TOWARDS KNOWLEDGE MANAGEMENT IN INTENSIVE CARE UNITS

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1 TOWARDS KNOWLEDGE MANAGEMENT IN INTENSIVE CARE UNITS Jesualdo Tomás Fernández-Breis *, Laura Campoy-Gómez, Rodrigo Martínez-Béjar, Fernando Martín-Rubio Departmento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia CP Espinardo (Murcia) España Tel: Fax: jfernand@dif.um.es; campoyl@fcu.um.es; rodrigo@dif.um.es;fmartin@dif.um.es; and Juan Antonio Gómez-Rubí Servicio de Medicina Intensiva, Hospital Universitario "Virgen de la Arrixaca", Carretera Madrid-Cartagena, 30120, El Palmar(Murcia), Spain Tel: Fax: jagrubi@arrixaca.huva.es Abstract. Nowadays, the importance of Knowledge Management is growing in the Information Society. Managing knowledge in healthcare environments has been considered very important by the scientific community. Patients on Intensive Care Units need continuous attention and monitoring. The patient may be connected to an alarm system, so that an alarm is triggered when the state of the patient changes. Traditional monitoring systems have the problem of the existence of superfluous and unnecessary alarms. In this work, an ontology-based system for managing clinical knowledge (i.e., patients, alarms, physicians, equipment) in Intensive Care Units is presented. One of our objectives is to be capable of checking in every moment whether the patient meets some diagnostic hypothesis in order to keep track of his/her correct evolution. Another objective pursued here was to perform the checking process in an efficient way, avoiding the superfluous alarms. Keywords: Knowledge Management, Ontology, Intensive Care Units 1 INTRODUCTION An important problem in patient management is the unfolding of illness over time [19], so that the representation and analysis of clinical data cannot be performed without taking into account its temporal dimension [21]. In this work, this temporal aspect has been considered, since our goal was to use automated decision support for patient care over substantial periods. Therefore, we can say that time is especially important when time-oriented clinical data take part of decision support applications such as determining a diagnosis or monitoring the evolution of a patient [20]. Conventional patients monitoring systems get physiological signals and process them to extract reliable information. When a certain parameter reaches a threshold, an alarm is triggered informing about the existence of an abnormal condition. Commercial patients' monitoring systems have some important problems to overcome such as the low specificity of the alarms and the triggering of superfluous ones. Each alarm warns about abnormal values of a certain condition. In Intensive Care Units, where response time is critical, it would be desirable to have systems that apply alarm management techniques, solving (some of) the problems mentioned before. Nowadays, there is an increasing interest in the development of patients' monitoring systems that are able to produce more significant outputs (smart alarms). One way to do that is improving the quality of the information supplied by the alarms by using the historical temporal evolution of the patient. An alarm can be defined as a warning of an approaching or existing danger and traditional monitoring systems generate an alarm when the value of a variable is higher than a predefined threshold. It would also be interesting to reuse, share the knowledge acquired in a particular Intensive Care Unit (ICU) with other ICUs, and ontologies are really interesting to reach that goal. In literature, ontologies are commonly defined as specifications of domain knowledge conceptualisations [25]. In [20], a domain knowledge ontology is presented as a theory of entities (concepts), their properties (attributes), and their relations in the domain. Our ontological model * Correspondence author 1

2 covers three types of relationships: taxonomic, mereological or temporal. In medical domains (c.f. [22]), actions and effects are not necessarily instantaneous, but actions are considered to have temporal extensions, that is, actions can be expected to be performed in a time interval or a period of time [23]. This characteristic is covered by the ontological model by measuring temporality in a fuzzy manner. In this sense, another goal of our ontological model was to cover the temporal dimensionality of actions (alarms in our case). Nowadays, the importance of Knowledge Management (KM) is growing in the Information Society and medical domains are not an exception. In [26], managing knowledge in the healthcare environment is considered to be important due to the characteristics of healthcare environments and the KM properties. The objective of this work is to design and implement an ontology-based system for managing knowledge in Intensive Care Units. From ICUs clinical management perspective, we intend to have the decision making, the knowledge acquisition and management processes in knowledge bases. So, such processes will be more efficient in resources and time dimensions. On the other hand, if the clinical management of the ICUs is made through intelligent systems oriented to clinical management, then, different clinical management variables (i.e., doctors, equipment, nurses, etc) will directly interact with pure clinical variables. Finally, since this work is based on the use of Information Technologies, it would incorporate a fundamental element, the ICUs management, to the Information Society. The structure of the paper is the following. Section 2 explains the technology in this work. The ontological model of an Intensive Care Unit is described in Section 3. In Section 4, the design and implementation of the system is covered, including a brief example. Finally, some conclusions are put forward in Section 5. 