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Knowledge Acquisition Environment for the Design of a Decision Support System : Application in Blood Transfusion Soumeya L. Achour, DVM', MS, Michel Dojat, Eng., PhD 2, Claire Rieux, MD', Philippe Bierling MD, PhD', Eric Lepage, MD, PhD', 1 Hospital Information Department, Henri Mondor, AP-HP, Creteil France 2 RMN Bioclinique, INSERM U438, Grenoble, France ,
of our project has consisted in the design of a knowledge acquisition environment where the expert can select terms and define new ones that correspond to his/her expertise. It allows the construction of the domain ontology, directly by the domain expert, from a selection of entities and relations present in the UMLS knowledge [4]. The second step has consisted in extending the environment for the domain knowledge acquisition. Again, the domain expert is guided to introduce new rules and modify the knowledge base he/she gradually builds, using the domain ontology previously defined. The development of MLMs is an interesting attempt to achieve sharing and reuse, so that the knowledge acquired in the precedent step, is then translated into MLMs written in Arden syntax. In this paper, we describe in section "Method", our knowledge acquisition environment. At the Mondor Hospital (Creteil, Fr) we have started a project for the development of a DSS for blood transfusion, the evolution of medical knowledge in blood transfusion and governmental regulation impose a continuous adaptation of the DSS, thus we describe in section "Result", the process of building such a DSS using our knowledge acquisition environment. The last section is devoted to discussion.
Blood transfusion is a medical domain where decision support systems (DSSs) could be very helpful to the physicians but must easily and continuously be maintained We have developed a knowledge acquisition tool that allows the construction and the maintenance of such a system by the domain expert. The methodology used could be applied to another highly evolutive medical domain. In this paper, we detail our knowledge acquisition tool, its use and the final DSS obtained, which is fully integrated into our hospital information network. INTRODUCTION have been demonstrated as very DSSs Clinical helpful to medical practitioners. Unfortunately, two major reasons limit their development and integration in medical field: 1) the time and the necessary efforts requested from the medical experts to work out the knowledge bases and 2) the extreme difficulty for sharing and reuse validated knowledge bases. Indeed, various authors have stressed out the importance of sharing and reuse for the DSSs integration in clinical routine [1], [2]. A possible way to achieve sharing is the standardization of medical knowledge bases. For a significant class of medical decisions, it appears that a basic particular structure of knowledge, represented by modular units independent from each other, is applicable and facilitate sharing. Per modular unit, one understands the possibility of extending in an incremental way, the initial logic of decision of an existing base of knowledge. These units of medical logic are called " Medical Logic Module " (MLM) [3]. The Arden Syntax has been promoted as an open standard for the procedural representation and sharing of MLMs . The objective of our work is to design tools to facilitate the creation and the maintenance of a knowledge base by the domain expert and then its sharing and reuse by other institutions. The first step
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BACKGROUND Knowledge Acquisition: Knowledge acquisition and modeling play a leading role in the development of knowledge based system (KBS). Traditionally, acquisition of knowledge was seen as the activity of transferring expertise from human experts or from technical documents in an abstract structure, in the form of models in the artificial world. Generic Abstractions: First generation of on a knowledge-based systems were built knowledge transfer principle. The second generation of KBS puts forward the well-known separation between conceptual model (knowledge level) and the representation paradigm (symbol level) [5]. Clearly, this distinction facilitates reuse through the
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elicitation of generic abstractions such as tasks, problem solving methods, lexicons and model ontologies. This leads to the elaboration of methodologies and frameworks for knowledge acquisition for general applications such as KADS [6] or specific purposes applications such as Protdge [7] in the medical domain. Generic abstractions are reused as units or reconfigured and modified by the developers. Standardization: Another way to achieve reuse and sharing is to adopt at the symbol level a standard syntax to encode knowledge. In recent years the Arden syntax has been accepted by the ASTM as a standard for medical knowledge representation An MLM, equivalent to a single rule in a rule-based expert system, contains enough medical knowledge and data to make a single clinical decision, MLMs are therefore independent of each other [3]. A MLM is an ASCII file composed of slots grouped into three categories: Maintenance, Library and Knowledge (Figure 1). The first two categories maintenance and library are used for identification of the source, explaining and documenting the knowledge represented in the MLM. The knowledge category contains four important slots: (1) the data slot wich defines data used by the MLM, (2) the evoke slot wich defines the conditions that trigger the MLM (3) the logic slot wich defines the rules of the MLM, and (4) the action slot that defines the action which must make the MLM. Our approach mixes these two aspects. Firstly, we distinguish the acquisition of domain ontology and domain knowledge and then we translate the knowledge acquired in Arden syntax to facilitate interchange of clinical decision rules.
