagnosis methods.6. Independence between the architecture and the concrete

domain description.

The following subsections describe in detail the possiblealternatives in the literature, and those which are the mostsuitable for applying our requisites in a CDTDarchitecture.

2.1. Conceptual complexity of the application domain

Our work is centred on the diagnosis problem study indomains of high conceptual complexity. The reductionand simplification of domains usually helps to developmore efficient systems. However, in some situations (e.g.certain medical fields), the development of the diagnosistask requires adherence to the complexity of the applica-tion domain. To this end, we define a domain as havinga high conceptual complexity if it has:

1. A large number of concepts of different nature.2. Highly interrelated concepts.3. Different kinds of relations.4. The temporal dimension is a fundamental element.

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The decision of how to represent the application domaincertainly conditions the KBS description at both theoreti-cal and the practical level. Obviously, in the domain stud-ied (MDB in medicine), an implicit representation of thedomain within the diagnosis task is impracticable. Further-more, an explicit domain representation also provides thegenericity of the diagnosis problem solving method andfacilitates the maintenance and reuse of knowledge.

2.1.1. Application domain representation

One of the classical representations to model the domainknowledge in KBSs explicitly is the Semantic Networkapproach. This kind of representations has its origins inthe Semantic Memory model from the cognitive psychol-ogy, proposed by Quillian, Memory, and Minsky (1968).The fundaments of this proposal are based on the observa-tion that the mind manages to form concepts by groupingelements from an episodic memory. This proposal is agraphical model that can be easily translated into its sym-bolic form and it provides a useful tool to represent taxo-nomic knowledge. The Semantic Network language iscomposed by nodes and directed arcs, which are bothlabelled. The syntax and semantics is (in an informal man-ner) simple to describe and comprehend. However, itsexpressiveness is limited to structures where axiomaticdomain descriptors are not possible.

Some years later, the Frame proposal was introduced byMinsky (1975). This proposal can be considered as a tex-tual representation of Semantic Networks which can alsorepresent procedural knowledge. Each node of a corre-sponding object becomes a frame, described by an identifierand a slot set. The capacity of combining procedural anddeclarative knowledge allows activation of some particularprocedures depending on the domain configuration. In thisproposal it is also quite easy to incorporate meta-knowl-edge in its representations, that is, incorporate knowledgeof the frame itself. However, this technique goes beyonda descriptive definition of the knowledge, integrates theprocedural knowledge that is difficult to maintain whenthere is a large number of elements, or if they are highlyinterrelated.

Nowadays, ontologies are widely used for different pur-poses and in different fields within AI systems. The mostquoted definition of ontology in the literature, by Gruber(1993), states that an ’’ontology is an explicit specification

of a conceptualisation’’ (other relevant definitions suggestimportant contributions such as: sharing, modelling, logics,etc.).

The specification of the domain knowledge by the use ofontology provides a knowledge-based system with theopportunity of adding the semantics of the domain, statingan explicit conceptual description of the domain. Thisexplicit formalism also facilitates the maintenance of theknowledge base. From the architectural point of view,another advantage of the use of ontologies is that, unlikeadding semantics directly to the problem solving method,

it permits a low coupling between the inference engineand the domain. This means that different ontologies couldbe added or removed from the system, keeping the samePSM, which is a clear example of how ontologies also facil-itate sharing and reuse of knowledge.

We consider that the use of ontology is the most appro-priate approach to describe and manage the knowledge ofcomplex conceptual domains, allowing the domain descrip-tion to be independent of the problems solving method.However, this representation compromises the necessarymeans to manage the ontology in the stated architectureand to guarantee the semantic consistency of the use ofthe ontology in the rest of the architecture.

2.2. Management of imprecise temporal information

The notion of time is implicit in most of the problemsthat are considered complex, and in like manner, humanunderstanding requires the same notion to solve them.Temporal representation and reasoning have been studiedby many disciplines including computer science, philoso-phy, psychology, and linguistics. Within the computationdisciplines many areas cope with temporal problems, forexample in the development of Information Systems, Prob-lem Verification, or Artificial Intelligence (AI). AI is themost active field in the study of time (Kahn & Gorry,1977; Pani, 2001; Vila, 1994), stating that it is more conve-nient to use explicit representations of time in order toavoid its management within problem solving methods.

In this section, some of the most relevant approaches tomodeling time are analysed, with descriptions of which isthe most suitable to represent time in conceptual complexdomains.

2.2.1. Alternatives for temporal modelling

The goal of temporal reasoning is to formalise thenotion of time, providing the tools to represent the tempo-ral aspects of knowledge, and using modelsto reason withthem (Felix, Barro, & Marin, 2003; Pani, 2001). To thisend, in conceptual complex domains we need a temporalrepresentation and the capacity to reason.

The analysis of time is mainly centred on the elements ofthe temporal dimension (references or temporal events), therelation between them, and atemporal assertions (those ele-ments that do not belong to the temporal dimension).

A fundamental decision in order to model time is whatthe temporal references are. Temporal events are repre-sented in the temporal dimension by temporal entities (orprimitives). The literature points to two kinds of temporalentities: temporal points and intervals. Depending onwhich kind of temporal entity is used, different kinds ofquantitative temporal relations can be defined, thus describ-ing different algebras. The point algebra (Shoham &McDermott, 1988) assumes as a unique primitive the tem-poral points and the relations amongst them (Vilain &Kautz, 1986). One of the interval proposals most referredto is the algebra proposed by Allen (1983), which states

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intervals as primitives. An integrative proposal is thatdescribed by Vila (1993) defining a simple method formaintaining both temporal entities (points and intervals).An alternative approach is to state a point duration algebra(McDermott, 1982; Navarrete & Marin, 1997), which isbased on the point algebra, but adding duration variablesthat allow the algebra to be more expressive than the origi-nal point algebra.

Other kinds of relations that must be also considered arethe qualitative temporal relations, that is, relations thatexpress quantification values of time to relate temporalentities. Thus, the simplest quantitative relations are thosethat describe an absolute numerical temporal valuebetween the time origin and the instant when the temporalevent occurs (absolute relations). A date, an hour, or anyother kind of conventional timestamp usually representsthese relationships. Another advantage of qualitative rela-tions is that efficient algorithms can be applied. But quali-tative relations can also be used amongst the rest of thetemporal events (relative relations), to describe acyclicgraphs, providing a more flexible model.

Another important factor is having at our disposal a suf-ficiently expressive language to describe the relationsbetween the temporal primitives and the capacity to queryabout them. In the point algebra (Shoham & McDermott,1988; Vilain & Kautz, 1986) three binary basic relationscan be distinguished: before, after, and equals. The Allen’sinterval algebra (Allen, 1983) states 13 kinds of binarybasic relations between intervals. In both algebras, the rela-tions could be extended to represent quantitative relations.However, the establishment of these relations amongstprimitives involves solving two problems: consistency andconstraint propagation. In these kinds of algebras the useof techniques from the Constraint Satisfaction Problem(CSP) field is particularly convenient to model and solvethese problems (Marın & Navarrete, 2003; Vilain & Kautz,1986). The CSP techniques provide a simple way to formal-ize the temporal reasoning models, and moreover, well-known algorithms could be used for consistency and prop-agation purposes. For complex domains, an expressivemodel is necessary to deal with the different kinds of tem-poral expressions, but, in practice, the model must alsohave the least computational complexity possible. Forinstance, in the case of Allen’s interval algebra, where thebasic relation set is 13, and the set of all possible disjunc-tions between relations is 213, the consistency computationis NP-Complete (Vilain & Kautz, 1986). In this case, thesolution consists of the model simplification by the reduc-tion of a tractable relation subset.

Other kinds of relationships which must also be consid-ered are those which quantify time in some way. Thus, themost simple quantative relationships are those describing,with regard to an origin, the instant of which a temporalevent occurs in an absolute and numerical way. These areprecisely represented by a date, a time or any other conven-tional numerical value. Moreover, they have the advantageof being easy to use with practical algorithms. However, it

is also possible to establish quantative relationshipsbetween different (relative) events, which can often be rep-resented by acyclical graphs, and by leaving a wide repre-sentation margin of the quantification of the relation.