2 TECHNOLOGY USED IN THIS WORK There are different products available related to knowledge-based systems and specific ontological constructors for medical domains (developed by the School of Medical Informatics (SMI) at the University of Stanford), although there is no system that tries to solve the problem we present in this work by using the knowledge management/engineering techniques applied here. This project makes use of a technology that is currently very important in Artificial Intelligence (AI) and Knowledge Engineering (KE), that is, Ontological Engineering. Such technique is now described and its application in this work justified. Knowledge Management (KM) is one of the key factors in organisations [6]. Such management implies an explicit and persistent representation of the knowledge possessed by groups of persons who might be (geographically) dispersed in the organisation. Although KM is studied in areas such as Human Resources Management and Business Organisation beyond any technological issue, there are important aspects that intelligent information systems can make viable. So, AI and related areas - such as KE- provide solutions to important parts of the KM problem, such as the identification and analysis of intensive knowledge processes in organisations. Strategic planning and products design are examples of such processes. Nowadays, there are technologies that allow for the reusing and sharing of knowledge components. One of these technologies are the ontologies, which account for the static domain knowledge. Such technologies have already been used for representing knowledge in clinical domains ([18]; [23]). However, the construction of ontologies is a complex process, so that there are not currently many methods available. One of these methods is based in a set of functions for quantifying knowledge ([14]; [15]). By applying these functions, different ontological vocabularies may be obtained as well as constraints that must be held by the structure of the ontology ([14]). Such approach has been validated through an environmental engineering project and for real situations ([3];[13]). 2.1 The Ontological Model In this work, an ontology is seen as a specification of a domain knowledge conceptualisation [25]. Ontologies are represented here by means of multiple hierarchical restricted domains (MHRD). This representation has been used previously in a similar sense by other authors (see, for instance, [5]). In particular, we have used the notion of Partial, Hierarchical, Multiple and Restricted Domain (PHMRD) [14], that can be specified as a set of concepts which are defined through a set of attributes. A concept has two types of attributes: specific attributes (derived from its nature) and inherited attributes (derived from its relationships with other concepts of the domain). This ontological model allows for three types of relationships among whatever two concepts: taxonomic (allowing for multiple inheritance), mereological and temporal. Taxonomic relationships are assumed to hold all the irreflexive, the antisymmetric and the transitive properties, while mereological relationships are assumed to hold all of them except for the transitive one [2]. 2

3 In addition to this, the ontology representation schema adopted here includes "structural" axioms, that is, axioms that result from the relations concept has attribute, concept 1 is a class of concept 2, concept 1 is a part of concept 2, and concept 1 occurs after concept 2. Moreover, this schema also embodies other axioms derived from some properties concerning interconceptual relationships in taxonomic, mereological and temporal organisations. We must clarify that the fact of defining ontologies without non-structural axioms does not mean that this sort of axioms cannot be defined by users as a (part of the) specification of a conceptualisation. What we do is to split up the classic definition of ontology (i.e., the one including structural and non-structural axioms) into two parts so that we term ontology to the whole specification of a conceptualisation excluding non-structural axioms. In order to implement temporal relationships, the Fuzzy Temporal Constraint Network (FTCN) model [4] has been used. This model formalises the computational representation of general situations in which an arbitrary number of events are specified. A FTCN is a couple <X,L>, where X= {X 0, X 1,..., X n } is a finite set of variables (concepts in our case) and L= {L ij i, j n} represents a finite set of fuzzy binary temporal constraints. The variable X 0 represents a precise origin, in our case, when the time is supposed to start (i.e. the first process of the temporal chain occurs). Therefore, each constraint L 0i defines the absolute occurrence of X i. 3 AN ONTOLOGICAL MODEL OF AN ICU The aim of this section is to describe the structure of an Intensive Care Unit in an ontological manner. An ICU can be seen as the application domain to model and we can extract from this domain the concepts that belong to it, such as people or equipment. Figure 1 is the graphical representation of an ontology. Each concept is expressed in a rectangle. Inside the rectangle we can see the information of each concept, its name and its specific attributes. Relationships are represented by the arrows between the concepts that take part of the relationship. The inherited attributes of a concept are not explicitly expressed in the figure but they can be obtained through the union set of the specific attributes