PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Expert Systems”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,063 records · Page 59Linked to original sources
PubMed1990

Computer-assisted techniques for evaluation and treatment of hypertensive patients.

An integrated approach, progressively implemented in the ARTEMIS system since 1975, is described for the computerized management of hypertensive patients. From a medical point of view, computerized programs can be used to memorize patients' individual records and profiles, to facilitate patient management and follow-up, to store medical knowledge about hypertension and to provide facilities for decision making at the level either of the individual patient or of the population followed up. From a technical point of view, the methodology used integrates data and knowledge management facilities into the same software. Five hypertension clinics are presently using the system in France and more than 22,000 records have been registered. Answer rates to 12 mandatory questions regarding past history and examination at first visit were superior to 95% in 19,601 records created between January 1976 and December 1987. Patient database interrogation can be used to evaluate the sensitivity and specificity of various signs and symptoms for the diagnosis of secondary hypertension, and to predict, for each patient, his/her cardiovascular risk, the risk of drop-out, the risk of insufficient blood pressure control and the probable blood pressure level. It also serves to test the content and validity of the associated expert system which is progressively built up. A prospective evaluation of the performance of the expert system on 80 cases of hypertension showed overall agreement between the specialists and the expert system ranging from 58 to 91% depending on the decision.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed1979

Competitive approach may ease problems in delivery system.

Experts believe that the absence of effective market forces in the health care field removes incentives for efficiency from both providers and consumers of health care/In today's climate, the authors say, it may be that competition, not regulation, may help solve our current problems.

Delivery of Health Care↗
PubMed2001

Fuzzy logic and its applications in medicine.

Fuzzy set theory and fuzzy logic are a highly suitable and applicable basis for developing knowledge-based systems in medicine for tasks such as the interpretation of sets of medical findings, syndrome differentiation in Eastern medicine, diagnosis of diseases in Western medicine, mixed diagnosis of integrated Western and Eastern medicine, the optimal selection of medical treatments integrating Western and Eastern medicine, and for real-time monitoring of patient data. This was verified by trials with the following systems that were developed by our group in Vietnam: a fuzzy Expert System for Syndromes Differentiation in Oriental Traditional Medicine, an Expert System for Lung Diseases using fuzzy logic, Case Based Reasoning for Medical Diagnosis using fuzzy set theory, a diagnostic system combining disease diagnosis of Western Medicine with syndrome differentiation of Oriental Traditional Medicine, a fuzzy system for classification of Western and Eastern medicaments and finally, a fuzzy system for diagnosis and treatment of integrated Western and Eastern Medicine.

Diagnosis, Differential↗
PubMed1994

International Federation of Clinical Chemistry. Use of artificial intelligence in analytical systems for the clinical laboratory. IFCC Committee on Analytical Systems.

The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI) both as expert systems and as neural networks. This paper considers the role of software in system operation, control and automation and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel-processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system. In the second part of this paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories. It is concluded that AI constitutes a collective form of intellectual property and that there is a need for better documentation, evaluation and regulation of the systems already being used widely in clinical laboratories.

Artificial Intelligence↗
PubMed1993

The representation of medical reasoning models in resolution-based theorem provers.

First-order predicate logic essentially is a language to express knowledge concerning objects and relationships between objects in a domain. Many medical problems can be cast naturally in such terms. In this paper the suitability of logic as a knowledge-representation formalism for building medical expert systems is investigated. In particular, we investigate the logical representation of three typical reasoning models in medicine: diagnostic, anatomical and causal reasoning. It turns out that each of these models has its own characteristic logical structure. Furthermore, the pragmatics of using theorem-proving techniques in consulting such logic-based medical expert systems is discussed. In particular, attention is paid to the use of a meta-level architecture to improve the applicability of theorem-proving techniques in building expert systems.

Anatomy↗
PubMed1992

Automated review of blood donor screening test patterns at a regional blood center.

Regional blood centers recently increased the number of tests used to protect transfusion recipients from infectious disease. The Food and Drug Administration has noted that computer systems for managing these data have lost donor data or failed to recognize all deferrals. Of 118,396 consecutive donations, including 4,859 records with at least one positive test result, records were analyzed to determine the number and frequency of distinct combinations of test data. A modular precedence-logic expert system assigned donor deferrals and unit dispositions. Ten combinations of data accounted for 4,334 records (89%); the remaining 525 records (11%) were distributed among 85 combinations of test data. The expert system correctly assigned all records. Regional blood centers must interpret a growing number of test results, including donations with a complicated pattern of multiple positive screening test results. The distribution of these data is described and the expert system's ability to monitor deferrals and ensure database completeness is demonstrated.