2.2.2. Temporal imprecisionIn most real life situations, and in many complex fields,

the notion of time is linked to a certain degree of vague-ness. Therefore, any attempt to develop a valid applicationfor these fields would have to provide the adequate tempo-ral models to deal with the aforementioned imprecision.

In most temporal models dealing with the problem oftemporal imprecision, this is included in the specificationof the temporal relations between temporal entities. Oneway of reflecting this imprecision is through the collectiveuse of qualitative and quantative temporal constraints,given that in this way it is possible to establish different lev-els of precision (Pani, 2001) (for example, ‘‘John arrivedbefore Steve’’ is less precise than ‘‘John arrived 2 minbefore Steve’’). However, this approximation is not veryflexible, as it does not establish an explicit way for express-ing the aforementioned imprecision. To solve these prob-lems there are other proposals which provide a moreflexible way of dealing with temporal imprecision.

One of the possibilities is the use of fuzzy-sets, which pro-vide us with a formal mathematical framework to representuncertainty. In (Klir & Folger, 1992) a fuzzy-set is definedthrough its membership function l: X! [0,1]. Each ofthese sets could represent temporality with a degree of tem-poral imprecision, depending on the characteristics of theset. For example, ‘‘John arrived approximately 7 minbefore Steve’’ could be represented by a fuzzy-set wherethe function l is defined as (l(<6) = 0, l(6) = 0.5,l(7) = 1, l(8) = 0.5, l(>8) = 0). When proposing an effi-ciently computable function, the most frequent approxima-tions are the description of an interval, a triple or quartet ofvalues. On the other hand, Dubois and Prade in their The-ory of Possibility (Dubois et al., 1994), as part of the generaltheory of evidence, propose describing the certainty associ-ated with that of events. This theory offers a much moreflexible way of modelling the uncertainty than the Theoryof Probability (which is based on the frequency of occur-rence of an event), given that it is much more normative.In (Klir & Folger, 1992), a link is established between the-ory of fuzzy-sets and the theory of possibility, in that amembership function to a diffuse set can be defined by apossibility distribution. In this way, the diffuse sets associ-ated with temporal relationships can be representedthrough possibility distributions. Therefore, in the previousexample with ‘‘John and Steve’’ the function l is definedwith possibility distribution (0,0.5, 1,0.5,0). Thus, we con-sider that an appropriate model for the imprecise adminis-tration of time in complex fields should be that which:

• Considers instants as intervals.• Makes it possible to establish quantative and qualitative

temporal constraints.

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• Formally models the imprecision and uncertainty whichgenerates the said imprecision.

2.2.3. Time independent of the domain and the problem

With the aim of making modelling of the complexdomains easier for those intending to apply the architectureand of improving efficiency for reusing the proposal, it isconvenient to separate modelling and time managementfrom modelling and domain management. This idea isbased on the proposal by Kahn and Gorry (1977) on thestrict separation between knowledge of the field and knowl-edge of time.

This separation between the temporal domains and thatof problem solving can be approached in two ways. On theone hand, there are systems in which the problem solver iscentred not only on obtaining a solution but also on dealingwith the implicit administration of time. Another alternativeconsists of building a generic temporal reasoner, whose rea-soning services are taken from that of the problem resolutor.Therefore, the temporal reasoner deals only with manage-ment tasks and the inference of time and temporal relation-ships, as it does not need any knowledge of the domain. Thislast solution offers numerous advantages, and is thus able toestablish an independent temporal reasoner from the knowl-edge of the domain and the resolutor’s task, thus favouringthe reuse both of domain knowledge and time management.On the other hand, the description of the specific domain istotally independent, and on the other it allows the temporalreasoner to carry out any task relating to different levels ofthe architecture in a generic way.

2.3. Description of behaviour dependent on time and context

One of the requirements for diagnostic architecture inhigh conceptual complexity domains is the use of behaviourdescription models which are sensitive to context. In the fieldof artificial intelligence there are many proposals along thisline: methodologies for domains which are not highly struc-tured, such as Case-Based Reasoning (Aadmot & Plaza,1994; Montani et al., 2003; Pal & Shiu, 2004), techniquescentred on rules such as RBR (Sun, 1995) or techniqueswhich include statistical knowledge (Belief Networks, Heck-erman, Geiger, & Chickering, 1994; Lauritzen & Spiegelhal-ter, 1990; Pearl, 1988), etc. An alternative is that of theSystems Based on Models, where the literature shows thatthese have been successfully applied to highly complex differ-ent domains (Chantler et al., 1998; Console, Rivolin, & Torr-aso, 1991; Torasso, 2001). The model-based reasoning(MBR) is basically an approximation which resolves prob-lems through the explicit incorporation of structural modelssymbolic of the application domain. However, its use willrequire the architecture to provide the tools needed for theconstruction of a knowledge base which contains the saidmodel, as well as those for its maintenance.

As a result, we consider that the use of an explicit behav-

iour model of the application domain is a flexible and direct

way of meeting the requirement proposed as a possiblearchitecture CDTD.

2.3.1. Description of behaviour model

The behaviour model should be expressive enough toallow for management of the temporal dimension in theinferences of a diagnostic process. The use of deep causalmodels together with model-based diagnosis techniqueshas proved its efficiency in the development of intelligentdiagnosis systems (Torasso, 2001). Below we describe theset of characteristics which must be included in the model:

1. Model of abnormal domain behaviour (as opposed tonormal).

2. Causal description (causal relationships).3. Temporal description (temporal constraints).

In highly complex domains, it is evident that an exten-sive description of normal behaviour is impracticable,meaning it is usually more practical to use an abnormalbehaviour model from the domain. Moreover, the modelmust allow the diagnostic process to draw a hypothesisset from a series of findings. Therefore, causality relation-ships are a simple way of acquiring sufficient knowledgefor carrying out said inference through abductive logic(Console & Torasso, 1991). It is also necessary to take intoaccount that the model must describe temporal behaviour.Thus, and depending on how time was defined (see Section2.2), the establishment of temporal constraints, betweenprimitives is a simple and expressive way of describing saidbehaviour. It is even possible to establish constraints with acertain degree of imprecision.

2.3.2. Context in the behaviour model

In practice there is a large set of external factors which,if they are present, could alter expected behaviour. Forinstance, in medical domains, the increase of patient bloodpressure (when it is excessively low) by the correspondingtreatment could describe a contextual factor to be consid-ered when the blood pressure is analyzed. In this case, a risein this signal could be detected. However, this increaseshould not be interpreted as an abnormal manifestation,since this rise is expected.

Model-based reasoning is a field where the analysis ofcontext is relevant in order to solve an intelligent task(der Maas, Hofstede, & de Vries Robbe, 1999; Schreiberet al., 1999). Classical approaches consider that contextualdata correspond to data that have not to be accounted forby diagnosis, unlike findings that do have to be accountedfor (Brusoni et al., 1998). Contextual knowledge is includedin a model using this definition: non-observation atoms canbe added in the antecedent of causal rules defining somediagnostic hypotheses.

However, we consider it possible to model contextualknowledge more completely, and to extend the causal–tem-poral behaviour model with a set of constraints not only atcausal level, but also at temporal level. The context must

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satisfy these constraints in order to later apply some mod-

ification operators which adapt the behaviour of the modelto the contextual situation.

2.4. Diagnosis method and inefficiency

A diagnostic process consists of searching for the causalfactor to explain a set of findings which are discrepant withnormal behaviour. This has been one of the most studiedintelligence tasks in AI (Benjamins, 1993; Peng & Reggia,1991; Portinale et al., 2004). Depending on the type of sys-tem model, the literature provides a wide range of propos-als, from approximations based on superficial knowledgeto solutions based on causal knowledge, via structuralmodels based on qualitative functional components andmodels. However, in this study we propose the approxima-tion based on causal models as a flexible strategy in orderto represent the behaviour in complex domains, and weframe our architecture in the field of model-based diagnos-tics (MBD) (Chantler et al., 1998; Struss, 1997).