of its taxonomic parents. Moreover, Figure 1 corresponds to the graphical representation of an ICU according to our ontological model. Figure 1. The (partial) ontological model of an ICU In our graphical representation, a concept of the application domain is represented through a rectangle which is labelled with the name of the concept and it includes the name of every specific attribute of that concept. Inherited attributes are not explicitly represented given that they can be automatically calculated by applying the functions defined previously. According to Figure 1, an ICU can be seen as comprised of three different parts, its 'Equipment', the 'Person's of the ICU, and the medical 'historical knowledge file' stored in the ICU, such a file storing the sequence of symptoms of diagnosis 'Hypotheses', or 'Best practices' guides. Concerning the equipment (of the ICU), this can be categorised according to the task performed by it. Thus, there is 'General' purpose equipment, such as a bed, and 'Monitoring' equipment such as the alarm system which is comprised of a set of alarms. Finally, we should mention the 'Person' 3

4 taxonomy of the ICU ontology. According to its attribute role, a 'Person' can be either a 'Patient' or a member of the 'Health Care' personnel. If we continue specialising the concept 'Health Care' by its inherited attribute role, a new classification can be made, and we obtain two new concepts, 'Doctor' and 'Nurse'. If we now pay attention to the concept 'Patient', we can see that it has an attribute named evolution. For this attribute, an ontology can also be constructed so that it is possible to specify the temporal evolution of the patient. The ontology corresponding to the evolution of the patient is shown in Figure 2. The evolution of a patient is specified in Figure 2 as comprised of observations. The taxonomy of observations is comprised of all the possible situations that can be detected by the system. Amongst these observations there will be temporal relationships, that are denoted in the figure by the arrows labelled with 'AFTER'. An (simplified) ontological model for an ICU has been presented in this section. We can extend our approach to an environment with more than one ICU, that is, we can interconnect several ICUs. Figure 8 shows a brief outline of the underlying global ontology for more than one ICU. The 'Global_ICU' would be the root node of that ontology, while each interconnected ICU would be a mereological child of the root concept. Each ICU_i would represent an ontology as we have specified above in this section. By allowing this interconnection, we would be encouraging the knowledge sharing between the different ICUs since each ICU could get access to other ICU's knowledge in terms of diagnostic hypotheses and methods of treating different diseases. All this knowledge (hypotheses and best practices) is stored as ontologies, which are interesting tools for providing reusable and shareable knowledge. Therefore, if doctors of an ICU incorporate a new ontology (i.e., a new diagnostic hypothesis) into the system then the rest of ICUs would benefit from this knowledge provided that all the ICUs would share the same ontologies. Thus, for instance, ICUs in less developed countries could benefit from the medical research of the most developed countries in an efficient and cost effective way. Figure 2. Ontology for the evolution of a patient 4 DESCRIPTION OF THE SYSTEM This section is a conceptual introduction to the system that has been designed and implemented, and we will explain the architecture of the system, as well as its knowledge representation schema. The system has been designed and implemented with the purpose of allowing for managing clinical information and knowledge in Intensive Care Units (ICU s), including tasks such as diagnosis and monitoring. It is based on the assumption that medical doctors can interact with the alarm system connected to patients through ontologies that are implicitly built by medical doctors. Figure 3 shows the ICU context the system has been designed for. The ICU under question is supposed to be comprised of a set of n patients, each one connected to an intelligent alarm system (IAS i, 1 i n). We assume that every IAS i includes a set of different medical devices connected to a given patient (to collect some relevant clinical information from the patient). It will also include any kind of information (mainly in terms of observations) supplied 4

5 by nurses which is theoretically complementary to the information supplied by the devices. In short, the system is in charge of integrating all patient's information (supplied by IAS i, 1 i n) into an ontology that contains all information considered as relevant by medical doctors in monitoring patients state during their stay at the ICU. In an ICU, different elements such as patients, medical doctors, nurses, or medical equipment (alarm systems, beds and so on) can be found. We are interested in building a system able to help medical doctors to follow the temporal evolution of their patients, by integrating the knowledge offered by themselves and the knowledge collected from the intelligent alarm systems as we can see in Figure 3. Patients in ICUs must continuously be monitored due to the special condition of an ICU patient s disease. Thus, the clinical response time is a critical factor, so that having a system that applies alarm management techniques is very useful to avoid false and redundant alarms. It would also be desirable to have the alarms labelled, where each label must describe pathological states of the patient. The system has been designed to meet all these