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At least 919 records · Page 51Linked to original sources
PubMed1997

DIABETOR computer aided tutoring in diabetes management.

Computer Aided Instruction (CAI) and Computer Aided Learning (CAL) Systems, are software systems that can tutor people in a given domain. Medicine is a field that is particularly amenable to computer aided instruction because one is allowed to experiment with a large number of hypothetical simulated patient/disease cases, without the ill consequences of the wrong decision in real life. This paper presents an Intelligent Tutoring system for the Management of the diabetes disease, and specifically for instruction in insulin administration. The system will be used for the education of medical personnel (general practitioners, nurses), as well as students of medicine in diabetes management. It is based on an existing expert system for diabetes management called DIABETES as well as on the knowledge of expert diabetologists.

Artificial Intelligence↗
PubMed1998

Siegfried: System for Interactive Electronic Guidelines with Feedback and Resources for Instructional and Educational Development.

The proliferation of clinical practice guidelines (CPGs) has necessitated computerized solutions for guideline distribution and implementation. In this paper we describe a Web-based system that interactively presents CPGs at the point of care. Our system, known as Siegfried, provides a generalized solution for implementing CPGs by maintaining the guideline knowledge base separate from the application that presents the guidelines. As a result of this design, new CPGs can be easily added and existing CPGs can be expeditiously modified without additional programming. This system also solicits feedback from users regarding guideline recommendations and provides hypertext links to relevant Web-based instructional and educational resources.

Artificial Intelligence↗
PubMed2000

Contribution of artificial intelligence to the knowledge of prognostic factors in laryngeal carcinoma.

Many studies have investigated prognostic factors in laryngeal carcinoma, with sometimes conflicting results. Apart from the importance of environmental factors, the different statistical methods employed may have influenced such discrepancies. A program based on artificial intelligence techniques is designed to determine the prognostic factors in a series of 122 laryngeal carcinomas. The results obtained are compared with those derived from two classical statistical methods (Cox regression and mortality tables). Tumor location was found to be the most important prognostic factor by all methods. The proposed intelligent system is found to be a sound method capable of detecting exceptional cases.

Aged↗
PubMed2000

Health expert's tacit knowledge acquisition and representation using specialised healthcare scenarios.

The abundance and transient nature to healthcare knowledge has rendered it difficult to acquire with traditional knowledge acquisition methods. In this paper, we propose a Knowledge Management approach, through the use of scenarios, as a mean to acquire and represent tacit healthcare knowledge. This proposition is based on the premise that tacit knowledge is best manifested in atypical situations. We also provide an overview of the representational scheme and novel acquisition mechanism of scenarios.

Artificial Intelligence↗
PubMedDate not supplied

[Factorial structure of the executive functions in young university students].

INTRODUCTION: Several studies have proposed a multiple dimensional theoretical model for executive function. OBJECTIVE: To identify the factor structure of the executive function in a sample of young university students of different modalities of learning in their careers. METHODS: 100 participants of both sexes, age 16 to-21-year old and normal Full Scale IQ were selected in a randomized and representative approach from private universities of Medellin Colombia. They were student of verbal, visuospatial and mathematical careers. A executive function assessment battery were applied, which included: Wisconsin Card Sorting Test (WCST), Trail Making Test (TMT) A and B, verbal fluency test (FAS) by phonologic and semantic guides, and Stroop test. RESULTS: A structure of four factor was found, which explained 74.9% of variance. Factors were: 1. Organization and flexibility, which explained 26.6% of variance; 2. Processing speed, 19.7%; 3. Inhibitory control, 15.1%; and 4. Verbal fluency 13.4%. CONCLUSION: A multiple factor structure of the executive function in young university students was demonstrated.

Adolescent↗
PubMed2001

Acquisition and analysis of repeating patterns in time-oriented clinical data.

