Knowledge Management in Ubiquitous Healthcare

Bo Hu, Liang Xiao, David DupplawSchool of Electronics and Computer Science

University of SouthamptonSouthampton S017 1BJ

Email: {bh, lx, dpd}@ecs.soton.ac.uk

Abstract-Ubiquitous healthcare (UbiCare) refers to an emerg-ing paradigm that fundamentally changes the way one accesseshealthcare services. UbiCare presents itself as both an oppor-tunity and a challenge to the traditional semantics-enrichedknowledge management (KM). It provides a good testbed uponwhich traditional KM methodology can be validated and verified.It is a challenge in the sense that many assumptions made withrespect to the traditional KM models are no longer applicable.In this paper, we review our recent work in the Breast Cancerdomain and reflect on what can and should have been achievedwhen semantic web technologies are applied to healthcare. Wethen speculate on the possible impact of the new healthcareparadigm on KM.

1. INTRODUCTION

Ubiquitous healthcare (UbiCare) refers to an emergingparadigm that is gradually reshaping the old "patient-seeing-doctor" scenario into one in which health services and in-formation (e.g. clinical advices and warning, patient statusmonitoring and feedback, etc.) become just "one-click" away.Its benefits are evident as patients will enjoy more convenientand smarter healthcare services than ever before; clinicianswill be saved from many tedious routine jobs to focus moreon critical tasks; and administrative cost might be signifi-cantly reduced. At the heart of this envisioned "anywhereand anytime" healthcare paradigm is empowering miniaturecomputing devices with the ability to acquire and understanddata in a real-time fashion and within distributed environments,identify and locate other devices to work together by formingan ad-hoc network, and communicate with end-users in ahuman friendly fashion. While the advance in technology hasprepared us with essential hardware (e.g. sensors, HCI devices,etc.), we are facing unprecedented challenges that are posedby the vast amount of data and the distributed nature of thenew approach towards healthcare.

1.1 ChallengesandMotivations

Challenges faced by UbiCare are confronted also by otherapplications aiming to provide "anywhere-anytime" smart ser-vices, e.g. e-Commerce, e-Learning, etc. Some characteristicsunique to healthcare, however, make it different from thoseapparently similar ones. In this paper, we focus on the knowl-edge management (KM) issues in the following three aspects:

Distributed EHR ManagementA direct outcome of the new healthcare paradigm is therapidly growing quantity of data. Monitoring patients onan anywhere and anytime basis produces a large amount

1-4244-0971-3/07/$25.00 C)2007 IEEE.

of electronic health record (EHR). Methods are neededto store, transport and manipulate EHRs efficiently andsecurely without violating national and international reg-ulations on data privacy. Data heterogeneity is anotherconcern in distributed EHR management due to the lackof standard data format commonly agreed by equipmentmanufacturers, unsatisfying standardisation, etc.

. Application OntologiesIn medicine, ontology is by no means a new researchtopic. So far, many "heavy-weight" ontologies have beenproposed and developed, e.g. FMA [1], SNOMED1, etc.However, the sheer size of such ontologies prevents themfrom being successfully applied in a ubiquitous environ-ment due to the limited computing power of miniaturedevices. "Light-weight" and application-specific ontolo-gies are, therefore, sought after by many developers.

. Service CompositionThe data-centric nature of UbiCare implies that knowl-edge services play an important role. Collaborating"smartly" with neighbouring equipment builds the foun-dation for efficient healthcare environments while com-municating with mutual-understanding ensures the rightservices are delivered.

We investigate how to address that above challenges in thecontext of Semantic Web initiative [2]. We draw the conclu-sions based on our experience from the UK EPSRC-fundedMIAKT 2 project aiming to provide technical support to breastcancer diagnosis and patient management. It, however, shouldbe noted that the views and conclusions contained herein arethose of the authors.