The application domains considered in this articledemand that the model take into account the temporaldimension, making both its theoretical development andpractical application much more complex. In (Brusoniet al., 1998), a general characterisation of MBD is made,establishing a classification of systems based on this tech-nique starting from three dimensions: Temporal Phenom-ena, Ontology of Time and Diagnosis Definition. We willfocus on these characteristics to analyse the type of diagno-sis required.

In the previous paragraphs, the models for the followingare described in detail: Temporal representation, possibleontologies and selected alternatives. Depending on this,the Temporal Phenomena dimension is described as Tempo-

ral Diagnosis Phenomena which: ‘‘assumes that some faultsoccurred during time but these faults do not change duringdiagnosis performance’’. In other words, from the point ofview of temporal modelling, once a temporal event occursand a temporal primitive is associated to it (instant or inter-val), this remains constant in the temporal universe. Tem-poral ontology is described by temporal primitives andthe set of metric and qualitative constraints.

In relation to the Diagnosis Definition dimension, thediagnostic task should establish a solution which is capableof explaining the different discrepant findings. Thus, inhighly complex domains, it is reasonable to think of a solu-tion with multiple errors (more than one discrepant find-ing) and various possible explanations. What is more, thesolution must be consistent with the temporal descriptiondescribed by temporal constraints of the model.

2.4.1. Efficiency, completeness, precision and reliability of

the diagnosis

In this work, we consider four objectives that must befollowed by a diagnostic process: completeness, precision,reliability and efficiency. It is obvious that only in very sim-plified domains is it possible to maximise these four factors

at the same time. However, it is possible to obtain an effec-tive diagnostic process by prioritising these objectives.

The result of the diagnostic process is a diagnostic solu-tion, which we define as a set of hypotheses explaining theset of discrepant findings. An elemental problem of thediagnostic process is the excessive proliferation of hypoth-eses explaining those findings. This problem is more acuteif we consider that different temporal instances can be gen-erated from the same hypothesis occurring at differenttimes. However, the temporal diagnostic process requiresthe diagnostic solution to be consistent with the temporal

constraints described in the model. This requirement meansthat only some hypotheses will be feasible, partly solvingthe problem of exploding alternative hypotheses.

In balance, the efficiency will be compromised due to thecomputational cost of checking temporal consistency (seeSection 2.2.1). On the other hand, the administration oftemporal imprecision can also have an impact on efficiency.For this reason, we consider that the diagnostic process inhighly complex domains should be applied off-line, andshould not be considered in real time systems if expecta-tions of completeness and precision are not maintained.An example of off-line application is the use of diagnosticassistants for making daily clinical reports on patients inhospital.

Finally, it is hoped that the degree of reliability of adiagnostic solution is acceptable and to do this the appro-priate mechanisms must be provided to evaluate the reli-ability of that solution. The most common alternativesare: the use of heuristics and the establishment of probabi-listic techniques which estimate the degree of reliability.

However, heuristic approximations depend on a highdegree of control of the specific domain application. More-over, although its inclusion in the systems is simple (Fox,Glasspool, & Bury, 2001), the probabilistic theory is extre-mely restrictive and, depending on the domain, can be dif-ficult to apply, or the knowledge derived from theprobabilistic values difficult to obtain. A feasible alterna-tive is the application of the possibility theory, whichallows for evaluation of the degree of possibility and theneed (see Dubois et al., 1994) for a hypothesis to be thesolution for a model. Thus, there is an estimation of thereliability of the solutions which is useful in final decisionmaking.

3. CDTD generic framework

In previous sections, we have established design require-ments, the different alternatives for resolving requirements,and those which are the most appropriate. Next, we willdescribe the basic elements of a generic framework forthe establishment of temporal diagnostic architectures thatmanage context information in domains of high conceptualcomplexity.

Thus, we consider a framework which must manageknowledge on two levels (see Fig. 1). The first is the mod-elling of domain knowledge where the architecture named

Fig. 1. The generic framework for CDTD architectures.

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Application Domain Level must be applied. The second, thesolution to the problem of temporal diagnosis from thedescription of domain behaviour, is the Problem Level.

In this study we consider domains of high conceptualcomplexity, which means it will be necessary to describein depth the different concepts and relationships of thedomain through a Domain Ontology. However, we considerthat the Model and the management of the Temporal

Dimension should be independent of the ontologicaldescription of the domain, with the advantage of: a reus-able representation for any specific domain and a represen-tation that allows for efficient time management.

We also consider that certain problems in Temporal

Diagnosis can be approached with a resolution methodbased on Causal–Temporal Behaviour Models and using aTemporal Reasoner independent of the domain. Therefore,the causal–temporal behaviour model will use the descrip-tion of the application domain (conceptual and temporaldescription) to establish the abnormal behaviour, whichthe abductive diagnostic task will use to generate a set ofdiagnostic hypotheses as a solution. Likewise, the diagnos-tic task will check the temporal consistency of the infer-ences through a temporal reasoner based on the temporalmodel of the domain.

4. Practical experience: acudes, an architecture for intensive

care units

At this point, we demonstrate the utility of the CDTD

framework by presenting ACUDES, a CDTD-frameworkbased architecture for a specific medical domain. This sec-tion relates our experience to the design and implementa-tion of ACUDES at an Intensive Care Unit, stating howthe conceptual complexity is managed, how the timedimension is managed; and providing a solution to helpin the decision support problem at the ICU.

4.1. The intensive care unit domain

The intensive care unit (ICU) is a medical service thatprovides critical attention to medically recoverablepatients. One of the fundamental characteristics of thisdomain is that patients require a permanent availabilityof monitoring equipment and specialist care. Thus, clini-cians must work in shifts in order to provide a 24 h service.The temporal evolution of patients is permanently recordedand analysed by physicians, who must tackle a wide range

of patient pathological problems (e.g. cardiovascular,renal, infections, neurological, etc.). Therefore, physiciansat ICU have to deal with an overwhelming amount ofinformation provided not only by on-line monitoring, butalso collected from patients’ records (e.g. laboratoryresults, radiology, etc.). Although the management of allthis information is a complex task that these specialistsmust tackle, they are also required to intervene immedi-ately if any patient event occurs, and to provide detailedreports describing the different diagnostic hypotheses theyassume and the subsequent actions (tests, treatments, orrequiring new laboratory analysis).

Due to the different medical areas involved in the ICU,the complexity and the importance of the temporal dimen-sion (implicitly and explicitly analysed in patients’ evolu-tion), we consider that the ICU is a suitable domain toapply an architecture based on the proposed framework.Furthermore, after a deep analysis of the requirements ofwork in a real ICU, the description of an architecture basedon our proposed framework will help to partially solvesome problems related to knowledge management anddecision support.

4.2. ACUDES: Architecture for ICU Decision Support

In this work, we present ACUDES, the Architecture forintensive Care Unit DEcision Support. ACUDES is a spe-cific architecture applied to the ICU (see Fig. 2) that fol-lows the proposed CDTD framework.

This architecture tackles the application domain repre-sentation by the use of a specific ICU ontology and anontology server, that provides the conceptual and termino-logical consistency of the whole architecture. Furthermore,this ontology integrates the concepts of the Health Infor-mation System of the ICU service in order to make an inte-gration possible. Nevertheless, the temporal domain isindependent of the domain representation (reasons in Sec-tion 2.2.3), and modelled by the Fuzzy Temporal Con-straint Network formalism. Therefore, this temporalrepresentation is managed by the Temporal Reasoner(called Fuzzy TIME), which also provides a query languageto check the consistency of the temporal description.

ACUDES core is the temporal model-based diagnosticprocess. This model describes the causal and temporalbehaviour of pathophysiological, evidential, and diagnosticknowledge (called Temporal Behavioural Model, TBM).The critical step of Knowledge Acquisition to storeinstances of the model in the Knowledge Base is directedby an implemented Knowledge Acquisition tool (CATE-

KAT) that helps physicians to carry out these descriptions.The diagnostic process receives as input a set of patientobservations and the clinical contextual knowledge (gath-ered by a Temporal Event Acquisition Tool). Hence, thediagnostic process tries to build a temporal and causal con-sistent diagnosis solution to explain the abnormal patients’observations (based on the TBM) by using both the abu-

ductive diagnosis and the hypotheses discrimination pro-

Health Info. System

ICU Ontology

ONTO SERVERTemp.Info

FuzzyTIME

-FTCN

Clinical Info

CATEKATacq.tool

Temp.EVT.

