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At least 19 recordsLinked to original sources
PubMed1995

Effective domain-dependent reuse in medical knowledge bases.

Knowledge reuse is now a critical issue for most developers of medical knowledge-based systems. As a rule, reuse is addressed from an ambitious, knowledge-engineering perspective that focuses on reusable general purpose knowledge modules, concepts, and methods. However, such a general goal fails to take into account the specific aspects of medical practice. From the point of view of the knowledge engineer, whose goal is to capture the specific features and intricacies of a given domain, this approach addresses the wrong level of generality. In this paper, we adopt a more pragmatic viewpoint, introducing the less ambitious goal of "domain-dependent limited reuse" and suggesting effective means of achieving it in practice. In a knowledge representation framework combining objects and production rules, we propose three mechanisms emerging from the combination of object-oriented programming and rule-based programming. We show these mechanisms contribute to achieve limited reuse and to introduce useful limited variations in medical expertise.

Artificial Intelligence↗
PubMed1986

Designing information systems for nursing practice: data base and knowledge base requirements of different organizational technologies.

One of the major causes of failure of information system design is the failure of developers to take into account the organizational environment, thereby leading to an unusable system. The first step in designing an effective system is to describe the user's view of the system, a view that incorporates how the system will help users to manage information in their particular organizational environment. For nurses involved in designing a nursing information system, a useful way of considering the organizational environment is provided by Perrow. The organizational technologies described by Perrow can be viewed as different models of nursing practice, each with particular requirements for a knowledge base and a data base. Nurses can identify the model that most closely corresponds to actual or desired nursing practice in their agencies and use the model's associated knowledge base and data base requirements as a guide to specifying the information system to be developed.

Artificial Intelligence↗
PubMed2002

Automatic generation of a metamodel from an existing knowledge base to assist the development of a new analogous knowledge base.

Knowledge acquisition is a key step in the development of knowledge-based systems and methods have been proposed to help elicitating a domain-specific task model from a generic task model. We explored how an existing validated knowledge base (KB) represented by a decision tree could be automatically processed to infer a higher level domain-specific task model. On-codoc is a guideline-based decision support system applied to breast cancer therapy. Assuming task identity and ontological proximity between breast and lung cancer domains, the generalization of the breast can-cer KB should allow to build a metamodel to serve as a guide for the elaboration of a new specific KB on lung cancer. Two types of parametrized generalization methods based on tree structure simplification and ontological abstraction were used. We defined a similarity distance and a generalization coefficient to select the best metamodel identified as the closest to the original decision tree of the most generalized metamodels.

Artificial Intelligence↗
PubMed2004

The internet-based knowledge acquisition and management method to construct large-scale distributed medical expert systems.

The Internet offers an unprecedented opportunity to construct powerful large-scale medical expert systems (MES). In these systems, a cost-effective medical knowledge acquisition (KA) and management scheme is highly desirable to handle the large quantities of, often conflicting, medical information collected from medical experts in different medical fields and from different geographical regions. In this paper, we demonstrate that a medical KA/management system can be built upon a three-tier distributed client/server architecture. The knowledge in the system is stored/managed in three knowledge bases. The maturity of the medical know-how controls the knowledge flow through these knowledge bases. In addition, to facilitate the knowledge representation and application in these knowledge bases as well as information retrieval across the Internet, an 8-digit numeric coding scheme with a weight value system is proposed. At present, a medical KA and management system based on the proposed method is being tested in clinics. Current results have showed that the method is a viable solution to construct, modify, and expand a distributed MES through the Internet.

Computer Systems↗
PubMed1991

AI/Consult: a prototype directed history system based upon the AI/Rheum knowledge base.

An expert system is designed which uses the AI/Rheum knowledge base as the basis for a directed workup system for rheumatological disorders. A Turbo Pascal prototype is demonstrated which, unlike AI/Rheum, permits entry by the clinician of a patient's chief complaint(s) and subsequently develops a dynamic differential diagnosis for this limited set of findings. The system restricts its line of questioning to those pertinent in order to rule in or rule out items on this dynamic differential diagnosis, and, unlike AI/Rheum, it provides immediate notification to the user when a critical mass of information has been entered in order to meet a diagnosis at one of the AI/Rheum criteria table's three levels of diagnosis (possible, probable, definite). Thus the system allows for a more rapid, focused decision making approach than does AI/Consult, while it follows the trend established by QMR in that it abandons the Greek Oracle problem solving approach and instead adopts a physician-assisted hypothesis investigation approach. The system is currently a prototype, without AI/Rheum's page oriented and mouse driven interface, and thus the total user interaction time may be longer than with AI/Rheum even though fewer user interactions (responses) are required. Plans for future development include optimization of the knowledge base to allow for more efficient, problem oriented questioning and modification of the knowledge base compiler in order to dynamically rule out diseases through the addition of three new levels of diagnosis in the knowledge base criteria tables.

