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At least 595 records · Page 33Linked to original sources
PubMedDate not supplied

A decision support system for the management of accidental mushroom and plant poisoning.

In this paper the discussion focuses on a decision support system to be used as a tool in the treatment of poisoning cases attributable to mushrooms, plants and animals. In this first release, attention is focused on the risks related to fungi. Problems involved with mushroom poisoning and identification are analyzed to highlight which elements or characters must be taken into account in devising a computerized expert system. The components of such a system are presented, the different approaches are discussed and the choices made are motivated. Some preliminary results are also presented.

Artificial Intelligence↗
PubMed2003

The intelligence of the moral intuitions: comment on Haidt (2001).

The social intuitionist model (J. Haidt, 2001) posits that fast and automatic intuitions are the primary source of moral judgments. Conscious deliberations play little causal role; they are used mostly to construct post hoc justifications for judgments that have already occurred. In this article, the authors present evidence that fast and automatic moral intuitions are actually shaped and informed by prior reasoning. More generally, there is considerable evidence from outside the laboratory that people actively engage in reasoning when faced with real-world moral dilemmas. Together, these facts limit the strong claims of the social intuitionist model concerning the irrelevance of conscious deliberation.

Cognition↗
PubMed2004

Benign/malignant classifier of soft tissue tumors using MR imaging.

UNLABELLED: This article presents a pattern-recognition approach to the soft tissue tumors (STT) benign/malignant character diagnosis using magnetic resonance (MR) imaging applied to a large multicenter database. OBJECTIVE: To develop and test an automatic classifier of STT into benign or malignant by using classical MR imaging findings and epidemiological information. MATERIALS AND METHODS: A database of 430 patients (62% benign and 38% malignant) from several European multicenter registers. There were 61 different histologies (36 with benign and 25 with malignant nature). Three pattern-recognition methods (artificial neural networks, support vector machine, k-nearest neighbor) were applied to learn the discrimination between benignity and malignancy based on a defined MR imaging findings protocol. After the systems had learned by using training samples (with 302 cases), the clinical decision support system was tested in the diagnosis of 128 new STT cases. RESULTS: An 88-92% efficacy was obtained in a not-viewed set of tumors using the pattern-recognition techniques. The best results were obtained with a back-propagation artificial neural network. CONCLUSION: Benign vs. malignant STT discrimination is accurate by using pattern-recognition methods based on classical MR image findings. This objective tool will assist radiologists in STT grading.

Algorithms↗
PubMed2006

Stock trading using RSPOP: a novel rough set-based neuro-fuzzy approach.

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model.

Algorithms↗
PubMed1999

Knowledge-mediated retrieval of laboratory observations.

Intelligent medical applications including agents, clinical decision support systems, and expert systems can benefit from components that expose the meanings of medical concepts. We have endeavored to create an ontology for laboratory observations and to make the ontology accessible in a distributed environment through a knowledge mediator offering several services. To date we have created two such services, one service to mediate the retrieval of laboratory observations and an auxiliary service to facilitate the mapping of units of measure to LOINC property-types. We report progress and insights on the development of our ontology and related knowledge mediator.

Artificial Intelligence↗
PubMedDate not supplied

Using CommonKADS to create a conceptual model of a guideline system for breast cancer prognosis.

One of the major aspects in breast cancer research is the identification of prognostic factors accurate enough to define different therapeutic decisions; each prognostic factor on its own is insufficient for the prediction of the biological behaviour of the tumour, but a combination of these parameters is necessary. The work described here focuses on the definition of a conceptual knowledge model of the prognosis of breast cancer. Our approach to the conceptualization of the problem follows the CommonKADS (Knowledge Acquisition and Design Structuring) Library for Expertise Modelling. The aim of this work is to provide a first conceptualization of breast cancer prognosis while evaluating the efficacy of the CommonKADS methodology in facing the problem.

Artificial Intelligence↗
PubMed2001

Multialternative decision field theory: a dynamic connectionist model of decision making.

The authors interpret decision field theory (J. R. Busemeyer & J. T. Townsend, 1993) as a connectionist network and extend it to accommodate multialternative preferential choice situations. This article shows that the classic weighted additive utility model (see R. L. Keeney & H. Raiffa, 1976) and the classic Thurstone preferential choice model (see L. L. Thurstone, 1959) are special cases of this new multialternative decision field theory (MDFT), which also can emulate the search process of the popular elimination by aspects (EBA) model (see A. Tversky, 1969). The new theory is unique in its ability to explain several central empirical results found in the multialternative preference literature with a common set of principles. These empirical results include the similarity effect, the attraction effect, and the compromise effect, and the complex interactions among these three effects. The dynamic nature of the model also implies strong testable predictions concerning the moderating effect of time pressure on these three effects.

Artificial Intelligence↗
PubMed1993

A decision aid for diagnosis of liver lesions on MRI.