2. MIAKTPROJECT

Breast cancer is the most common cancer for women in theUK [3]. Diagnosis of breast cancer normally involves multi-disciplinary meetings with experts from different medicalbackgrounds, e.g. radiologists, surgeons, oncologists, histolo-gists and other clinical staff. Images are presented as evidenceson the meeting with descriptions and diagnosis. As with otherdisciplines, we expect considerable variability among expertsor even between two interpretations carried out by the sameexpert. Hence, it provides the motivation to make uniform thevocabulary used in the breast cancer screening and diagnosis

1 http://www.snomed.org/2http:llwww.aktors.org/miakt

process. A typical scenario of breast cancer assessment processstarts with a report from routine X-ray mammographic exami-nations or self-reported abnormal symptoms followed by an X-ray mammographic examination. Therefore, a very good start-ing point of formally representing the breast screening processwould be the Xray mammography. Meanwhile, studies showthat, when a definitely benign diagnosis cannot be made, thebreast magnetic resonance imaging (MRI) is normally treatedas an expensive complementary method to the breast X-rayimaging to increase the diagnostic confidence [4]. The MedicalImage and Advanced Knowledge Technology (MIAKT) aimedto accommodates the above needs and challenges in diagnos-ing and treating breast cancer patients. Although MIAKT isnot, strictly speaking, a ubiquitous healthcare application, itshares common characteristics with real ubiquitous applica-tions and our experience in developing and deploying such ansystem might be beneficiary to real ubiquitous framework.

2.1 MIAKTOntology

The MIAKT Domain Ontology, BCIO, captures the exper-tise and information that are necessary to facilitate diagno-sis and prognosis of different types of breast cancers andmanagement issues of breast cancer patients. Such knowledgeis elicited and formalised with unambiguous and explicitdefinitions. Hence, a carefully design ontology can providethe ground on which consensuses can be constructed. Thisis particularly important for a distributed environment suchas the one envisioned by MIAKT, since it is not rare that insuch an environment, a meaningful conclusion has to be drawnupon suggestions and observations by experts with differentbackground knowledge using different terminology. What ag-gravates the situation is that such experts might reside atdifferent physical sites and speak different natural languages.The domain ontology provides a vehicle for necessary domainknowledge that allow them to establish mutual understandingswith only a moderate amount of effort.The same claim can be made with respect to "knowl-

edgable" services which are arguably the replacement for hu-mans at some situations in MIAKT. "Knowledgable" servicesare expected to take over certain routine work from humanexperts and thus they should be made juxtaposed against thelatter in understanding the vocabulary/terminology in braintumour domain at least the part that they are bound to takefull responsibility of. That is when we give instructions tosoftware agents and when software agents communicate witheach other, the domain ontology would be the language spokenby all participants for conveying the intended messages. Ex-amples of such conversations are "retrieve cases of all patientsunder age 25" and "fetch a malignant case from Hospital A"where underlined words are concepts from the ontology.Among others, BCIo has dedicated models for specifying

various types of breast cancers and multiple-layer abstractionfrom low-level image features, mid-level morphologic descrip-tors to high-level clinical concepts. It also provides clinicianswith the basic means to compile their diagnosis and patientmanagement conclusions [5].

2.2 TheMIAKTSystem

The MIAKT project aims to employ the latest developmentin breast cancer diagnosing methodologies and treatment.While the architectural (see Figure 1) details do not haveimmediate relevance to the medical application we apply itto, an exposition of the principles that have guided its designindicates how it builds upon an awareness of the flow ofinformation within the system, the flow being driven by thedemands for the relevant knowledge by the medical users ofthe system. These information flows present an interactionprotocol that is driven by legal and ethical concerns given thesensitive nature of medical information. As with the domainontology where the interactions between the different sourcesof knowledge about a case was organised into the separation ofdifferent groups of concepts, here too we develop an "servicecomposition ontology (SCO)" that organises the different ser-vices that are invoked and cause information transfers aroundthe system, so that a client can invoke the available distributedservices described. Apart from the client and the server, ageneral task invocation framework allows remotely developedand appropriately packaged modules to be called upon, shouldthey be relevant to the knowledge handling requirements foruse. This too is described in SCO and is run on a remoteserver.

SessronandState Marhall

SOAP IRS Globus

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SerOlft AXPI t a* llD-p g D>Itlau

Figure 1. MIAKT services

1) Declarative Specifications and Applications: The taskinvocation sub-system uses a number of different mappingsto provide task-level invocation of functionality on disparatesystems. The idea in the task invocation sub-system is first andforemost to protect the client application from changes in theremote web-resources. Secondly, it provides a clean interfacefor the execution of web-resources that are accessed throughdifferent interfaces. The flexibility provided by the system alsoprovides a good base for application deployment.