Acquis

Diag.ViewerTBM

dftp

xml

KNOWLEDGE BASE

DIAGNOSIS

TIME

USERPHYSICIAN

USER

PHYSIC.

TCP

HDP

Fig. 2. ACUDES. Architecture for intensive Care Unit DEcision Support.

998 J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010

cesses. Finally, the Diagnosis Viewer Tool shows the physi-cian a representation of the diagnostic solution in order toprovide the clinicians with some alternatives (or reinforce-ment) of their diagnosis hypotheses.

5. Intensive care unit ontology

The specification of the domain knowledge, by the useof ontologies, provides the proposed architecture with theopportunity of adding the semantic of the domain withinthe formal model. Furthermore, the use of ontologies,unlike adding semantics directly to the formal model, per-mits a low coupling between the model and the domain,which is one of the proposed requisites of CDTD architec-ture (requisite 6, Section 2). This means that differentontologies could be added or removed from the systemwhile keeping the same formal model. In this sense,ACUDES is an architecture based on the ICU ontology.However, the use of other ontologies could be easilyincluded in order to extend the domain.

Another advantage of the domain ontology is the use ofan ontology server (see Fig. 2) that allows the rest of theelements of the architecture to access the ontology of thedomain. This ontology guarantees that all concepts usedin the architecture are semantically consistent.

Ontologies can be described from in a very informal toin a highly descriptive way. The ontology community dis-tinguishes between lightweight and heavyweight ontologies(Gomez-Perez, Fernandez-Lopez, & Corcho, 2004). Light-weight ontologies include concept taxonimies, propertiesand relationships, as distinguished from heavyweightontologies, which entail a deeply detailed description ofterms, and adding axioms, thus needing the use of a formalknowledge representation paradigm (such as DescriptionLogic). It is worth highlighting that the process of buildingontologies, far from being simple, is one of the most rele-vant challenges in Knowledge Engineering (Gomez-Perez

et al., 2004; Taboada, Martınez, & Mira, 2007). Nowadays,there are a wide number of methodologies proposed suchas: METHONTOLOGY, KACTUS approach, or On-ToKnowledge.

The knowledge of clinical domains is often representedby ontologies. These approaches have stated a successfultechnique for modelling in different medical domains.Thus, in this work, we represent ICU domain by a classicalontology approach, modelling relevant entities (concepts),their attributes, and their relationships. Note that, in thisapproach, the domain is strictly represented by taxonomic,partonomic, and abtractional relationships. Hence, it canbe considered a lightweight ontology.

The upper levels of the ICU ontology are shown inFig. 3. The ontology for the ICU domain was built usingProtege 3.0 (Genneari et al., 2002) and the Unified MedicalLanguage (UMLS) (National Library of Medicine, 2003)as standard reference of vocabulary terms. Other ontolo-gies, such as EON ontology (Tu & Musen, 2001) andON9.3 biomedical core ontology (Elkin et al., 2001), havebeen considered for reuse. The development of the domainontology was carried out following the methodology pro-posed in (Taboada et al., 2007).

In practice, ICU ontology of ACUDES has been devel-oped to help the intensive care unit research on the study ofIschaemic Cardiopathy diseases. Fig. 3 shows this particu-lar description of the domain. In this particular portion ofthe domain, the ontology defines 105 different relations and173 different concepts, from high-abstraction level concepts(such as event, symptom, treatment) to lower-abstractionlevel (clinical concepts as ST-segment elevation ortachypnea).

It is worth mentioning that Event concept of the ICUontology is defined by the 4-tuple (m, s,v, t), where m standsfor clinical concepts (pain, st-level, etc.), s and v for anattribute-value pair (location-precordial, intensity-moder-ate, etc.) and t for the corresponding temporal variable rep-

Fig. 3. Intensive care unit ontology in Protege 3.0 and Visualized subset for Ischaemic Cardiopathy Research.

ta bc d

h

π(t)

Fig. 4. Possibility distribution associated to the fuzzy number (a,b,c,d,h).

J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010 999

resenting an instant of time. For instance, in order todescribe the fact that a patient suffers from precordial painat time t, the corresponding event in the ICU ontology isdefined as follows: (pain, location, precordial, t2).

6. Temporal model: fuzzy temporal constraints network

The temporal dimension is modelled by means of theFuzzy Temporal Constraint Network (FTCN) formalism(Marın, Cardenas, Balsa, & Sanchez, 1996). An FTCN is apair N ¼ hT;Li consisting of a finite set of temporal vari-ables, T ¼ fT 0; T 1; . . . ; T ng, and a finite set of binary tem-poral constraints, L ¼ fLij; 0 6 i; j 6 ng defined on thevariables of T. An FTCN can be represented by means ofa directed constraint graph, where nodes represent temporalvariables and arcs represent binary temporal constraints.

Each binary constraint Lij on two temporal variables Ti

and Tj is defined by means of a fuzzy number, that is a con-vex possibility distribution pLij, whose discourse universe isZ, and which restricts the possible values of the time elapsedbetween both temporal variables. In the absence of otherconstraints, the assignments Ti = ti and Tj = tj are possibleif pLijðtj � tiÞ > 0 is satisfied. T0 represents an arbitrary timeorigin. A convex possibility distribution can be representedby means of a 5-tuple fuzzy number (a,b,c,d,h), as shown inFig. 4, which indicates that the event associated to it neces-sarily occurs in interval [a � c,b + d] (referred to as sup-port), but possibly occurs in interval [a,b] (referred to askernel), with a possibility of h.

An n-tuple S ¼ ðt1; . . . ; tnÞ 2 Zn is a r-possible solution ofan FTCN network N if pSN ¼ r, where

pSN ¼ minfpLijðtj � tiÞ; 0 6 i; j 6 ng. The possibility distri-bution pSN defines the fuzzy-set SN of the r-possible solu-tions of the network, with r P 0. An FTCN network N isconsistent if and only if SN is not empty given a previouslyestablished threshold Pth for r.

The inference of unknown relations is carried out byapplying a constraint propagation algorithm. Thus, anew FTCN, equivalent to the original one, is obtained bymeans of constraint propagation. The equivalence propertystates that both networks have the same solution set. How-ever, the new constraints are included in the correspondingconstraints of the original FTCN. Although the new FTCN

contains the same fuzzy-set of solutions, it describes thedifferences between variables in a more precise way. Con-straint propagation removes impossible values from the

Fig. 5. Fuzzy Temporal Constraint Network.

1000 J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010

original constraints. A constraint is minimal when all theimpossible values have been removed. A minimal con-straint contains the smallest degree of imprecision. A net-work whose constraints are all minimal is called aminimal network. The minimal network makes all theimplicit constraints in the network explicit, and always cor-responds to a complete graph. There are some efficientalgorithms to propagate constraints. It may be proved thatwhen the possibility distributions are convex, the wellknown shortest-path algorithm is complete for the FTCN

model, that is, it obtains the minimal constraints for consis-tent networks and it detects inconsistent networks. One ofthe main advantages of obtaining the minimal network isthat it represents the most precise temporal informationconsistent with the temporal information provided (Marınet al., 1996).

Fig. 5 shows the representational capabilities of Fuzzy-TIME by the description of a simple example where eachtemporal entity (t1, t2, t3, t4, t5) represents the instance ofthe occurrence of an event in the temporal world, wherea special temporal entity, t0, describes the temporal origin.