OBJECTIVES: (1) Creation of an expressive language for specification of temporal patterns in clinical domains, (2) Development of a graphical knowledge-acquisition tool allowing expert physicians to define meaningful domain-specific patterns, (3) Implementation of an interpreter capable of detecting such patterns in clinical databases, and (4) Evaluation of the tools in the domains of diabetes and oncology. METHODS: We describe a constraint-based language, named CAPSUL, for specification of temporal patterns. We implemented a knowledge-acquisition tool and a temporal-pattern interpreter within Résumé, a larger temporal-abstraction architecture. We evaluated the knowledge-acquisition process with the help of domain experts. In collaboration with the Rush Presbyterian/St. Luke's Medical Center, we analyzed data of bone-marrow transplantation patients. The expert compared the detected patterns to a manual inspection of the data, with the help of an experimental information-visualization tool we are developing in a related project. RESULTS: The CAPSUL language was expressive enough during the knowledge-acquisition process to capture almost all of the patterns that the experts found useful. The patterns detected in the data by the pattern interpreter were all verified as correct. Completeness (whether all correct patterns were found) was difficult to assess, due to the size of the database. CONCLUSIONS: The CAPSUL language enables medical experts to express temporal patterns involving multiple levels of abstraction of clinical data. The ability to reuse both domain-patterns and abstract constraints seems highly useful. The Résumé interpreter, augmented by the CAPSUL semantics, finds the complex patterns within a clinical time-oriented database in a sound fashion.

Algorithms↗
PubMed2003

Making the standard more standard: a data and query model for knowledge representation in the Arden syntax.

CONTEXT: Arden Syntax is a Health Level Seven (HL7) standard that can be used to encode computable knowledge. However, dissemination of knowledge is hampered by lack of standard database linkages in Arden knowledge bases (KB). Moreover, the HL7 Reference Information Model (RIM) is object-oriented and hence incompatible with the current Arden data model. Also, significant investment has been made in Arden KBs that would be lost if a backward-incompatible data model were adopted. OBJECTIVE: To define a data model that standardizes database linkages and provides object-oriented features while maintaining backward compatibility. ANALYSIS: We identified the objects of the RIM that could be used as a schema for standard database queries. We propose extensions to Arden to accommodate this model, including the manipulation of objects. CONCLUSION: A data model that standardizes database linkages and introduces object-oriented constructs will facilitate knowledge transfer without violation of backward compatibility in the Arden Syntax.

Artificial Intelligence↗
PubMed2004

Artificial neural networks in nuclear medicine.

An analysis of the accessible literature on the diagnostic applicability of artificial neural networks in coronary artery disease and pulmonary embolism appears to be comparative to the diagnosis of experienced doctors dealing with nuclear medicine. Differences in the employed models of artificial neural networks indicate a constant search for the most optimal parameters, which could guarantee the ultimate accuracy in neural network activity. The diagnostic potential within systems containing artificial neural networks proves this calculation tool to be an independent or/and an additional device for supporting a doctor's diagnosis of artery disease and pulmonary embolism.

Algorithms↗
PubMed2004

The oncological nurse assistant: a web-based intelligent oncological nurse advisor.

As more people get cancer the need for medical guidance increases. In Norway, one of the providers of medical guidelines is the Norwegian Cancer Association where oncological nurses assist people with a cancer diagnosis or their relatives. The nurses search through both national and internal guide-books and web pages. The input to this process is mostly dis-charge letters. The whole process is time consuming. To serve more patients, PaSent, a web-based intelligent oncological nurse advisor, has been developed. Through using discharge letters as input to our neural network based information retrieval system PaSent, we have been able to provide relevant medical information to the patient as well as to the health personnel themselves. The PaSent search method uses predefined knowledge about the context, paired with the vocabulary of the input document, to compute a relevance measure for a potential result document. The system has been validated by oncological nurses and medical doctors. In the reported experiments, the achieved search results from PaSent have been comparable to the results achieved by the health personnel.