In the task invocation framework, a Task Registry plays

7uskReisti

the analogous role, but here it is the inputs and outputs ofthe services that have to remain uncorrupted, not the internalworkings of the modules. The Task Registry is a dictionary-like structure that maps tasks on to task implementations.The task's identification (its name and other parameters) isrepresented by a Task Description. A specific task implemen-tation may be used in more than one task description, althoughnew instances of that implementation are used in each task.Both task descriptions and task implementations can havedefault arguments and both can have mappings applied to anyargument names. This also ensures flexibility between the taskdescriptions and the task implementations.

2) Knowledge Services: Our system provides a genericplatform to compose various medically relevant services de-signed particularly for Breast Cancer Domain. A number ofsuch services are provided by some of our partners in theproject from other universities and they have been and arebeing written up elsewhere. These include MRI diagnosticclassification tasks, natural language generation from caseannotations against our domain ontologies, image registration,and so on. As a way of illustrating the possible functionalitiesthat could be offered to medical personnel in this domain,our main focus was to offer a way of integrating remotelyaccessible services. Also, with the descriptive framework fortask invocation in place, the (forthcoming) semantic web ser-vice description standards could then be used to advertise suchcapabilities in appropriate semantic web service repositories.The architecture illustrated in Figure 1 has been designed

to accommodate, among other methods, classification ser-vices based on features extracted from images via automaticmeans by image processing methods run on entire images orparts of the image segmented out by the hand-drawn regionhighlighting. In addition, in the multi-disciplinary meetingfor patient management, the cases already come with expertlabels attached shape features for masses seen in X-rays, forexample. The classifiers that we present all take descriptorsfrom the Bcio as both inputs and outputs.

3. FROM MIAKT TOUBICARE

MIAKT prototype provides us an ideal platform to investi-gate the impacts and implications of applying semantic webtechnologies to the knowledge management with respect toa distributed clinical system. In this section, we elaboratethe issues that have been addressed or briefly touched whendeveloping the MIAKT system and that would benefit thedesign of UbiCare frameworks.

3.1 LightweightOntologiesv.s.HeavyweightOntologiesPart of the problem that we faced in MIAKT was to build

a computer aided patient data system to support routine breastcancer screening sessions and subsequent patient managementmeetings. For this purpose, we needed to provide a frameworkand communication interface for the mutual understanding ofclinicians from different backgrounds. We needed to makeinformation accessible and understandable by computers inorder to provide paperless storage and remote access and

implement procedures for intelligent retrieval and sound infer-ence. All such requirements necessitate applications overlaidon a common vocabulary. Developing a common vocabularyfor a particular application is not new in any sense. Thereis a long standing tradition in medical practice to formalisethe information possessed by experts in the hope that suchformalisation can help to reduce inter- or intra-individualvariances and ambiguities in interpretation of natural lan-guage descriptions. When developing MIAKT domain ontol-ogy, we were caught in the middle of the battle betweenlightweight, application-specific ontologies and heavyweight,general-purpose ontologies. More specifically, we have gonethrough intense arguments within the local development teamand with domain experts and clinical users on whether anupper ontology and/or a existing medical ontology should bereferred to and/or included.An upper ontology seeks to provide a set of concepts

and relationships among concepts that are general enough toaddress a broad range of domains. Existing efforts towardsworkable upper level ontologies abound, e.g. Cyc3, IEEESUO [6], GOL [7], etc. Many of such ambitious projects arestill under development. It seems too optimistic to foreseea commonly accepted upper ontology in the near future.Such an uncertainty has forced us to reconsider the benefitof using upper ontologies in BcIo. Meanwhile, Bcio is avery domain-specific and application-driven ontology. Keepingthe ontology to a reasonable size to reduce complexity andensuring the operability of the entire system are concernedprior to alleged philosophical rectitude. Referencing an upperontology is likely to put unnecessary burden on the uninitiated.

There is also a trend to develop large and comprehensivemedical ontologies, e.g. UMLS, FMA, Galen and OncologyOntology (NCI)4. Argument in favour of adopting such on-tologies is the obvious compatibility with ongoing projectsand potential standards and seamless integration with otherdomain knowledge encapsulate in such ontologies. The sheersize (thousands of concepts and properties) and complexity(especially those based on Description Logics [8]) make itimpractical for miniature computing devices (e.g. PDAs andMobilephones) to process, populate and even browse suchontologies, let alone inference. This problem might be alle-viated with tools or methods for properly modularising andsegmenting ontologies, e.g. those discussed in [9] and [10].However, before such techniques and algorithms can reachthe level of practical application, limited computing power isalways the bottle-neck of employing comprehensive ontologiesin ubiquitous computing environments.