requirements. Returning to Figure 3, different ICUs could be connected via a communication network to share the same knowledge base. This would result in a better global response of the system owing to manage a larger amount of knowledge which is not independent from an ICU to another one because all of them have patients with similar (or inclusive the same) diagnostic hypotheses. Therefore, sharing knowledge among different ICUs can be said to be an important element for improving the efficiency and the applicability of the system, because the medical community would take an advantage with it. Patient 1 IAS 1 Medical doctor 1 Patient 2 IAS 2 System Medical doctor 2 Patient n IAS n Medical doctor m Figure 3. The role of the system The system stores information about diagnostic hypotheses and about the temporal evolution of the patients. The information about the diagnostic hypotheses is directly introduced by the medical team (i.e., physician, nurses, etc) whereas the events of the patients' temporal evolution are obtained from both the alarms systems and the medical team. More information concerning the patients, diseases and hypotheses are kept by the system. The knowledge is represented as ontologies. There are two sources of knowledge, the intelligent alarms and the medical doctors, as Figure 3 shows. Medical doctors, when they act as experts, are in charge of introducing the knowledge about the diagnostic hypotheses whereas the alarm system produces event-based knowledge about the patients. The information is stored in a database, and the ontologies are constructed from the information found in that database. The alarm system and the medical doctors give their information related to events and patients' evolution, while this information is stored in a database and managed as ontologies. However, the system represents knowledge by using ontologies. Therefore, the information supplied by both the IAS and the medical doctor must firstly be transformed into an ontological representation to allow the system to work with this knowledge (after that, in order to store the information in a database, the ontology can be transformed into tables following the relational or the object-oriented model for example). Therefore, a systematic method for creating ontologies from temporal evolutions must be specified. The method proposed here is quite straightforward. Every ontology needs a root concept. In our case, the root of the ontology has been represented by the concept 'Evolution' and the identification of the patient is a specific attribute for that concept. There is also a common (but differently instantiated for every patient) concept for each ontology named 5

6 'Entrance' to the ICU that represents the first event in the patient's ICU history. This concept can be modelled as a mereological child of the root concept, and it has a specific attribute which represents the occurrence time of the event. When there is a new observation about the evolution of the patient, a new concept is generated and inserted into this ontology. This new concept will be a mereological child of 'Evolution' and it will have temporal relationships with events that are already in the ontology. It is obvious that the new concept cannot be a temporal parent concept of 'Entrance'. In principle, if we want to automate this transformation process, we could decide that each new event is a temporal child concept of the concept occurred previously, but this automatic process could pay not enough attention to other temporal relationships that would provide richer semantics to patient's evolutions. The ontology is modified as soon as a new event occurs to the patient, and what we handle at each instant is the evolution of the patient which is not globally comparable to the ontology that describes the theoretical evolution of the disease. Therefore, we cannot check for the equivalency of both ontologies but we must check for their consistency, that is, whether the patient's evolution follows the pattern described by the (prescribed) normal evolution of the disease. The system can also deal with patients suffering from a disease but the medical doctors are not very sure of the correct diagnosis. This is possible due to the fact that our knowledge base contains information about different diagnostic hypotheses. In this case, the patient would enter the ICU without having being diagnosed a specific disease. Thus, the patient's disease can be discovered by checking his/her temporal evolution and trying to find a temporal pattern in the knowledge base that is satisfied by the patient evolution. This situation could provoke that different hypotheses were satisfied by the symptoms of the patient (events triggered by the intelligent alarm system). In that case, all the possible diagnoses would be proposed by the system. This mechanism could help the system to deal with patients with more than one disease. However, the current version of the system is not intended to solve this problem due to the complexity of the modelling process that suffering from multiple diseases may involve (this situation is out of the scope of this work). One of the problems of traditional patient monitoring systems is the existence of false alarms, that is, alarms that are triggered but that do not have any real influence on the patient s evolution. Our system stores information about all (kinds of) alarms. When the correction of a diagnostic hypothesis is checked, the system characterises alarms as either positive or false, and it does not take into account the false ones in the process of detection whether the patient meets a specific temporal pattern (i.e., a