Abdominal magnetic resonance imaging (MRI) plays an important role in the evaluation of liver abnormalities. The interpretation of MR images requires expert training in a rapidly changing field. DAFODILL (Decision Aid for Diagnosing Liver Lesions) is a decision-support tool designed to aid radiologists in the diagnosis of hepatic lesions seen on MRI. DAFODILL uses a knowledge base of MRI findings and a belief-network inference engine to generate probabilistic differential diagnoses of the most commonly encountered hepatic lesions. DAFODILL performs limited image processing to identify clinically relevant features, which are presented to the user for confirmation before they are used by the network. Preliminary evaluation of an initial version of the system suggests that DAFODILL may be a useful tool for radiology residents and nonexpert radiologists in interpreting MR images of the liver.

Artificial Intelligence↗
PubMedDate not supplied

A design for decision making: construction and connection of knowledge bases for a diagnostic system in medicine.

We describe the process of organizing medical knowledge into knowledge bases and designing one architecture for a decision support system in clinical psychiatry. We define a set of knowledge bases that we regard as the necessary and sufficient structures to represent the medical knowledge to provide clinical consultations: disease profiles; frames with semantic relations to represent clinical findings; production rules with probabilities, to relate findings with diagnoses; a hierarchical classification tree, to represent disease categories; heuristic questions, to narrow the diagnostic hypotheses; and diagnostic criteria to conclude the clinical investigation. We propose one new architecture for a support system connecting these knowledge bases in a particular way to simulate medical clinical reasoning.

Artificial Intelligence↗
PubMed1994

MEDUSA: a fuzzy expert system for medical diagnosis of acute abdominal pain.

Even today, the diagnosis of acute abdominal pain represents a serious clinical problem. The medical knowledge in this field is characterized by uncertainty, imprecision and vagueness. This situation lends itself especially to be solved by the application of fuzzy logic. A fuzzy logic-based expert system for diagnostic decision support is presented (MEDUSA). The representation and application of uncertain and imprecise knowledge is realized by fuzzy sets and fuzzy relations. The hybrid concept of the system enables the integration of rule-based, heuristic and case-based reasoning on the basis of imprecise information. The central idea of the integration is to use case-based reasoning for the management of special cases, and rule-based reasoning for the representation of normal cases. The heuristic principle is ideally suited for making uncertain, hypothetical inferences on the basis of fuzzy data and fuzzy relations.

Abdomen, Acute↗
PubMed1992

CASSPERT--an expert system to guide choice and strategy in coronary angioplasty.

Coronary angioplasty is a technique widely used in the treatment of coronary artery disease. The success of coronary angioplasty depends on patient selection, the use of an appropriate technique, and to a large extent a carefully considered choice of angioplasty equipment. The authors have developed an expert system which can assist in this decision making process. CASSPERT was developed using the expert system shell 'Leonardo' running on a standard Personal Computer. The user interacts with CASSPERT to build up a detailed profile of clinical, investigational and angiographic features of the patient. This information, together with technical data stored within Leonardo's own object orientated database, is used to infer a suitable choice of equipment. To make the expert system useful in clinical practise, it was interfaced within a functional, readily accessible, data acquisition and storage environment which could be used within the day to day running of the department.

Angioplasty, Balloon, Coronary↗
PubMed1996

Clinical decision support for physician order-entry: design challenges.

We report on a joint development effort between ALLTEL Information Services Health Care Division and IBM Worldwide Healthcare Industry to demonstrate concurrent clinical decision support using Arden Syntax at order-entry time. The goal of the partnership is to build a high performance CDS toolkit that may be easily customized for multiple health care enterprises. Our work uses and promotes open technologies and health care standards while building a generalizable interface to a legacy patient-care system and clinical database. This paper identifies four areas of design challenges and solutions unique to a concurrent order-entry environment: the clinical information model, the currency of the patient virtual chart, the granularity of event triggers and rule evaluation context, and performance.

Artificial Intelligence↗
PubMed2006

Prognosis in critical care.

Prognostic risk prediction models have been employed in the intensive care unit (ICU) setting since the 1980s and provide health care providers with important information to help inform decisions related to treatment and prognosis, as well as to compare outcomes across institutions. Prognostic models for critical care are among the most widely utilized and tested predictive models in healthcare. In this article, we review and compare mortality prediction models, including the APACHE (1981), SAPS (1984), APACHE-II (1985), MPM (1987), APACHE-III (1991), SAPS-II (1993), and MPM-II (1993). We emphasize the importance of model calibration in this domain. In addition, we present a brief review of the statistical methodology, multiple logistic regression, which underlies most of the models currently used in critical care.

Artificial Intelligence↗
PubMed1987

Artificial intelligence. Expert systems for clinical diagnosis: are they worth the effort?

Modeling the decision-making processes of human experts has been studied by scientists who call themselves psychologists and by scientists who say they are students of artificial intelligence (Al). The psychological research literature suggests that experts' decision-making processes can be adequately captured by simple mathematical models. On the other hand, those in Al who are preoccupied with human expertise maintain that complex computer models, in the form of expert systems, are required to do justice to those same processes. The resultant paradox of simple versus complex decision-making models is investigated here. The relevant literatures in psychology and Al are reviewed and, based on these findings, a resolution of the paradox is offered.