3.2 DataHeterogeneity

In a ubiquitous computing environment, participants of aconversation tends to speak their own languages presentingboth syntactical and semantical heterogeneities. In practice,heterogeneity might also be incurred when pulling data from

3http://www.cyc.com/4http://www.mindswap.org/2003/CancerOntology/

different repositories running on different platforms. Imagine ascenario that a patient is visiting a hospital after recommendedby his/her family doctor. Doctors at the hospital might need toretrieve the patient's EHR from the family doctor's local data-base to gain a comprehensive view of the patient's status. Theymight need to request an X-ray or MRI exam in a different de-partment within the same hospital or from a different hospital.When prescribing, they need to take into account the patient'sallergy history (again from his/her family doctor), possibleconflicts with the patient's ongoing medication, etc. Patient'shospital visit might end with the transfer of prescriptions fromthe hospital to a pharmacist close to his/her home who willprepare the medicine for the patient to collect. In this simplescenario, at least four data sources need to be integrated: thefamily doctor's local database, hospital's EHR system, X-ray(MRI) scheduling system, and pharmacist's local database.Mapping and aligning among the heterogeneous data sourcesimplies the following two tasks: mappings among vocabulariesfrom different domains (e.g. different nomenclatures used bypharmacists and clinicians) and mappings between legacydatabase systems and ontologies.

Ontology/database schema mapping is the key technologyto reconcile the discrepancies among different nomenclatures.Ontology mapping has been intensively studied due to thefact that global consensus is not only prohibitively expensiveto establish and maintain but also too difficult to commonlyagree upon. Ontology mapping as an alternative means forachieve interoperability allows the existence of modelling andcoding idiosyncracy at the design time and aims at on-the-fly reconciliation of disagreements. Thus far, a variety ofontology mapping techniques have been proposed [11], [12].The current mapping capability, although sophisticated andwell advanced, is not applicable in a large scale UbiCare envi-ronment. We proposed to fine-tune current mapping techniquesin at least three directions.

1) Approximate Mapping: Many existing automated orsemi-automated systems treat ontology mapping as a black-or-white question, i.e. two concepts/terms are either equivalentor disjoint. Although, confidence values are used in somesystems [13], mainly they indicate to what extent a humanuser trusts the results provided by the system. Such a trustmeasurement is highly subjective whose semantics are difficultto be quantitatively represented and unified. People wouldargue that such confidence values can be acquired from thedistance values computed based on ontological features, e.g.the string distance between two concept names, the WordNet-based distance, etc. This argument, however, neglects thedifference between a symbolic representation and its real-world counterpart and the ambiguity of human expressions inconceptualising and conveying meanings. It, therefore, onlyreflects the syntactic variances rather than semantic discrep-ancies.The idea of "approximation" helps to relax the restrictions

on mappings and bring in "soft" mappings. Imagine an in-dividual submits a query q to others using terms ti from itslocal vocabulary. q results in an empty set to be returned as the

answer that implies either the query handling individuals donot know the answer or there is no correspondence of ti in thelocal vocabulary of those receiving the query. In the latter case,the original query can be relaxed with the approximations of ti,e.g. the least upper bounds (lub) and the greatest lower bounds(glb) of ti [14]. When multiple concepts (terms) present inthe query, the upper and lower bounds are computed as theconjunction A lub(Ci) and disjunction V glb(Ci) of respectiveupper and lower bounds of each individual concepts.

2) Partial Mapping: In real life applications, it is morelikely that mappings are only sought after between fragmentsof local vocabularies due to the prohibitive cost of identifyinga complete set of alignments. such a situation gives raise totwo issues, namely fragmenting ontologies and refining (up-dating) primary alignments. Ontology fragmentation reducesthe complexity of ontology mapping problem by focusing onlyon most relevant concepts while mapping refinement provides"soft" (non-perfect) results based on available resources.

Mapping refinement, on the other hand, aims to providesufficiently "good" mapping results at each revision. Hav-ing received the initial message, e.g. "list all patient withAstrocytoma", an individual starts off by proposing a set ofcorrespondences that makes sense with regard to the currentstatus of knowledge, e.g. interpreting Astrocytoma as somesorts of Cancer. Such initial mapping results are revisited,during system idle, according to either i) knowledge fromexternal resources or ii) information gathered from other peerindividuals, that might help to narrow down the type of cancersto Brain Tumour. In both cases, refining while system idlewould reduce the system overhead. More specifically, thetarget terms (terms that are used to compose a query) togetherwith its nearest k neighbours are isolated from the rest of thelocal vocabulary as the current core set SCore for mapping.The value of k is decided based on the "greediness" of thequery submitter and the status of the ad-hoc network. Apreliminary alignment Scurrent is suggested with regard to SCoreby utilising the cheapest mapping techniques (e.g. string edit-distance). This preliminary is called the current consensus.In subsequent steps, as follow-up interactions broach moreinformation, SCurrent is enriched with more terms/conceptstowards the entire local vocabulary.