Table 1A Fuzzy Temporal Constraint Network sample after minimization

t3 t4 t2

t3 (0,0,0,0,1) (3,3,0,0,1) (�2,2,1,1,1)t4 (�3,�3,0,0,1) (0,0,0,0,1) (�5,�1,1,1,1)t2 (�2,2,1,1,1) (1,5,1,1,1) (0,0,0,0,1)t5 (�,+,+,+,1) (�,+,+,+,1) (�,+,+,+,1)t1 (�2,0,0,1,1) (1,3,0,1,1) (�4,2,1,2,1)t0 (1,4,0,1,1) (4,7,0,1,1) (1,6,0,2,1)

t0 BEFORE t1.t0 BEFORE t2.t0 BEFORE t3.t0 BEFORE t4.t1 approx 1 s BEFORE t4.t3 approx 3 s BEFORE-EQUAL t4.t2 approx AT t3.t4 approx 5 s AFTER t0.

6.1. Temporal reasoner: FuzzyTIME

In order to interact with this theoretical framework ahigh-level temporal language has been proposed and imple-mented in FuzzyTIME Campos, Carceles, Palma, andMarın, 2002. FuzzyTIME is a general purpose temporalreasoner providing reasoning capabilities on fuzzy tempo-ral constraints between temporal variables which can rep-resent both instants and interval. However, intervals arenot considered in the diagnostic model presented here.FuzzyTIME provides procedures for maintaining and que-rying temporal information at FTCN level, which is givenas high-level temporal sentences.

The key element in FuzzyTIME high-level language isthe temporal relation. A wide range of qualitative andquantitative temporal relations between temporal entitiesis allowed in the high level language. There are five kindsof basic relations (Kautz & Ladkin, 1991; Marın, Barro,Palacios, Ruiz, & Martın, 1994; Meiri, 1996; van Beek,1991; Vila, 1994):

qualitative interval–interval relations (BEFORE,MEETS, STARTS, DURING, FINISHES, OVER-LAPS, EQUAL, and their inverses);qualitative point–interval/interval–point relations(BEFORE, STARTS, DURING, FINISHES andAFTER);qualitative point–point relations (BEFORE, EQUAL,AFTER);metric point–point relations (temporal gap between twotemporal instants in some temporal unit).

All convex disjunctions of basic relations are admittedand are expressed as subsets of each set of basic relations.

For instance, FuzzyTIME language could represent thefollowing fuzzy temporal expressions that physicians use intheir common practice:

ðdehydratation; t 1ÞAPPROX 1 HOUR BEFORE

ðpain; location; precordial; t 2Þ ð1Þ

t5 t1 t0

(�,+,+,+,1) (0,2,1,0,1) (�4,�1,1,0,1)(�,+,+,+,1) (�3,�1,1,0,1) (�7,�4,1,0,1)(�,+,+,+,1) (�2,4,2,1,1) (�6,�1,2,0,1)(0,0,0,0,1) (�,+,+,+,1) (�,+,+,+,1)(�,+,+,+,1) (0,0,0,0,1) (�6,�1,1,0,1)(�,+,+,+,1) (1,6,0,1,1) (0,0,0,0,1)

J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010 1001

The previous equation represents the temporal evolution ofa patient suffering from dehydration (formally this manifes-tation is internally represented as (dehydration, pres-ence, true, t1)) approximately 1 h before a precordial pain.

Table 1 shows the representation of these temporal con-straints with fuzziness from the previous example (Fig. 5).In this table, it can be seen that FuzzyTIME representstemporal constraints using fuzzy number notation:ha,b,c,d,hi, where + and � represent +1 and �1, respec-tively. Note that some of the constraints are not explicitlydescribed (e.g. between t1 and t2, or t2 and t3) but presentin the temporal constraint network. These constraints areinferred in the network minimization process describedpreviously.

7. Temporal behavioural model description

The CDTD framework shows the use of a behaviouralmodel. Therefore, ACUDES provides a temporal behav-ioural model based on our previous work on fuzzy theoryapproach for temporal model-based diagnosis (Palma,Juarez, Campos, & Marın, 2006). The temporal behav-ioural model (TBM) proposed is based on an abnormalbehavioural model in which only the causal and temporalrelations between diseases and their abnormal findingsare represented. These relations are defined by Diagnostic

Fuzzy Temporal Patterns (DFTPs). Furthermore, eachDFTP includes knowledge about how the context affectsthe temporal evolution of the disease – referred to as Tem-poral Context of the Pattern – and information about thetemporal relations between contextual elements. EachDFTP is therefore composed by:

1. A main hypothesis, H (or �h) which represents the diseasbeing described and a temporal variable t�h indicating itsapproximate time appearance.

2. A set of implied abnormal manifestations, IM, whichrepresents the set of events describing disease evolution.Each abnormal manifestation is defined by the tupleim = (m, s,V, t) where, in contrast to the definition ofevents, V stands for the set of event values admittedfor the abnormal manifestation.

3. A set of implied hypotheses, IH, which specifies the setof diseases caused by the diseases H under description.By means of these causal links, from H to elements inIH, the temporal behavioural model constitutes a causalnetwork of diseases. This type of causal relation allowsus to model existing causal relations between the etiolog-ical and pathophysiological diagnoses.

4. A consistent FCTN, Rdftp ¼ hTdftp;Ldftpi, whose tem-poral variables, Tdftp, are those associated to the mainhypothesis, implied manifestations and implied hypoth-eses. The set Ldftp represents the set of fuzzy binary tem-poral constraints between temporal variables,Ldftp ¼ fCðti; tjÞ; ti; tj 2Tdftpg. These constraints areobtained from temporal information provided by medi-

cal experts. They are stored as a set of metric point topoint relations between temporal variables (see Section6.1).

5. S1 and S2 define the necessity degree associations of thecausal relations of the patterns, based on PossibilityTheory (Dubois et al., 1994). S1 defines the necessitydegree association of the causal relation between �h andits implied manifestations and hypotheses, while S2 theimplied manifestation between each implied manifesta-tion and hypothesis with their �h.

DFTPs allow us to capture all kinds of causal knowl-edge which can be extracted from medical domains (Con-sole & Torasso, 1991): pathophysiological knowledge(causal relations between pathophysiological states), evi-dential causal knowledge (causal relations between externalmanifestations and either pathophysiological or etiologicalstates) and diagnostic knowledge (relations between patho-physiological states and etiological states).

7.1. DFTPs in ICU

We will now show two of the patterns included in theACUDES architecture for the study of Ischaemic Cardiop-athy in the ICU.

The Tissue Hypoperfusion pattern (TH) (see Fig. 6)establishes the possible outcomes, assuming the aforemen-tioned etiological diagnosis, and also describes the tempo-ral fuzzy constraints between those elements. In thistemporal pattern three abnormal manifestations are con-sidered: Paleness, ST Re-elevation and Slow Capillary Fill-ing, as is the partial order between those elements: Palenessand Capillary Filling approximately at the same time, butthe Capillary filling approximately 1 min after the TH, etc.

The Distributive Shock (DS) pattern (see Fig. 7) estab-lishes a temporal behaviour pattern similar to the previousone. However, in this one the definition of the DFTP alsoincludes an Implicated Hypothesis. This means that one ofthe consequences of the etiological diagnosis of the DS isassuming the diagnostic hypothesis of the presence of aTH.

In both patterns, the functions S1 and S2 indicate thedegree of necessity associated with each causal relation.For example, given S1 in the DS pattern, if we assumethe diagnostic hypothesis DS, it is comparatively more nec-essary to justify the presence of TH than the presence ofHypotension. Likewise S2 shows us that the presence ofTH will imply a DS with greater clarity than if only Hypo-tension is present.

7.2. Temporal context of the pattern

The use of contextual information is especially impor-tant in medical domains, since it is very difficult to explaina real-world patient evolution using theoretical descrip-tions of diseases. This is mainly due to the presence of con-textual factors that affect theoretical patient evolution. For

Main Hypothesis: (Tissue Hypoperfusion, )

Implied Manifestations

– (Paleness,Presence,True, )

– (ST Re-elevation,Presence,True, )

– (Capillar Feeling,Velocity,Slow, )

Implied Hypotheses

Temporal Constraints

– Explicit Temporal Constraints

approx AT

approx AT

approx 1 min after

– Default Temporal Constraints

before

before

before

Fig. 6. Description of Tissue Hypoperfusion temporal pattern.