Diagnosis, Computer-Assisted↗
PubMed1997

A knowledge-based decision support system for prevention and treatment of pressure ulcers.

As part of a research project intended to provide problem-based knowledge to clinicians at the point of care, we have developed a system that supports the nurse's development of patient-specific, guideline-based treatment plans for patients who have pressure ulcers or are at risk for developing them. The system captures coded data about assessment, diagnosis and interventions using a point-and-click interface. Knowledge is accessible to the user via: 1) hypertext links from the data entry screens; 2) explicit entry into an indexed version of the guideline; 3) imbedded knowledge-based rules that critique the diagnosis and offer guidance for treatment; and 4) explicit entry into interactive algorithms. The system has been implemented experimentally on one care unit at our hospital, where its impact will be assessed in comparison with a control unit. Data on 113 patients were entered during the 21-week experimental period. The system is being evaluated for its instructional adequacy, its impact on clinicians' decision-making and knowledge, and on processes of care. Users' perceptions of the system are also being evaluated. Dissemination issues in the context of today's health care environment are addressed.

Boston↗
PubMedDate not supplied

Case-based tutoring from a medical knowledge base.

The past decade has seen the emergence of programs that make use of large knowledge bases to assist physicians in diagnosis within the general field of internal medicine. One such program, Internist-I, contains knowledge about over 600 diseases, covering a significant proportion of internal medicine. This paper describes the process of converting a subset of this knowledge base--in the area of cardiovascular diseases--into a probabilistic format, and the use of this resulting knowledge base to teach medical diagnostic knowledge. The system (called KBSimulator--for Knowledge-Based patient Simulator) generates simulated patient cases and uses these cases as a focal point from which to teach medical knowledge. This project demonstrates the feasibility of building an intelligent, flexible instructional system that uses a knowledge base constructed primarily for medical diagnosis.

Artificial Intelligence↗
PubMed2006

Knowledge-based control and case-based diagnosis based upon empirical knowledge and fuzzy logic for the SBR plant.

Because biological wastewater treatment plants (WWTPs) involve a long time-delay and various disturbances, in general, skilled operators manually control the plant based on empirical knowledge. And operators usually diagnose the plant using similar cases experienced in the past. For the effective management of the plant, system automation has to be accomplished based upon operating recipes. This paper introduces automatic control and diagnosis based upon the operator's knowledge. Fuzzy logic was employed to design this knowledge-based controller because fuzzy logic can convert the linguistic information to rules. The controller can manage the influent and external carbon in considering the loading rate. The input of the controller is not the loading rate but the dissolved oxygen (DO) lag-time, which has a strong relation to the loading rate. This approach can replace an expensive sensor, which measures the loading rate and ammonia concentration in the reactor, with a cheaper DO sensor. The proposed controller can assure optimal operation and prevent the over-feeding problem. Case-based diagnosis was achieved by the analysis of profile patterns collected from the past. A new test profile was diagnosed by comparing it with template patterns containing normal and abnormal cases. The proposed control and diagnostic system will guarantee the effective and stable operation of WWTPs.

Ammonia↗
PubMed1998

Towards knowledge-based retrieval of medical images. The role of semantic indexing, image content representation and knowledge-based retrieval.

Medicine is increasingly image-intensive. The central importance of imaging technologies such as computerized tomography and magnetic resonance imaging in clinical decision making, combined with the trend to store many "traditional" clinical images such as conventional radiographs, microscopic pathology and dermatology images in digital format present both challenges and an opportunities for the designers of clinical information systems. The emergence of Multimedia Electronic Medical Record Systems (MEMRS), architectures that integrate medical images with text-based clinical data, will further hasten this trend. The development of these systems, storing a large and diverse set of medical images, suggests that in the future MEMRS will become important digital libraries supporting patient care, research and education. The representation and retrieval of clinical images within these systems is problematic as conventional database architectures and information retrieval models have, until recently, focused largely on text-based data. Medical imaging data differs in many ways from text-based medical data but perhaps the most important difference is that the information contained within imaging data is fundamentally knowledge-based. New representational and retrieval models for clinical images will be required to address this issue. Within the Image Engine multimedia medical record system project at the University of Pittsburgh we are evolving an approach to representation and retrieval of medical images which combines semantic indexing using the UMLS Metathesuarus, image content-based representation and knowledge-based image analysis.