However, it should be borne in mind that ontology frag-mentation and mapping refinement cannot prevent entirelythe "approximateness" of ontology mapping for the followingreasons. In an distributed and real-time application, time andresource constraints simply mean that establishing completeand accurate mapping is impractical, if it is not impossible.Meanwhile, miniature devices are "living" in contexts andthus the perfectness/imperfectness of mappings are context-specific. Implanting defined mappings from their originalcontext into another will bring along approximateness. More-over, the freedom and dynamic nature of UbiCare imply thatmappings established a priori might often be broken. Residualdevices might have to live with incomplete mappings beforesuch links are recovered.

3) Progressive Mapping: Traditionally, ontology mappingis performed "off-line", mainly at the design time. Manyof us have grown to believe that mapping tasks performedat design-phase would have knock-on effect on ontologyrelated interoperability issues and if such issues persist, it isprobably because of a wrongly selected mapping method orinherent problems with the ontologies themselves. This staticperspective of most current mapping systems will be strugglingin a dynamic environment.

Given the limited resource available for miniature comput-ing devices, we would like to emphasis a progressive mappingstrategy. A possible continuous mapping scenario might be asfollowings. In the first step, a set of features are extractedfrom the local vocabulary and an initial consensus is defined.In subsequent steps, as follow-up interactions broach morefeatures of the ontologies/vocabularies from both the querysubmitter and the query handlers, candidates in the currentconsensus is gradually fine-tuned with more sophisticated andexpensive algorithms to discard non-qualified ones until a fixedpoint is reached or no resource is available. In this way,the current consensus becomes smaller and more accurateat each recursive step. By keeping a good explanation andbacktracking mechanism, it is possible to recover discardedcandidates when conflicts are encountered. In order to facilitatesuch a progressive approach, selected algorithms must presentdiverse computational complexity and possibly a monotonicpattern of mapping capability (the number and accuracy ofresulting mapping candidates).

Progressive mapping is based on the assumption that in aubiquitous computing environment, interoperability becomesan imminent issue only when requests are submitted andprocessed. Once a query has been tackled and the communica-tion channel has been established, data transferring substitutinginteroperability becomes the dominant issue till new requestsare raised. It is, therefore, reasonable to relocate the interimperiods between two consecutive requests to refine and updateexisting mappings. Moreover, it should be possible to extractand categorise a set of features the mapping task of whichpresents a spectrum of increasing accuracy and/or compu-tational complexity. Meanwhile, a fix point that terminatesrefinement procedure can be defined. The simplest form ofsuch a fix point can be a "hard" deadline when the mappingprocess has to stop.

3.3 MultimediaData

With the advance of technology, the format of EHR be-comes versatile. Multimedia data (e.g. images and video clips)has become increasingly important in healthcare from whichmedical staff need to draw their diagnostic and managementalconclusions. Providing integration of multimedia data into theknowledge management is, therefore, crucial to UbiCare. InMIAKT, the needs of multimedia data are accommodatedby methods built upon the image annotation module of theMIAKT domain ontology [15], [16]. We defined low-levelimage feature descriptors (e.g. hue, saturation, etc.) compat-ible with the MPEG7 Ontology [17]. Semantic Bridge is

established between low-level features and high-level med-ical descriptions with the help of image classifiers and/ormanually by domain experts (see discussions in [18] and therelevant work in [19]). For instance, the abstract morphologicaldescriptor "irregularShape" can be induced from the low-level "AreaPerimeterRatio" feature. The latter can be drawnfrom an image analysis software based on whether there is adramatic change with respect to the perimeter of a delineatedregion. The former is in turn used as the evidence to supportdescriptors from the higher abstraction level.