Main Hypothesis: (Distributive Shock, )

Implied Manifestations

– (Hypothension,Variation,Moderate, )

Implied Hypotheses

– ( Tissue Hypoperfusion, )

Temporal Constraints

– Explicit Temporal Constraints

approx AT

approx 100 secs after

– Default Temporal Constraints

before

before

Fig. 7. Description of Distributive Shock temporal pattern.

1002 J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010

example, in an intensive care unit (ICU), it is very commonto increase a patient’s blood pressure (when it is excessivelylow) by means of the corresponding therapy. If, in this con-text, a rise in blood pressure is detected, it should not beconsidered as an abnormal manifestation, since the rise isexpected. In our model, an event associated to a manifesta-tion in a given hypothesis can play the role of a contextualfactor in another hypothesis.

Therefore, it is necessary to provide a representationalframework to model how contextual factors affect DFTP’sdefinitions. In our proposal, this is done by means of one ormore temporal contexts, TC, associated to a given DFTP

(Palma & Marın, 2002). Temporal contexts specify the dif-ferent modifications that have to be accomplished in aDFTP definition if a certain context is present. To definea temporal context the following elements are necessary:

• A set of atemporal concepts, Ai, describing informationsuch as patient’s demographics (age, sex, etc.), risk fac-

tors (smoker, diabetes, etc.) and all the relevant informa-tion that has no temporal dimension.

• A set of temporal concepts, Ti, mainly related to theadministration of drugs.

• A consistent FTCN, Rcti , that specifies the temporal con-

straints between the hypothesis H and the temporal con-cepts TCi.

• MFi = {mf1, . . . ,mfm} is a set of modification functions(mfi) that describes how the context changes the basicDFTP definition. These functions create, delete andmodify elements of the IM, IH sets, and the Rdftp

network:– modify valuesðDFTP ; im; V new

m Þ substitutes the set Vm

for the allowed values of the implied manifestationim.

– modify necessityðDFTP ;mh; Snew1 ; Snew

2 Þ modifies thenecessity degree associated to causal implication.

– add implicationðDFTP ;mhnew; Snew1 ; Snew

2 ;Cnewðt�h; tmhÞÞadds a new implied manifestation or implied hypoth-esis mh to DFTP. It also assigns the necessity degreevalues of the corresponding causal relationships anda temporal constraint between the added manifesta-tion or hypothesis and the pattern main hypothesis.

– remove_implication(DFTP,mh) deletes the impliedmanifestation or implied hypothesis mh, its necessitydegree value from the DFTP and the associated tem-poral variable and constraints.

J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010 1003

For example, let us suppose that a patient has sufferedan Acute Myocardial Infarction (AMI) and, approximately1 h earlier, has suffered dehydration. Let us also supposethat 45 min after the infarction (on arrival at the ICU),he or she has been administered Solinitrine (nitro-glycer-ine). This context causes a bradycardia approximately fiveminutes after AMI – a finding that in normal conditions isnot expected. This context can be modelled by means of thetemporal context, associated to the temporal pattern.

7.3. TBM knowledge acquisition

The medical domain, in particular ICUs, is hard to clas-sify and its terminology is hard to interpret. In some cases,the definition of terms may leave space for incorrect inter-pretation. Hence, the knowledge acquisition lies in anontology. It allows a consistent use of the same medicalterms. Thus, using ontologies, KB acquisition could beviewed as a process of enlarging a domain ontology withspecific knowledge of a particular domain. In some othercases, terms are equivalent but not considered in the ontol-ogy. We suggest the use of dictionaries of synonyms andthesaurus.

Another point that we dealt with was the incompletenessof knowledge acquisition. For example, the physician coulddescribe some related clinical signs. However, the physiciandoes not specify how these signs must be interpreted whenthey are present in the patient. This insufficient informationmust be acquired from some other experts.

Therefore, we propose the use of a knowledge acquisi-tion (KA) tool to assist the physicians in order to help inthe acquisition of this knowledge. To this end, we imple-

Fig. 8. CATEKAT Interface and ‘‘Tissue

mented the web based KA user interface called CATEKAT,the CAusal and TEmporal Knowledge AcquisitionTool.

There are several requirements that characterise itsdesign and implementation:

• A multi-user environment.• Role-awareness, i.e., the management of different roles

such as expert or knowledge engineer.• Avoiding inconsistencies in temporal pattern edition by

locking a pattern when a user is working on it.• Providing an effective cooperative work platform.• Allowing the definition of projects related to different

domains.• Browsing and querying capabilities.

The final goal of this tool is the knowledge acquisition ofdiagnosis patterns, and their storage in the KnowledgeBase (see Fig. 2). As described in Section 7, the proposedformal model represents knowledge by the use of FuzzyTemporal Constraint Patterns. In the particular case ofICU domain, each of these patterns represents the causaland temporal behaviour of the evolution of a single disease(pathology). Note that in Fig. 8 the CATEKAT fields (onthe left) and the graphical representation of a Tissue Hyp-

operfusion pattern (on the right) can be seen.One of the main problems in the configuration of the

DFTPs, that conforms the TMB, lies in the expert’s capac-ity to define each DFTP element. Our experience in medicaldomains reveals that the definition of S1 and S2 functions(see definition on Section 7) is one of the most complexpoints. Accordingly, useful information can be extractedfrom Evidence Based Medicine (EBM) literature (Sackett,

Hypoperfusion’’ Diagnosis Pattern.

1004 J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010

Richardson, Rosenberg, & Haynes, 1997). In EBM, given adisease �h and a manifestation or implied hypothesis mhk,the causal relationship between them can be quantified bymeans of two values:

• Sensitivity, which measures the proportion of allpatients suffering the disease who present themanifestation.

• Specificity, which measures the proportion of allpatients not suffering from that disease who do not pres-ent the manifestation.

Both values can be represented by conditional probabil-ities of the DFTP elements (⁄main hypothesis, and mhk

implied manifestation or hypothesis) as follows: Sensitiv-

ity = P(mhk/�h) and Specificity ¼ Pð:mhk=:�hÞ. In (Palmaet al., 2006), it is demonstrated that S1 and S2 necessityfunctions can be determined from sensitivity and specificitymeasures.

8. The diagnosis task of acudes

The main objective of the diagnosis task is the buildingof a diagnosis explanation from the set of abnormalobservables, their temporal references, and the content ofthe Knowledge Base (see Fig. 2). The diagnostic processis based on an extension of the classical method Cover

and Differentiate (Eshelman, 1988) which has been adaptedto deal with temporal component and to a Possibility The-ory (Dubois et al., 1994) based hypotheses evaluation andthe TBM. The main characteristics of the proposed Diag-nostic Process are:

• Multiple hypotheses in the solution. Several hypothesesmay be found in a solution, representing alternativesor complementary explanation of the observed behav-iour. Furthermore, different instances of the samehypothesis (the same hypothesis located at different timeinstants) are possible in a solution. This makes it possi-ble to cope with General Temporal Diagnosis (Brusoniet al., 1998) in which diagnosis is defined as an assign-ment of faults over time that explains both the temporaland time-varying behaviour of the temporal evolution.

• Parsimonious covering based diagnosis. The proposedprocess explains those events considered abnormalthrough parsimonious covering. New hypotheses areincluded in the final explanation to explain a givenevent, if and only if there are no alternative instantiatedhypotheses which can explain that event.

• Acceptable efficiency of the process. Despite the fact thatthe algorithm presents an exponential time execution,the algorithm includes some heuristics (subsumption

and temporal shifting) to improve efficiency.

Essentially, our diagnostic proposal is composed of twophases: first, a causal network (temporally consistent) isbuilt using an abductive strategy to explain the set of

events. This phase is carried out by the Temporal CoveringProcess (TCP). Secondly, some hypotheses are prunedfrom this causal network through a Hypotheses Discrimi-nation Process (HDP).