diagnostic hypothesis) or not. A problem comes up when a false alarm is not false as such but medical doctors did not specify it. 4.1 Comparing Diagnostic Hypotheses And Temporal Evolutions It has been stated that the system checks whether the temporal evolution of a patient meets a set of diagnostic hypotheses in order to find the adequate diagnosis for the patient. For it, the ontologies built, one per hypothesis and another one for the temporal evolution must be compared in some way. There are several approaches for performing such comparisons but we decided to use a consistency-based approach in this work. Consistency-based diagnosis is based on assigning a label "normal" or "not normal" to a finite set of formulae which are denoted observations (see, for instance, [12]). The observations will be labelled as normal when they meet the consistency condition. In our case, the consistency condition is the (ontological) consistency of the ontologies that represent the patient's evolution and the diagnostic hypothesis. Therefore, the system must look for the diagnostic hypotheses that are consistent with the evolution of the patient. The consistency framework used in this work has been built on top of the framework for integrating ontologies presented in [7]. Such framework includes different functions for knowing whether two ontologies are inconsistent so that it is appropriate for our work since we are looking for consistent ontologies. However, such framework only accounts for taxonomic and mereological relations so that it had to be extended in order to deal with temporal relations as well. Two ontologies can be inconsistent from different points of view: a) Attributes: Two ontologies are inconsistent from the attribute point of view when there are two concepts, one belonging to each ontology such that they have the same name but no attributes in common. b) Structure: Two ontologies are inconsistent from the structural point of view when there are two concept, one belonging to each ontology such that they have the same attributes but their respective sets of parent/child concepts are disjoint. c) Temporal: Two ontologies are inconsistent from the temporal point of view when there are two concepts, one belonging to each ontology such that, there exists a (direct or induced) temporal relation between both concepts in both ontologies but they establish contradictory temporal orders. The extension made to the framework for integrating ontologies presented in [7] is the inclusion of functions for managing the temporal dimension. Functions for checking if two concepts are temporally equivalent or 6

7 inconsistent are included in order to know, combining these functions with the already existing ones, whether two ontologies are, respectively, equivalent or inconsistent. 4.2 An Example Let us suppose that a patient has been diagnosed a heart attack. Figure 4 (left) shows the ontology that represents the (partial) hypothetical evolution of this disease. This information has been supplied by medical doctors, who act as experts and have defined (part of) the temporal pattern for this disease on basis on their experience, knowledge and ability. This temporal pattern is known by the system, and the set of temporal ontologies that define temporal patterns for different diagnostic hypotheses constitute the most important part of the knowledge base of the system. On the other hand, Figure 4 (right) shows a temporal evolution of a (hypothetical) patient who suffers from a heart attack. This ontology is created from the stored (clinical) history of the patient, that is, from the set of events that the intelligent alarm system has detected. This ontology will evolve, so that it will change when a new event occurs. In this case, the system would check whether both ontologies, namely the one representing the hypothesis and the one representing the temporal evolution of the patient, are consistent or not. Both ontologies can be temporally inconsistent, that is, either a patient's event does not take place at the interval time specified in the diagnostic hypothesis or the temporal relationships specified in the diagnostic hypothesis are not respected by the occurrence of events (i.e., it occurs when the hypothesis contains a relationship "A occurs after B" whereas it can be drawn from the patient's evolution that "B has occurred after A". In case they are consistent, the system checks whether the occurrence times of the concepts from both ontologies are consistent, that is, it checks whether the intersection of both occurrence times is a valid fuzzy number according to the membership function used. In this example, both ontologies are consistent and the occurrence time for each event of the patient is consistent with the times that exist in the hypothesis. Therefore, the hypothesis is accepted. Figure 4. The ontologies corresponding to the diagnostic hypothesis (left) and the patient's temporal evolution (right); the label of the events is also shown in the figure. CONCLUSIONS In this work, we have presented for managing patients in Intensive Care Units. This system is part of a project whose objective is to perform Knowledge Management in ICUs, aspect that has been considered necessary in [26]. There, managing knowledge in the healthcare environment is considered to be important due to the characteristics of healthcare environments and the KM properties. We have focused this work on a single ICU although this approach can easily be extended to multiple ICUs which could share knowledge about diagnostic hypotheses. According to the author in [16], the principle functions of a knowledge management system are to facilitate: conversion of data and text into knowledge; conversion of individual and group s knowledge into accessible knowledge; connection of people and knowledge to other people and other knowledge; communication of information between different groups; creation of new knowledge that would be useful to the organisation. The system presented here has all that functionality because: (1) data supplied by the alarms systems, the medical team is transformed into knowledge by the system; (2) the knowledge from the medical team is made explicit and it will be accessible for other users; (3) 7