Generalising to UbiCare, the fundamental idea of the MI-AKT approach is still applicable. Apart from the technologythat enables faster and more secured transferring of largeamount of clinical data (e.g. those envisioned/materialisedin MAMMoGRID5), the necessity of image ontologies withmultiple abstraction levels and a semantic bridging mechanismgluing together concepts from different levels continues. Thereis, however, the need to develop ontologies to cover broaderrange of multimedia data, e.g. feature descriptors of spectra,video, and sound. Moreover, in clinical domain, there are callsfor implementing sophisticated automatic and semi-automaticannotation, classification, and content-based retrieval methodsbased on a multimedia markup languages tuned specifically tomedical applications.

3.4 InteractionAmongServices

MIAKT utilises a service composition ontology (SCO) tofacilitate and regulate the exchange of information amongdifferent services. Analogous to other communication pro-tocols, this communication ontology specifies the format ofmessage passing from one service to another, the roles of themessage initiators and recipients, the mechanism to parse andunderstand the contents of the messages, and how to composereplies. MIAKT SCO consists pointers to existing messagingstandards, e.g. IEEE FIPA specifications6. Available servicesare registered on a Yellow Page-like central repository. Thecapability of a service is described with the MIAKT systemontology. This approach is, however, lack of flexibility andextensibility. In a UbiCare environment, an interaction modelcan be adopted to give more freedom to participating services.One of the exemplar technique facilitating declarative inter-action specification is the Lightweight Coordination Calculus(LCC) [20]. LCC is a process calculus for specifying coordi-nation among multiple participants. It does so by explicatingwhat messages should be sent and are expected to receiveand what constraints should be satisfied before a messagecan be initiated. Figure 2 illustrates an example of LCC thatclassifier A asks datafeeder F to provide Patient's EHR iff ithas accessibility of this EHR.

Security and data privacy during interaction are critical tomedical applications to have any practical values. Thus far,role-based access model (RBAM) [21] is a straightforwardand flexible solution. Access control in MIAKT is realised

5http:llwww.gridstart.org/MAMMOGRID.shtml6http://www.fipa.org

a(classifier, A) ::ask-ehr(Patient) => a(datafeeder, F) -- accessible(Patient, A).

Figure 2. LCC Exmaple

by defining different categories of end users. We differentiatefive groups of users, namely Patients, Clinicians, Clinicianwith Limited Access, Developers, and System Administrators.Users playing different roles are granted different privilege.In a UbiCare environment, RBAM is still applicable withevery registered device assigned a role dynamically basedon its role in one particular interaction model. More rolesthan those defined in MIAKT, however, need to be definedto facilitate a smooth and fine-tuned service composition.Context ontologies (such as the one proposed in [22]) canplay an important role in defining the rights and obligations ofdifferent types of participants. For instance, a handheld life-sign monitoring device only takes the role of data provider.It will not be able to access and retrieve patient's EHR. Itwould also be restricted from updating the database directlybut do so through a database broker or database interface forsafety considerations. Apart from controlled accessibility, dataintegrity is the other major concern of a UbiCare application.This refers to mechanisms to ensure that data is passed to theright recipients, to protect data from unauthorised operations,and to check the data's integrity over transportation. In thispaper, however, the integrity issue will not be further expandeddue to the limited space.

4. CONCLUSIONS

The advances in lifesaving technology have created newscenarios of healthcare, as envisaged by IT journalists: "[ . .]electronic recording of routine data [...] alerts specialistswhen a patient shows signs of deterioration [...]"7. Centralto this are computing devices (e.g. PDAs, embedded andwearable devices, etc.) that take the role of both data providersand consumers. Knowledge management is a key factor forsuccessfully deploying this new healthcare paradigm. In ad-dition to the issues addressed in the traditional KM models,one needs to provide solutions to the management of a vastamount of multimedia data. Other challenges include findingthe balance between the expressivity of domain ontologiesand the processing power of miniature devices, dealing withlimited design time global consensuses, and offering declar-ative and nondeterministic service composition mechanisms.In light of the above challenges, we reviewed our experiencewith the MIAKT project and the breast cancer screeningmanagement. We focused particularly on the domain ontology,system architecture and knowledge services in MIAKT. Itis our contention that although not a full-fledged UbiCareframework, some design concepts of MIAKT can be borrowedand enhanced with the latest technologies to facilitate KM inUbiCare.

7Fitter, healthier, more productive, The Guardian, 15 March 2007

ACKNOWLEDGEMENT

Part of the work presented in this paper was supportedby the British Engineering and Physical Sciences ResearchCouncil (EPSRC) under the MIAKT grant GR1R85150/01.

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