which exist between them, and the classes or categories which subsume them. During the phase (A) (Figure 2) the domain expert using a specific browser [4] explores the UMLS Metathesaurus. He/she searches for the concepts corresponding to his/her expertise; adds them, defines the ascending hierarchy for each concept and relationships between them, selects the concept located at the top of the hierarchy that will compose the categories which subsume these concepts; and finally selects the semantic types from the UMLS semantic network. At the end, the expert has built his/her domain ontology starting from UMLS corpus. m aintenance: title: filename: version: institution: author: specialist: date: validation:
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library: purpose: Transfusionnels criteria explanation: Transfusionnels criteria for patients ws ith 'Sickle Cell anemia' and Multiple Organ Failure' keywords: erythrocyte transfusion, Hemoglobine. 'Sickle Cell anemia. Multiple Organ Failure' kn ow led ge: type: data-dri en.: data: hb:=read last( select hgb from nfs table-): cond :=read last('select cond from mid table): evoke: hb storage-: logic: if cond ='Sickle Cell anemia' and cond ='Multiple Organ Failure and Hemoglobin=8 then conclude true: texte:='Erythrocyte Transfusion ': endif ac tion: wirite texte:.
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Figure 1. A typical MLM in blood transfusion Domain Knowledge: During the second phase (B) (Figure 2), the expert defines how a specific medical problem can be solved using domain ontology and a rules-based problem solving method. We have developed a tool to acquire new rules, according to a pre-established model based on the domain ontology. Then the rules are automatically integrated in the knowledge base. Eventually, corresponding MLMs are generated. The user is guided through three major steps during this process. In the first step, corresponding to the acquisition of the Actions The entities that compose the conditions and actions
METHODS As indicated on Figure 2, our methodology consists in using a knowledge acquisition tools to acquire, directly from the expert, the domain ontology (A) and the domain knowledge (B) and then design the DSS dedicated to one application (C). The Knowledge Acquisition Tool (KA-Tool) Domain Ontology: We have chosen UMLS, for starting our domain ontology representation. The term ontology is used in different ways and in different disciplines with various meanings. In AI, according to Gruber [8] an ontology is a «< specification of a conceptualization >>. According to our use we define domain ontology as a lexicon grouping all medical terms used to represent knowledge in domain of discourse and the relations
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parts of the rules come from the domain ontology which are linked to the semantic types. According to this model, the user can create his/her rules. (Figure 3). Example: From the semantic types we can choose: conditions: (Al, A2) and action (A3) (Figure 3), the model is: (Al, A2) ---------> (A3)Let us suppose the user wants to create this rule IF condition (b,) present and condition (b3) present THEN action (b7) activated With the KA-tool the user selects, firstly, the conditions, then the semantic types according to the preestablished model presented i.e. (A,, A2). After selecting the semantic type (A,), the user can choose among two categories: (be) or (be), according to his/her rule, has to choose: (b ). After selecting the category (ba ) three concepts are presented ( b1 ), ( b2) and ( b3). Always according to his/her rule, the user chooses the concepts ( b, ) and
end, the domain knowledge is translated in Arden Syntax reusable by other users, and compiled in Visual Fox Pro (Microsoft) to be used by the DSS. RESULTS We have used our methodology to build a specific DSS for blood transfusion. Domain Ontology: For the collection of terms present (n=108) in his/her expertise for blood transfusion, the expert finds a correspondence in UMLS metathesarus for 72 concepts (i.e. 70%). A difficulty is related to composition terms such as "Thrombocytopenia post transfusionnel ". Each term exists in UMLS but the composition, which in the example refers to a causal relation, does not exist.
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Figure 4. An excerpt of our domain ontology based on the UMLS A : Semantic types, B : Categories, C : Terms used in the medical expertise Thus we have added several terms to the initial metathesaurus to enrich it. An excerpt of the domain ontology is shown in Figure 4. From the construction of the domain ontology, three different structures are obtained: 1) a semantic network similar to this present in UMLS, the main semantic types used in the expertise for blood transfusion are : », >, », > and >. 2) terms and concepts used in the expertise, 3) these terms have an ascending hierarchical context, connected by relations inverseisa. The concepts located at the top of the hierarchy that have been selected will compose the categories which subsume all the medical terms used in the expertise, so each of this terms have a semantic type and a category. An example is presented in Figure 4. Domain Knowledge: We can use our model presented above for representing rules of transfusion. The condition parts of the model can be linked to any semantic types represented. The action parts is linked to the semantic type .