8 the interconnection of different ICUs or different ICUs sharing the same knowledge base would meet the third and fourth functions; (4) new knowledge can be suggested by the system but the medical doctors will always make the final decision. The technology used in this work has been the ontology. Ontologies have previously been used to do temporal abstractions. Thus, in [22] an ontology-based approach facilitates acquisition, maintenance, sharing and reuse of the required temporal-abstraction knowledge. As stated in [18], for medical domains it is not adequate to have only taxonomic and mereological constraints. Physiological events have a temporal dimensionality as well. Therefore, we must take this into account for choosing the kind of ontological model to use. Our approach exploits a significant advantage of ontologies, namely, reuse [19], since existing ontologies can be easily reused for constructing new ones which are related to the previous ones (i.e., similar hypotheses, similar ICUs). The clinical data used in our work (i.e., patients' evolution) has been represented by means of ontologies, which have been used in clinical domains elsewhere. In particular, the use of ontologies we have made in this work intends to achieve the same goal as the parameter-properties ontology that appears in [23]. The ontological model of the ICU introduced in Section 3 is a clear example of an equivalent to the parameter-properties ontology because it covers the entities, properties and relationships (according to the ontological model used) of the ICU domain. The ontological temporality has been exploited in this work mainly for modelling the temporal evolution of patients and diagnostic hypotheses. In addition to this, another interesting feature of the ontological model is that the temporal relationships are measured in a fuzzy way so that when the model is applied to the medical domain, medical doctors are provided with more facilities. Thus, this facility is appreciated by when they introduce the diagnostic hypotheses, since they cannot ensure when exactly something is going to happen to a patient but they can be able to make an estimation. In [1], thirteen possible temporal relationships are specified although they can be seen as seven relationships and their respective inverse ones, except in the case of the "equal" relation. These are the relations: before, equal, meets, overlaps, during, starts and finishes, and their respective counterpart relations. In our work, the unique temporal relation used has been the AFTER relation which has the BEFORE one as its counterpart. In [1], a general theory of action and time was pursued and actions have a starting time and a duration. On the other hand, our work only needs to have information about the instant or time interval at which an alarm is triggered. Since we are not really interested in the duration of the events the temporal relations that appear in Allen concerning duration of action are not useful for our work. Therefore, whether an action starts or finishes while another one is taking place is not significant for us and this kind of temporal relations can be omitted in our framework. We can get rid of the relations "meets", "overlaps", "during", "start" and "finishes", as well as of their respective counterparts by considering the previous argument so that there would still be three relations yet, "equal", "after", and "before". The relation "after" and its counterpart "before" have been treated as a single one in our approach. A positive temporal quantification in the AFTER relation would stand for the "after" relation whereas a negative one would stand for the "before" relationship. Finally, a zero-valued quantification would stand for the "equal " relation so that, we can model the temporal relations in our framework by means of a single AFTER relation. In this work, a consistency-based diagnosis model has been used. According to the author in [12], an abductive diagnosis is formalised as reasoning from effects to causes, with causal knowledge represented as logical implications of the form causes! effects, where causes are usually abnormalities or faults, but they may also include normal situations. On the other hand, consistency-based diagnosis finds faulty device components that account for a discrepancy between predicted normal behaviour and actually observed behaviour. This discrepancy is formalised as logical inconsistency. In our work, this discrepancy is measured by the inconsistency between concepts from the hypothesis and the history of the patient. The consistency-based diagnostic model used in this work can be considered to be an event-based one according to the categorisation made in [10], because explaining the temporal evolution of patients requires the interpretation of multiple observations in concert. This consistencybased model also allows for ignoring false alarms since these alarms don not appear in the diagnostic hypothesis so that they will be discarded. Another way of reducing the rate of false alarms was presented in [24]. There, the