Artificial Intelligence↗
PubMed1999

A strategy for developing practice guidelines for the ICU using automated knowledge acquisition techniques.

OBJECTIVES: To implement practice guideline entry tools in a reminder system in order to provide decision support to health care workers in clinical care and emergency care environments. To design a knowledge acquisition environment that enables physicians to formulate, update, and verify guidelines without the assistance of a knowledge engineer. METHODS: We developed a knowledge acquisition environment for the Intensive Care Unit (ICU) consisting of 1) a graphical knowledge acquisition tool, 2) tools that perform logical and semantic tests on proposed guidelines, 3) a Patient Data Management System (PDMS) containing clinical patient data, and 4) an expert system that reminds ICU health care workers of inconsistencies between a treatment plan and implemented guidelines. Physicians enter the guidelines using the knowledge acquisition tool, after which consistency and correctness tests are performed on the guidelines. The guidelines are then transferred to the knowledge base of the reminder system and validated by applying the new guidelines to a large stored data set of previous patients. If the new guidelines are approved, they are exported to the reminder system that is used in daily practice. RESULTS: ICU physicians used the knowledge acquisition tool to enter 58 guidelines into the reminder system's knowledge base. These guidelines were tested on a data set consisting of 803 previously admitted patients. As a result, 27 guidelines fired at least once, generating 406 reminders in total. Of the 406 generated reminders, 356 (88%) were issued correctly and 50 (12%) were false alarms. The reminders that were issued correctly involved 3 situations: 1) the database contained inconsistent or incomplete information, 2) the actions or decisions of the health care workers were not the most appropriate ones, and 3) there was a potential risk involved. All false alarms were caused by the fact that the corresponding guidelines were not specific enough to handle certain exceptions. As a result of this analysis, the guidelines could be improved in such a way as to eliminate all false alarms. CONCLUSIONS: These first results demonstrate that this bottom-up knowledge acquisition strategy, implemented by the automated knowledge acquisition tools, enables medical specialists to improve the quality of computer support in an ICU without assistance of a knowledge engineer.

Artificial Intelligence↗
PubMed1995

Computer-assisted decision support systems for patient management in an intensive care unit.

The application of the intelligent monitoring techniques of case-based reasoning and neural network analysis to physician decision making concerning patient care in an Intensive Car Unit (ICU) is described. Case-based reasoning offers a model for quickly matching--using a predetermined hierarchical structure--a single patient's parameters (text or numeric) to similar parameters contained in a clinical database. The output produces a group of patients which may be set to match exactly on certain characteristics and may also be set to match "as closely as possible" on a gradient of patient properties. Clinicians may thus use the system to find the group of the closest matching cases to their current patient. Aspects of the ICU history of the selected group may then be displayed graphically (e.g., mortality, length of stay, hours of ventilation, procedures utilized, and complications encountered). Neural network analysis is a pattern recognition technique which uses a training set of patient data (text or numeric) to seek mathematical relationships between various subsets of patient parameters. The discovered relationships from the training set are then applied to estimate the outcomes (e.g., mortality, length of stay, hours of ventilation) of new patients. The effects of these intelligent monitoring techniques are scheduled to be tested in a field trial held in a regional referral center ICU.

Algorithms↗
PubMed2005

Knowledge representation and sharing using visual semantic modeling for diagnostic medical image databases.

Information technology offers great opportunities for supporting radiologists' expertise in decision support and training. However, this task is challenging due to difficulties in articulating and modeling visual patterns of abnormalities in a computational way. To address these issues, well established approaches to content management and image retrieval have been studied and applied to assist physicians in diagnoses. Unfortunately, most of the studies lack the flexibility of sharing both explicit and tacit knowledge involved in the decision making process, while adapting to each individual's opinion. In this paper, we propose a knowledge repository and exchange framework for diagnostic image databases called "evolutionary system for semantic exchange of information in collaborative environments" (Essence). This framework uses semantic methods to describe visual abnormalities, and offers a solution for tacit knowledge elicitation and exchange in the medical domain. Also, our approach provides a computational and visual mechanism for associating synonymous semantics of visual abnormalities. We conducted several experiments to demonstrate the system's capability of matching synonym terms, and the benefit of using tacit knowledge in improving the meaningfulness of semantic queries.

Artificial Intelligence↗
PubMed1996

A knowledge-based model construction approach to medical decision making.

We present a framework for representing the probabilistic effects of actions and contingent treatment plans. Our language has a well-defined declarative semantics and we have developed an implemented algorithm (named BNG) that generates Bayesian networks (BN) to compute the posterior probabilities of queries. In this paper we address the problem of projecting a contingent treatment plan by automatically constructing a structure of interrelated BNs, which we call a BN-graph, and applying the available propagation procedures on it. To address the optimal plan generation, we base our approach on the observation that normally the target plan space has a well-defined structure. We provide a language to describe plan spaces which resembles a programming language with loops and conditionals. We briefly present the procedures for finding the optimal plan(s) from such specified plan spaces.

Acute Disease↗