In our experience of the ICU domain, we have dealtwith two problems concerning the clinical decision support:the gathering of temporal medical information (the inputof the diagnostic process); and the representation of thediagnostic outcome. Therefore, to provide the decision sup-port in ACUDES, two additional phases are included inthe process. Firstly, before the TCP, an evidence acquisi-tion task must be carried out to obtain the set of temporalevents that the TCP requires as input. Secondly, after theHDP, a graphical visualization of the final solution isshown to provide the physicians with a useful interaction.

8.1. Evidence acquisition

Events are described as the input information of thediagnostic process of the architecture. The particular caseof the intensive care unit, and most medical domains, sug-gests that these events are the evidence (findings or observ-ables), that the physician gathers from a patient during his/her stay at the ICU. Symptoms that match abnormalevents are considered evidence of the diagnosis. However,it is significant in order to get a high quality diagnosis.Hence we must consider extending the event definition tothose other findings that confirm the correct behaviour ofthe human body and those relevant external factors thatcould affect the process.

In ACUDES, there are three kinds of events: abnormal,normal and contextual events. Abnormal events are thosethat are involved in the diagnostic process to obtain diag-nostic hypotheses, i.e. describe disfunctions of the humanbody. Normal events are those that confirm the correctbehaviour. Finally, contextual events describe external fac-tors that could affect the whole evaluation of the diagnosticprocess, e.g., high levels of sugar in urine are expected if thepatient suffers from diabetes.

As described in the ICU Ontology, each event inACUDES is represented by a 4-tuple evt = hm,a,v, ti,where m is the manifestation that describes the event, a isthe attribute that is assigned to the manifestation, v is thevalue associated to the manifestation, and t is the temporalvariable of the event in the clinical history, e.g., hsyn-

cope,presence,yes, 3:30 ami. Each component of the eventis also strictly defined in the ICU ontology (see Section5). Hence, the axioms of the ontology restrict the possibleassignment of the m, a, and v event components (Fig. 9).

Thanks to the fact that the ICU-ontology is shared inthe whole system, CATEKAT uses it and, therefore, themedical terminology introduced is consistent with theknowledge formalized by the TBM. In our implementation,both tools use the ontology server of the architecture.

The main goal of the diagnostic process is to build anexplanation of the patients’ evolution. The design of graph-ical and interactive tools for decision support has emerged

Fig. 9. Evidence Acquisition Tool and ICU-ontology integration.

J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010 1005

as an important weapon for the success of Knowledge BaseSystems.

In our proposal, the aim of the Evidence Acquisition

Tool is to provide physicians with the data collection ofclinical histories, allowing the system to return the diagno-sis outcome. This tool has been designed to facilitate a

Fig. 10. Temporal line

visual and interactive acquisition of patients’ findings, tak-ing into account the temporal dimension, which is criticalin ICU domains (in Fig. 10 on the left).

Time is one of the components of the event (evt) and thephysician can type its value for each gathered event. How-ever, temporal evolution of diseases is frequently monitored

ar representation.

1006 J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010

during a day while a patient is in the ICU. Thus, it is inev-itable that physicians manage a high number of documentsreferring to each day of evolution. Hence, if they need tocompare the temporal evolution of a given clinical parame-ter, they require the documentation from all the days.

To deal with this problem, we propose a chronologicallinear representation of events, which are allocated depend-ing on their temporal component. Note that, in Fig. 10,events are classified in three chronological lines: abnormalevents, normal events, and contextual events. This simplebut useful representation provides physicians with a com-plete overview of temporal evolution of diseases for a givenpatient.

8.2. Temporal covering process: TCP

The creation of the diagnosis explanation is the first stepin the proposed diagnosis task. The Temporal Covering

Process (TCP) tackles this problem by trying to explainall the events abductively, maintaining the temporal consis-tency of the temporal information asserted in the solution.The input information is provided by the Evidence Acqui-sition Tool (Section 8.1) which is the set of all input events(EVT), the context information, and their temporal infor-mation. The final explanation will explain those events inEVT considered abnormal by the TBM, taking intoaccount the current temporal context.

In addition to the input events, the generation of thecomplete causal network implies creating new events thatare associated to each hypothesis included in the explana-tion, and this must be explained in the same way as theabnormal input events (i.e., those events in EVT markedas events to be explained). These new events make it possi-ble for the TCP to extend the explanation upwards throughthe causal dimension until abducibles are reached. Thealgorithm finishes when it is not possible to find a higherlevel hypothesis that can explain any of the EVT events.These hypotheses conform to the set of abducibles of thesolution. The TCP can be described as follows:

• Firstly, an event evti is selected from those elementsfrom EVT which are marked as events to be explained.This event can represent either an observation or ahypothesis.

• Secondly, TCP tries to find all possible temporal pat-terns ðDÞ from the Knowledge Base of the architecturethat can explain the event evti.

• Finally, the algorithm tries to explain evti for each tem-poral pattern in D, and adds both the event and the tem-poral patterns into the solution as follows:– Firstly, TCP tries to contextualise the temporal pat-

tern. If contextualization is possible, a new instanceof the temporal pattern is created and included inthe explanation, and the corresponding temporalconstraints are asserted in Rexp. Then, evti is associ-ated to the new temporal pattern instance and TCP

proceeds with a new event. As said before, a new

event associated to the new instance is created andmarked as an event to be explained.

– If the contextualization is not possible, the diagnosticprocess tries to subsume the event in any of thealready instantiated patterns that exist in the partialsolution. The aim of the subsumption is to avoid anexcessive proliferation of temporally nearby hypothe-ses, by including the event in one of the alreadyinstantiated patterns.

– When the subsumption is not possible, the temporal

shifting function is applied. This function proposesincluding a new instance of the same pattern andassociating the event to it. If we reconsider the failedsubsumption, we will notice that only a few of theassociated events subsumed into it do not allow thenew subsumption.

– Only if all the previous processes fail to explain evti,TCP will generate a new instance of the temporal pat-tern in the solution. Of course, a new event associatedto the new instance is created and marked as an eventto be explained.

8.3. Hypothesis discrimination process: HDP

The Temporal Covering Process (TCP), described above(Section 8.2), can generate hypotheses which are not suffi-ciently supplied by the evidence or which may be inconsis-tent with some of the observations not explained in theprevious process. In order to eliminate such hypotheses itis necessary to establish some measure that will indicatethe degree of credibility associated with a hypothesis. Theapproach we propose is based on Possibility Theory(Dubois & Prade, 1988), by means of which we can calcu-late the degree of possibility and of necessity associated toeach hypothesis. Such degrees allow the hypothesis dis-crimination process (HDP) to eliminate from the finalexplanation all those hypotheses which should not beincluded therein.

The HDP starts with the evaluation of the possibilityand necessity degrees of the abducibles generated by theTCP process (top level hypotheses). This evaluation trig-gers the evaluation downwards through the causal networkof all the hypotheses. The calculus of possibility/necessityof each node of the causal network is established by arecursive function. The architecture uses the S1 and S2

functions that describes possibility and necessity values.These values are determined by the maximum/minimumof the possibility/necessity degrees associated to the differ-ent implied manifestations and hypotheses.

8.4. Example of clinical diagnosis case

We will now present the TCP of a simplified clinical caseof a patient admitted to the ICU. The knowledge base ofACUDES (for greater understanding) is made up of onlytwo temporal diagnostic patterns: Tissue Hypoperfusion

Table 2Example of temporal events

Manifestation Attribute Value Time

Hypotension Variation Sudden 00 h 33 m 35 sCapillar filling Velocity Slow 00 h 36 m 55 sPaleness Presence True 00 h 37 m 00 sST re-elevation Presence True 00 h 38 m 40 s

J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010 1007

(TH) and Distributive Shock (both described in Figs. 6 and7).

The aforementioned patient was admitted to the ICU ata specific time (taken as temporal origin). Within approxi-mately half an hour of his admission the clinical evidencedescribed in Table 2 was detected.

The TCP will try to explain, one by one, the differentEvents starting with the knowledge base of ACUDESaccording to the process described in Section 8.2. A sum-mary of the process can be seen in Fig. 11.