layered approach presented had a layer dedicated to removing the redundant and useless information in order to avoid the generation of false alarms. The philosophy of both approaches is different. In [24], only useful alarms about the patient are passed to the clinicians. Thus, false alarms are not registered in the system whereas our system does it. In our system, the patient's evolution contains all the information concerning the patient but only the alarms labelled as useful, that is, the non false ones, are taken into account for the clinical operations. We find useful to keep all the information about the patient's evolution since the diagnosis assigned to a patient might be erroneous so that those alarms labelled as false might be useful if the patient had been diagnosed the correct disease. It could also be stated that the system performs hypothetical reasoning, because of its capability of producing multiple diagnoses, generating different hypotheses and testing them in parallel. In [9], an approach for detecting trends by matching the data to patterns is presented. This approach, in addition to the consistency-based and the 8

9 abductive diagnosis, could be a third approach used for diagnosing. In that work, diagnosis is related to trend detection and the patterns matching is based on a regression model. In this work, hypotheses and temporal evolution of patients are confronted in order to know the correct diagnosis for a patient, while in [22] we could find equivalent figures for them. The equivalent for hypotheses could be clinical guidelines, obtained during design time from (medical) experts. During the execution time we would have the patient s state which could be considered as the equivalent to the temporal evolution of the patient in our system, although the aim of both works is different. In [22], the main objective is to generate a plan for guiding the patient whereas our system helps to find the correct diagnosis for the patient by looking at the hypotheses the systems has and the temporal evolution of the patient. In [22], one of the goals is to abstract the clinical data, which is often acquired or recorded as time-stamped measurements, into higher level concepts. Our approach follows a similar philosophy, and our higher level concepts could be considered to be the diagnostic hypotheses and the ontologies that represent the temporal evolution of patients, although our system does not try to create temporal abstraction in the sense that they are created in RÉSUMÉ [23]. In this sense, it can be said to be closer to the TrenDx system [8] given that our goal is not to abstract from the data but checking for the correctness of a particular diagnosis and suggesting when the diagnosis is unknown. The approach presented in ([8];[9]) is similar to our approach because both works implement some kind of pattern matching. TrenDx detects trends by matching the data to patterns called trend templates, which denote multivariate temporal and value variation in normality and in disorders. Furthermore, in the TrenDx system, the diagnosis is made though trend detection and based on a regression model. In our approach, the diagnosis is based on the consistency of the patient's evolution and the diagnostic hypothesis although this can also be seen as a way of matching different patterns. In TrenDx, each hypothesis tested is assigned a score so that the hypothesis with higher score is supposed to be the one that fits better the data whereas in our approach, due to the fact that it is not quantitative but qualitative hypotheses are labelled as possible or not according to the consistency among the patient' data and the hypotheses' temporal patterns. This system is capable of deciding whether a patient meets a diagnostic hypothesis or not, by doing an efficient management of the alarms triggering (that is, by detecting superfluous and false alarms). As pointed out in [8], false alarm rates are very high in current alarm systems and they can divert the attention of doctors away from more important tasks. But they are not the unique non-ideal alarms; false-negative alarms and true-positive ones with inappropriate delays should also be avoided. In [11], the author found that over 94% of alarms soundings in a paediatric intensive care unit might not be clinically important, the patient movement being the most common cause of the false alarms. Therefore, a reliable reduction of redundant and false positive alarms in ICUs is definitely needed [9]. The ontological model introduced here is used from two different perspectives. On the one hand, it is used to model the ICU, with its personnel, equipment, patients and so on, that is, from an organisational point of view. On the other hand, it is used to model the clinical evolution of the patients of an ICU. In [11], a different classification of alarms is made. There, alarms can be false, induced and significant. False alarms include both technical monitoring device failure and accurate alarms in response to real but transient physiological changes which have to resolved before medical intervention if necessary; induced alarms are provoked by staff manipulation of patient but they are not judged to be clinically important; significant alarms are those that result in a change in therapy. In our work, false alarms are those alarms that report an observation that is not included into the diagnostic hypothesis, being significant otherwise. As further work, one goal is to connect several ICUs through an interconnection network so that, while validated, this system can facilitate both reuse and sharing of clinical knowledge among ICUs. This, in turn, would