Abstracting and Indexing↗
PubMed1998

The distinction between linguistic and conceptual semantics in medical terminology and its implication for NLP-based knowledge acquisition.

Natural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

Artificial Intelligence↗
PubMed2001

Cased-Based Reasoning for medical knowledge-based systems.

In this paper we present the results of the MIE/GMDS-2000 Workshop 'Case-Based Reasoning for Medical Knowledge-based Systems'. While in many domains Cased-Based Reasoning (CBR) has become a successful technique for knowledge-based systems, in the medical field 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. And some systems rely on integrating CBR and other problem solving methodologies. In this paper we discuss the appropriateness of CBR for medical knowledge-based systems, point out problems, limitations and possible ways to cope with them.

Artificial Intelligence↗
PubMed1995

Conceptual analysis for knowledge-base design.

Knowledge-base design requires a thorough analysis of the concepts to be represented. If the analysis is incomplete or inaccurate, the resulting knowledge base may contain arbitrary restrictions, inconsistent data, or limitations that make future extensions impossible. Conceptual analysis is even more critical for sharing knowledge bases between heterogeneous systems. Analyses that are adequate for independent systems may cause incompatibilities when the systems share data. This paper discusses the kinds of problems that require a careful conceptual analysis and the difficulties caused by an incorrect or incomplete analysis. It also shows how independently developed knowledge bases can be related to one another by reanalyzing and redefining the basic concepts and relations. Such analysis is essential for sharing knowledge between systems developed for different purposes, such as relational and object-oriented databases or expert systems and natural language processors.

Algorithms↗
PubMed1997

Supporting multi-level medical education with knowledge-based systems.

Knowledge-based systems for medicine have enjoyed minimal success in developing countries as end-user systems. The reasons for this are complex. As funding agencies understandably tend to err on the side of caution, and knowledge-based systems are still (despite an almost 40 year history) seen as a new and untried technology, few have been implemented. Of those which have, most are inappropriately simple and thus do not fit in with the real-life clinical environment. In contrast to the sophisticated systems in use in developed countries which reflect a mature technology, the use of knowledge-based systems in medicine in developing countries has primarily revolved around simple 'expert' systems, where the program functions more as a 'guru' than as a support function. We propose the more appropriate use of these systems as educational tools in medicine. In this discussion paper we describe a multi-level programme to support medical education, focusing on patient information systems involving natural language generation, decision-support systems as educational aids for primary health-care workers and model-based reasoning tools which allow exploratory learning for physicians in training. Throughout this paper we refer to Knowledge-Based Medical Education Systems as KBMES.

Artificial Intelligence↗
PubMed2026

A Critical Assessment of Evidence-Based Design's Knowledge Base and Inspiration: A Systematic Review.

PurposeThis study examines Evidence-Based Design (EBD) as an epistemological framework for guiding design research and practice, with a particular focus on its reliance on Evidence-Based Medicine (EBM) as a source of methodological inspiration.BackgroundOver the past two decades, EBD has been promoted as a way to strengthen design processes through the systematic use of scientific evidence. Its relationship to EBM, however, remains conceptually ambiguous: EBD draws legitimacy from EBM's hierarchical conception of "best evidence" while at the same time acknowledging the specificities of design practice, which do not easily fit such a model.MethodologyA systematic review was conducted on 31 publications in the design research literature that explicitly address the tension surrounding EBD's conception of "best evidence." The criticisms raised were coded and analyzed by main topics and subtopics.ResultsThe review highlights several reasons why EBM's hierarchical view of "best evidence" is an unsuitable epistemological foundation for EBD. It imposes scientifically inappropriate and practically ineffective methodological standards, devalues important sources of design knowledge, and fails to address central epistemic challenges intrinsic to design processes.ConclusionsBy bringing together critical yet fragmented insights from the literature, this study argues for the development of an updated epistemological framework for EBD. Constructing this framework will require sustained interdisciplinary dialogue between design research and philosophy of science.

Humans↗