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This paper describes problems identified in our attempts to develop an expert system for management of urinary tract infections. We found three aspects which we believe are important to consider when developing such systems. The objective of our future work will be to evaluate the impact of these problems on expert system development and usage.
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Data mining methods used a racially diverse sample (n = 19,970) of pregnant women and 1,622 variables that were collected in Duke's TMR electronic patient record over a 10-year period. Different statistical and data mining methods were similar when compared using receiver operating characteristic (ROC) curves. Best results found that seven demographic variables yielded .72 and addition of hundreds of other clinical variables added only .03 to the area under the curve (AUC). Similar results across methods suggest that results were data-driven and not method-dependent, and that demographic variables may offer a small set of parsimonious variables with predictive accuracy in a racially diverse population. Work to determine relevant variables for improved predictive accuracy is ongoing.
Creation and maintenance of electronic clinical alerts within a hospital's electronic medical record (EMR) or database poses a number of challenges. Development can require significant programming effort. Final testing should ideally be performed in a real clinical environment without clinician notification, which may create technical challenges. After an alert is in production, modifications may become necessary in response clinician feedback, changes in clinical factors, or technical issues. Changes may be required in the knowledge base utilized by the alert or in the presentation of the alert condition to the clinicians. Occasionally, different users within the clinical environment may wish to have the same alert data presented differently. We have developed a strategy which allows development of multi-functional alerts and facilitates modification of alert function and/or presentation with minimal to no programming effort. Some elements of this scheme may be appropriate for incorporation into clinical alerting standards.
A searcher's inability to formulate an appropriate query can result in an overwhelming number of retrieved documents. Our approach to this problem is to use information about common types or categories of queries to (1) reformulate the user's initial query and (2) create an informative organization of the retrieved documents from the reformulated query. To achieve these goals, we first must identify which common categories or types of queries are the best abstraction of the user's specific query. In this paper, we describe a system that performs this first step of categorizing the user's query. Our system uses a two-phased approach: a lexical analysis phase, and a semantic analysis phase. An evaluation of our system demonstrates that its query categorization corresponds reasonably well to the query categorizations by medical librarians and physicians.
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In many domains Case-based Reasoning (CBR) has become a successful technique for knowledge-based systems. In medical domains, attempts to apply the complete CBR cycle are rather exceptional. Some systems have recently been developed, which on the one hand use only parts of the CBR method, mainly the retrieval, and on the other hand enrich the method by a generalisation step to fill the knowledge gap between the specificity of single cases and general rules. So, in this paper we discuss the appropriateness of CBR for medical knowledge-based systems, point out problems, limitations and possibilities how they can partly be overcome.
Successful integration of knowledge-based functions in the electronic patient record depends on direct and context-sensitive accessibility and availability to clinicians and must suit their workflow. In this paper we describe an exemplary integration of an existing standalone scoring system for acute abdominal pain into two different commercial hospital information systems using Java/Corba technolgy.
The healthcare enterprise requires a great deal of knowledge to maintain premium efficiency in the delivery of quality healthcare. We employ Knowledge Management based knowledge acquisition strategies to procure 'tacit' healthcare knowledge from experienced healthcare practitioners. Situational, problem-specific Scenarios are proposed as viable knowledge acquisition and representation constructs. We present a healthcare Tacit Knowledge Acquisition Info-structure (TKAI) that allows remote healthcare practitioners to record their tacit knowledge. TKAI employs (a) ontologies for standardisation of tacit knowledge and (b) XML to represent scenario instances for their transfer over the Internet to the server-side Scenario-Base and for the global sharing of acquired tacit healthcare knowledge.
The very complex and specific knowledge about inborn errors of metabolism increases rapidly and is spread worldwide. Therefore an Internet platform for the acquisition of statistical knowledge (PATDB) and for the collection of the experience of experts (Metabolic diseases database) has been established. The success of diagnostic support highly depends on the way of knowledge representation. An information retrieval system for physicians will offer first insights about useful user interfaces and handling properties. Furthermore information fusion of biomedical systems to a Biomedical Workbench for the simulation of biochemical reactions is described.
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Implementation of practice guidelines refers to the creation of strategies and systems to operationalize the knowledge and recommendations set forth by guideline developers. We describe an approach to guideline implementation that makes direct use of the guideline document as a knowledge base. The Guideline Elements Model (GEM) provides an XML-based guideline document model that facilitates implementation of guidelines. Knowledge extraction using GEM requires document markup rather than programming and can promote authenticity and consistent knowledge encoding. Knowledge customization for the local enterprise requires addition of meta-information to pertinent components of the GEM hierarchy in a design database. GEM provides an audit trail to track local adaptation. Knowledge integration with patient data can be promoted using information management services. A design goal is to devise a system that can be applied by local clinical domain experts, quality assurance experts, and information systems programmers without requiring trained informaticians and knowledge engineers to serve as intermediaries
We investigated the capability of multilayer perceptron neural networks and Kohonen neural networks to recognize difficult otoneurological diseases from each other. We found that they are efficient methods, but the distribution of a learning set should be rather uniform. Also it is important that the number of learning cases is sufficient. If the two mentioned conditions are satisfied, these neural networks are similarly efficient as some other machine learning methods. The conditions are known in the theory of neural networks [1,2], but not often taken seriously in practice. Both networks functioned as well, excluding the case with several input variables, where the Kohonen neural networks surpassed the perceptron.
The aim of this work was to determine whether the GEM-encoding step could improve the representation of clinical practice guidelines as formalized knowledge bases. We used the 1999 Canadian recommendations for the management of hypertension, chosen as the knowledge source in the ASTI project. We first clarified semantic ambiguities of therapeutic sequences recommended in the guideline by proposing an interpretative framework of therapeutic strategies. Then, after a formalization step to standardize the terms used to characterize clinical situations, we created the GEM-encoded instance of the guideline. We developed a module for the automatic derivation of a rule base, BR-GEM, from the instance. BR-GEM was then compared to the rule base, BR-ASTI, embedded within the critic mode of ASTI, and manually built by two physicians from the same Canadian guideline. As compared to BR-ASTI, BR-GEM is more specific and covers more clinical situations. When evaluated on 10 patient cases, the GEM-based approach led to promising results.
GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent system for the acquisition, representation and execution of clinical guidelines. Temporal constraints play an important role within clinical guidelines (e.g. to specify therapies). The treatment of such constraints is one of the distinguishing features of GLARE. During acquisition, GLARE supports (i) the representation and (ii) the check of the consistency of the temporal constraints. Moreover, it (iii) automatically checks that the times of execution of specific actions respect the general temporal constraints described in the guideline. Such a treatment of temporal constraints involves the extension of various Artificial Intelligence techniques.
An important step in building guideline-based clinical care systems is encoding guidelines. Protégé-2000, developed in our laboratory, is a general-purpose knowledge-acquisition tool that facilitates domain experts and developers to record, browse and maintain domain knowledge in knowledge bases. In this poster we illustrate a knowledge-acquisition wizard that we built around Protégé-2000. The wizard provides an environment that is more intuitive to domain specialists to enter knowledge, and domain specialists and practitioners to review the knowledge entered.
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