Artificial Intelligence↗
PubMed2004

HELEN, a modular framework for representing and implementing clinical practice guidelines.

OBJECTIVES: In order to implement clinical practice guidelines for the Department of Neonatology of the Heidelberg University Medical Center we developed a modular framework consisting of tools for authoring, browsing and executing encoded clinical practice guidelines (CPGs). METHODS: Based upon a comprehensive analysis of literature, we set up requirements for guideline representation systems. Additionally, we analyzed further aspects such as the critical appraisal and known bridges and barriers for implementing CPGs. Thereafter we went through an evolutionary spiral model to develop a comprehensive ontology. Within this model each cycle focuses on a certain topic of management and implementation of CPGs. RESULTS: In order to bring the resulting ontology into practice we developed a framework consisting of a tool for authoring, a server for web-based browsing, and an engine for the execution of certain elements of CPGs. Based upon this framework we encoded and implemented several CPGs in varying medical domains. CONCLUSIONS: This paper shall present a practical framework for both authors and implementers of CPGs. We have shown the fruitful combination of different knowledge representations such as narrative text and algorithm for implementing CPGs. Finally, we introduced a possible approach for the explicit adaptation of CPGs in order to provide institution-specific recommendations and to support sharing with other medical institutions.

Academic Medical Centers↗
PubMed1987

Using the computer to optimize human performance in health care delivery. The pathologist as medical information specialist.

The demands for information retrieval, processing, and synthesis placed on all providers of health care have increased dramatically in the last several decades. Although systems have been developed to capture charge-related data in support of cost reimbursement, there has been a conspicuous lack of attention paid to information tools to directly enhance the delivery of patient care. The termination of cost reimbursement, together with an increasing recognition of the problems inherent in current manual record-keeping systems, is creating a significant new focus on medical information. This change in focus requires a shift in systems orientation away from financial and departmentally centered systems and toward patient-centered approaches. There is thus increasing recognition of the need for a physician-level medical information specialist to serve as an institution's chief information officer, assuming responsibility for the collection, manipulation, and availability of all patient care-related data. By virtue of training, typical experience, hospital presence, and a noncompetitive position with the hospital's medical staff, the pathologist is uniquely suited for this position. To effectively perform this role, a variety of new specialized data management tools are becoming available. Integrated information systems, patient care management by exception, decision support tools, and, in the future, "artificial intelligence" assists can all be expected to become staples of pathology practice, especially impacting those pathologists who choose to be responsive to the new practice milieu of medical information science.

Artificial Intelligence↗
PubMed1993

The induction of rules for predicting chemical carcinogenesis in rodents.

This paper presents results from an ongoing effort in applying a variety of induction-based methods to the problem of predicting the biological activity of noncongeneric (structurally dissimilar) chemicals. It describes initial experiments, the long-term goal of which is to assist toxicologists, cancer researchers, regulators, and others to predict the toxic effects of chemical compounds. We describe a series of experiments in tree and rule induction from a set of example chemicals whose carcinogenicity has been determined from long-term animal studies, and compare the resulting classification accuracy with eight published human and computer predictions for a common set of 44 test chemicals. The accuracy of our system is comparable to the most accurate human expert prediction yet published, and exceeds that of any of the computer-based predictions in the literature. The induced rules provide confirmation of current expert heuristic knowledge in this domain. These early results show that an inductive approach has excellent potential in predictive toxicology.

Animals↗
PubMedDate not supplied

A case-based learning approach to grouping cases with multiple malformations.

A case-based classification system can provide assistance to specialists in dysmorphology. This article describes a case-based model designed to assist in identification and retrospective analysis of rare types of syndromes that have proved difficult to diagnose. The primary task of diagnosis is complemented by a learning, or grouping, task. Using data sets of diagnosed cases in related categories of syndromes, we demonstrate how a case-based learning algorithm can extend the retrieval and indexing mechanisms of standard databases to provide a focus for analysis of syndrome classifications.

Abnormalities, Multiple↗