Each rule of transfusion, can be integrated in the logic slot of a MLM. A typical rule for blood transfusion is: IF Condition (Sickle Cell anemia) present and Condition(Multiple Organ Failure) present and Condition(Hemoglobin=8 ) present THEN action (Erythrocyte Transfusion) In the KA-tool, the user selects from the domain ontology the type « Diseases and Pathologic Function >>, and the categories « hematologic diseases>> then choose the corresponding terms: «> and (Figure 5) and so on for the other terms. During the construction, the rule is shown at the bottom part of the screen. Then it is converted in Arden syntax to generate the MLM stored in a text file, and is automatically compiled and integrated in the knowledge base. The DSS for Blood Transfusion: The goal of the DSS is to support the physician in his/her decision to transfuse or not a patient and to choose the right blood product to be prescribed. The DSS is implemented under the Windows 95 environment, with the Visual FoxPro language.
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It works according to the event driven principle of the MLM-controller [9], that schedules the MLMs execution. lqpt of the data
our approach driven by the application is not generic compared to Protege. We use a problem solving method and our domain knowledge is modeled using simple independent rules. This is well adapted to blood transfusion application. In the future we hope to add a new module for metamodeling allowing the user to create his/her own model to build its knowledge base. Blood transfusion is a medical domain where decision support systems will be very helpful to the physicians. We are currently evaluating its performances at the Henri Mondor Hospital (Crdteil, FR). References 1. Wigertz, O., et al., Knowledge Representation and Data Model to support Medical Knowledge Base Transportability. Meth Inform Med, 1989. 2. Musen, M.A., Dimensions of Knowledge Sharing and Reuse. Comp and Biom Resea, 1992. 25: p. 435-467. 3. Hripcsak, G., et al. The Arden Syntax for medical logic modules. in Proc of the Fourteenth Annual Symposium on Computer Applications in Medical Care. 1990: IEEE Computer Press. 4. Achour, S.L., et al., The use of the UMLS Knowledge Sources for the Design of a Domain Specific Ontology : a Practical Experience in Blood Transfusion, in Proc of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, AIMDM'99. (Springer, Berlin, 1999) 249-253. 5. Newell, A., The knowledge level. 1982.Intel Arti 18: p. 87-102. 6. Wielinga, B., G. Schreiber, and J. Breuker, KADS: A modelling approach to knowledge engineering. Knowledge Acquisition, 1992. 4: p. 5-53. 7. Musen, M.A. Domain Ontologies in Software Engineering: Use of Protjge with EON Architecture. in Proc of the IMIA WG 6 conf on Nat Lang and Med Concept Representation. 1997. JacksonvilleFlorida. 8. Gruber, T., A translation approach to portable ontology specifcations. Knowledge Acquisition, 1993. 5: p. 199-220. 9. Gao, X., et al., Pre-compiling Medical Logic Modules into C++ in Building Medical Decision Support System. Comp Meth Prog Biomed, 1993. 2: p. 107-119.
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Figure 6. DSS of Blood Transfusion It is composed of various modules: the user interface that allows to enter requests concerning a patient in the data base; the knowledge base containing compiled MLMs that represent the expertise in blood transfusion, and finally the inference engine which executes the relevant MLMS. Here is, briefly described, the various steps of the DSS (Figure 6). Due to a request, an event occurs in the patient database (1) that triggers the event procedure (2). This activates (3) several MLMs that are executed by the inference engine (4) and actions are performed and results table is obtained. In case of transfusion, the product to be used is determined and an order is printed (5). DISCUSSION We have developed a KA-tool using a reusable and sharable domain ontology, the knowledge obtained is automatically transformed into Arden syntax thus can be re-used and shared by other institutions. This tool is not a simple MLM editor, but its allows the acquisition of knowledge thanks to the domain ontology. This tool has been experimented successfully for the blood transfusion application. Several points have to be improved. During domain ontology acquisition, the introduction of new items could generate redundancies and conflicts with terms already present in the base. During domain knowledge acquisition, some conflicts may appear between rules that currently should be solved by the domain expert. Our tool should be extended to incorporate specific mechanisms to deal with these drawbacks. Our approach follows the general philosophy of the Protege II project [7]: at the end we have designed a domain specific interface to allow the domain expert to directly enter or modify existing knowledge in a DSS using domain ontology. Clearly,
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