lead in our opinion to augment the care efficiency in ICUs. Another interesting research not performed in this work is the integration of different ontologies that specify the same diagnostic hypothesis. This would imply the interconnection of several ICUs or that the system allows medical doctors to define more than one hypothesis for the same disease. A framework for integrating domain-independent ontologies that are specified attending to the model that has been presented in this work has been specified in [7]. This terminology sharing would facilitate the creation of an ICU terminology, which could be automatically actualised by the integration process when new concepts are detected in the (local) ontologies. The management of medical terminology and its evolution is considered to be a crucial element when working with patient-record systems [17]. In our approach, managing this terminology would facilitate the use of the same vocabulary in each ICU, so that the integration process would require less effort and it could be performed in a more efficient way. In addition to this, it would also be interesting to allow for the establishment of more (types of) relationships between the members of the terminology, such as links between synonym concepts. 9

10 REFERENCES 1. Allen, J.F. (1984) Towards a General Theory of Action and Time. Artificial Intelligence in Medicine 23(2): Borst, W.N. (1997). Construction of Engineering Ontologies for Knowledge Sharing and Reuse. PhD Thesis. University of Twente. Enschede, The Netherlands. 3. Campoy-Gomez, L., Martínez-Béjar, R., and Martín-Rubio, F. (1999). A knowledge-based system prototype for environmental engineering, International Journal of Knowledge-Based Intelligent Engineering Systems, 3 (4): Cardenas, M.A. (1998). A constraint-based logic model for representing and managing temporal information (in Spanish), PhD Thesis, University of Murcia, Spain. 5. Eschenbach, C., and Heydrich, W. (1995). Classical mereology and restricted domains, International Journal of Human-Computer Studies, 43: Fernández-Breis, J.T., Martínez-Béjar, R. (2000). A cooperative tool for facilitating Knowledge Management. Expert Systems with Applications, 18(4): Fernández-Breis, J.T., Martínez-Béjar, R. (2001). A cooperative framework to integrate ontologies. International Journal of Human-Computer Studies. To appear. 8. Haimowitz, I.J. and Kohane, I.S. (1993). Automated trend detection with alternate temporal hypotheses. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Haimowitz, I.J., Le, P.P., and Kohane, I.S. (1995). Clinical monitoring using regression-based trend templates, Artificial Intelligence in Medicine 7: Huang, C. (1999). Intelligent Alarms: Allocating Attention Among Concurrent Processes. PhD Thesis. Stanford University. Stanford, California. USA. 11. Lawless, S.T. (1994). Crying wolf: False alarms in a pediatric intensive care unit. Critical Care Medicine 22(6): Lucas, P. (1998): Analysis of notions of diagnosis. Artificial Intelligence Martínez-Béjar, R., Benjamins, V. R., Compton, P., Preston, P. and Martín-Rubio, F. (1998b). A formal framework to build domain knowledge ontologies for ripple-down rules-based systems. In B.R. Gaines and M. Musen (Eds.), 11th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop. SRDG Publications: Banff (Canada) 14. Martínez-Béjar, R., Benjamins, V.R. and Martín-Rubio, F. (1997). Designing Operators for Constructing Domain Knowledge Ontologies. In E. Plaza and R.Benjamins (Eds.), Knowledge Acquisition, Modelling and Management, Lecture Notes in Artificial Intelligence , Springer-Verlag, Germany. 15. Martínez-Béjar, R., Cádenas, J. M., and Martín, F. (1996). Un prototipo de sistema experto difuso para la valoración visual de paisajes, In Actas del Séptimo Congreso Expañol de Lógica Difusa, pp Oviedo: Universidad de Oviedo. 16. O Leary, D.E. (1999). Reengineering and Knowledge Management. In Fensel and Studer (Eds.) Knowledge Acquisition, Modeling and Management, Lecture Notes in Artificial Intelligence , Springer, Germany. 17. Oliver, D.E, Shahar, Y., Shortliffe, E.H., and Musen, M.A. (1999). Representation of Change in Controlled Medical Terminologies. Artificial Intelligence in Medicine 15(1): Schulz,S., Romacker M., Faggioli, G., and Hahn, U. (1999). From knowledge import to knowledge finishing automatic acquisition and semi-automatic refinement of medical knowledge. In Proceedings of KAW' Shahar, Y. (1997). A framework for knowledge-based temporal abstraction. Artificial Intelligence 90(1-2): Shahar, Y. (1999). Timing is everything: Temporal reasoning and temporal data maintenance in medicine. Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making. AIMDM 99, Aalborg, Denmark, Springer. 21. Shahar, Y. (2000). Dimension of Time in Illness: An Objective View. Annals of Internal Medicine 132 (1): Shahar, Y., Miksch,S., and Johnson, P. (1998). The Asgaard project: A task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artificial Intelligence in Medicine 14:29, Shahar, Y. and Musen, M.A Knowledge-Based Temporal Abstraction in Clinical Domains. Artificial Intelligence in Medicine, 8(3): Van de Aa, J.J Intelligent Alarms in Anaesthesia. PhD Thesis, Technische Universiteit Eindhoven, The Nederlands. 25. Van Heijst, G., Schreiber, A. T., and Wielinga, B. J. (1997). Using explicit ontologies in KBS development, International Journal of Human-Computer Studies, 45: Yu-N, C. and Abidi, S.S.R Evaluating the Efficacy of Knowledge Management Towards Healthcare Enterprise Modelling. In Proceedings of the International Joint Conference on Artificial Intelligence, Workshop on Knowledge Management and Organizational Memories. 10