In step 1, Paleness event is explained using the patternTH as it fulfils both conditions: (1) Paleness is an impliedmanifestation of the definition of the pattern (see Fig. 6),and (2) the temporal constraints of the pattern are fulfilled.Furthermore, Paleness ceases to be an event to beexplained and the hypothesis TH is included as an eventwhich must be explained. In step 2, the event Capillar Fill-

ing is explained subsumed by the same application of thepattern which previously explained Paleness, given that itmeets the temporal and causal constraints of the pattern,thereby avoiding an explosion of the hypothesis.

In step 3, the TCP tries to subsume ST re-elevation in theapplication of pattern TH. However, on this occasion it isnot possible to meet the temporal constraints described(step 3.1). Therefore, in line with the process TCP a tempo-ral shifting of the TH pattern is carried out in order to

Fig. 11. TCP of a Distr

explain the ST re-elevation event (step 3.2), thereby creat-ing a second application of the hypothesis TH (denomi-nated TH ). These two instances are considered as possiblealternatives, and are considered as the same hypothesis attwo different times.

In step 4, the rest of the process is resumed until arrivingat the final diagnostic hypothesis. First the event Hypoten-

sion is explained through the Distributive Shock pattern.Secondly, event TH (included as a hypothesis event to beexplained) is subsumed by the Distributive Shock pattern.This occurs as, by definition of the pattern (see Fig. 7),TH is a hypothesis involved in the pattern. Thirdly, as inTH, TH is subsumed in the same pattern. Finally thehypothesis Distributive Shock, included as a new event,has to be explained. As no pattern exists in the ACUDESknowledge base, it is considered to be the diagnostic expla-nation of the process.

8.5. Diagnosis solution representation

In decision support systems, the representation of thediagnostic solution is a key question, not only from a prac-tical point of view, but also to provide an understable diag-nosis output. In model-based systems, these outputs mustinclude parts of the underlying model. In particular, thediagnosis output described in ACUDES is composed by:a causal network, a consistent temporal network, and thoseparts of the KB which are involved in the creation of thediagnostic inference. Note that an over ambitious tendencyto show a graphical representation might be annoying.Thus, we opted for a partial representation of the diagnosisoutput, including the causal network as the main represen-tation, but also certain belief values associated to the nodesof the causal network.

ibutive Shock case.

Fig. 12. Diagnosis Navigator Tool and Diagnosis Output.

1008 J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010

Fig. 12 (on the right) shows the true-life case of an AcuteMyocardial Infarction diagnosis. On the right, the causal(line arrow) and temporal constraint (dash arrow) net-works are represented. Note that the Diagnosis NavigatorTool represents the causal network but also shows somenumerical values. These numbers are the specificity andsensibility values of each hypothesis, important parametersthat, in Evidence-Based Medicine, additionally helps physi-cians to evaluate the diagnostic results (Sackett et al.,1997).

The Diagnosis Navigator (in Fig. 12 on the left) not onlyshows an understable output of the diagnosis process, butalso explains this diagnostic solution. This explanation ismade by describing each decision that the diagnosis taskhas reached through the process of causal network con-struction within the reasoning process. This functionalityallows the physician to understand the behaviour of thediagnosis process. Thus, the expert could criticize it andsuggest new modifications by means of the CATEKATtool.

9. Discussion

This paper proposes a set of basic requirements fordesigning knowledge based architectural systems thatresolve the problem of temporal diagnostics for highlycomplex conceptual environments, known as CDTD archi-tectures. The proposed requirements are analysed in thearticle, concluding with the proposal of a generic frame-work for CDTD architectures. Finally, the applicabilityof the framework for CDTD s is shown by the presentationof ACUDES, an architecture based on the principles pro-posed for aiding diagnostic decision making in an ICU.

This article also proposes a set of a priori criteria whichare summarised in the study of: domain modelling andtime, description of context-dependent behaviour and thestudy of the temporal diagnostic process under certain effi-ciency conditions. However, this work is not focused on

describing a specific technique for solving one of theseproblems (as on their own they are a branch of AI), butrather on proposing a complete solution for solving theproblems of modelling in complex domains, knowledgeacquisition, and simplification of the problem of develop-ment in these systems.

Another relevant approach in the literature that dealswith the design of flexible intelligent systems is the use ofrapid application development framework (or RADs).For instance, in (Lertpalangsunti & Chan, 1998) an envi-ronment for constructing hybrid intelligent forecasting sys-tems is presented. These approaches need the use ofmodules that embed the logical components of the system(e.g. algorithms, heuristics, etc.) that must be arranged dur-ing the development phase. This technique has achievedimportant results in the fast development of flexible intelli-gent systems. However, when the architecture of a system isdefined and developed using these frameworks, despite thesystem being quickly implemented for the given domain,there is a lack of adaptation to different domains if thereis not a step back to the development phase. Furthermore,the fuzzy temporal diagnosis is a problem which is notoften considered in these sort of approaches.

One of the main characteristics of the CDTD architec-ture framework is the use of ontologies in order to modelthe domain knowledge. The complexity inherent to somedomain knowledge has increased the problems associatedwith expert systems. Some of these problems have beensolved partially thanks to the prolific research field ofOntological Engineering (Gomez-Perez et al., 2004). Theuse of domain ontologies allows our proposed architectureto be general in terms of the specific domain.

Another important advantage of this framework is itsawareness of the temporal dimension. In this CDTDsarchitecture, we suggest the use of an explicit representa-tion of time using fuzzy temporal constraints (Allen,1983; Marın et al., 1996). To this end, a fuzzy temporal rea-soner for general purpose (Campos et al., 2002) is used to

J.M. Juarez et al. / Expert Systems with Applications 35 (2008) 991–1010 1009

maintain the temporal consistency of the wholearchitecture.

In the design of Architectures Based on Models, theselection of a knowledge model is a critical factor. At thispoint, an important question that we must consider is thedegree of dependency of the model and the domain thatis modelled. On the one hand, a weak dependence facili-tates the design of a generic model that can be reused inother domains. On the other hand, the approach of ahighly dependent model on the domain provides an easydesign of knowledge acquisition tools. We propose theuse of a temporal–causal model, and try to find an interme-diate representation between both criteria.

The application of our proposal in medical domains,ACUDES, shows the pros and cons of this general archi-tecture. The intensive care unit is a challenging domainwhere its complexity is one of the main problems to dealwith (Langenberg, 1996; Seyfang, Miksch, & Marcos,2002; Wachter, Markewitz, Rose, & Westenskow, 2005).

ACUDES is based on our previous research on the rep-resentation of temporal behavioural models TBM (for fur-ther reading Palma et al., 2006). The model used in thearchitecture deals with both generic model and easy acqui-sition thanks to the aforementioned intermediate represen-tation criterion.

Other practical problems, such as gathering input tem-poral events in clinical domains and the visualization ofthe diagnosis solutions, have been tackled by the develop-ment of the corresponding tools.

There are also some limitations to our proposal.ACUDES must deal with different levels of knowledgeacquisition, which is a classical bottleneck of knowledge-based systems and in practice implies a high-cost develop-ment process. Despite the advantages of modelling fuzzytemporal imprecision, some works (Fox et al., 2001) pro-pose simpler strategies than a fuzzy approach in medicaldecision support systems. However, the use of adequateknowledge acquisition tools, like CATEKAT, provides asuitable way of solving this problem by isolating medicalstaff from internal representation of knowledge. Anotherlimitation is the efficiency of the diagnosis task. Despitethe fact it is not considered an online diagnosis, the diag-nostic process is a generic proposal and for medicaldomains some optimisation can be addressed.

Our future works will be focused on the simplification ofthe KA process by including case-based reasoning tech-niques on the diagnostic process and the simplification ofthe behavioural model.

Acknowledgements

We would like to thanks Dr. Palacios from the ICU ser-vice at Getafe Hospital (Madrid) for his indispensable col-laboration and comments. This work was supported by theSpanish MEC under the FPU National Plan (Grant Ref.AP2003-4476) and the National Project TIN2006-15460-C04-01.

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