Blood Banks↗
PubMed1988

A prototype system for perinatal knowledge engineering using an artificial intelligence tool.

Though several perinatal expert systems are extant, the use of artificial intelligence has, as yet, had minimal impact in medical computing. In this evaluation of the potential of AI techniques in the development of a computer based "Perinatal Consultant," a "top down" approach to the development of a perinatal knowledge base was taken, using as a source for such a knowledge base a 30-page manuscript of a chapter concerning high risk pregnancy. The UNIX utility "style" was used to parse sentences and obtain key words and phrases, both as part of a natural language interface and to identify key perinatal concepts. Compared with the "gold standard" of sentences containing key facts as chosen by the experts, a semiautomated method using a nonmedical speller to identify key words and phrases in context functioned with a sensitivity of 79%, i.e., approximately 8 in 10 key sentences were detected as the basis for PROLOG, rules and facts for the knowledge base. These encouraging results suggest that functional perinatal expert systems may well be expedited by using programming utilities in conjunction with AI tools and published literature.

Expert Systems↗
PubMed1995

Machine vision-based histometry of premalignant and malignant prostatic lesions.

The implementation of knowledge-guided control of the processing and segmentation of histopathologic images of prostatic lesions has made automated analysis and interpretation possible. To establish correspondence between histopathologic concepts, terms and diagnostic criteria, and computed histometric entities, "interpretive transforms" are introduced. Scene segmentation is controlled by an expert system following a model-based reasoning process. The expert system is structured as an associative network with frames at each node, which controls a knowledge file and a large library of image processing algorithms.

Expert Systems↗
PubMed1992

Towards 3-D modelling of epithelia by computer simulation.

The present work is a preliminary step towards dynamic 3-D modelling by computer graphics simulation of the structure of normal and pathological epithelia, using an expert system. In its present state, Esexsy (Epithelium Simulation by EXpert SYstem) allows the construction, through iterative steps, of a simple 3-D representation of the nasal epithelium, based on the positions, sizes and shapes of nuclei. The iterative process is based on statistical comparisons between distributions of parameter values calculated from real (2-D) histological sections and those issued from an equivalent computer 'section' through the simulated 3-D image. We show the results of attempts at simulating normal, metaplastic and dysplastic states of the nasal epithelium, the latter two being characterized by a progressive architectural disorganization, accompanied by nuclear size/shape alterations. The representation takes into account the size, shape, orientation and spatial arrangement of nuclei, with one or several layers from the basal lamina to the lumen. A modified Poisson point process is used at present to position the nuclei, which are modelled by bi-axial spheroids (from prolate to oblate through spherical), with random orientation and size/shape deviations. It should be possible to use the same computer program to simulate other types of epithelia and to achieve increasingly realistic representations by incorporating, notably, nuclear deformations and chromatin texture.

Computer Graphics↗
PubMed2003

Quantitative structure-activity relationships for predicting mutagenicity and carcinogenicity.

Quantitative structure-activity relationships (QSARs) for predicting mutagenicity and carcinogenicity were reviewed. The QSARs for predicting mutagenicity and carcinogenicity have been mostly limited to specific classes of chemicals (e.g., aromatic amines and heteroaromatic nitro chemicals). The motivation to develop QSARs for predicting mutagenicity and carcinogenicity to screen inventories of chemicals has produced four major commercially available computerized systems that are able to predict these endpoints: Deductive estimation of risk from existing knowledge (DEREK) toxicity prediction by komputer assisted technology (TOPKAT), computer automated structure evaluation (CASE), and multiple computer automated structure evaluation (Multicase). A brief overview of these and some other expert systems for predicting mutagenicity and carcinogenicity is provided. The other expert systems for predicting mutagenicity and carcinogenicity include automatic data analysis using pattern recognition techniques (ADAPT), QSAR Expert System (QSAR-ES), OncoLogic computer optimized molecular parametric analysis of chemical toxicity system (COMPACT), and common reactivity pattern (COREPA).

Animals↗
PubMed1991

Evaluation of UNIS: Urological Nursing Information Systems.

Unis is a nurse expert system prototype specifically designed to assist nurses caring for elderly, incontinent patients residing in nursing homes. Two studies measuring the performance level of UNIS were implemented. In the first study, results of sessions with UNIS on case studies of elderly, incontinent patients were compared to sessions with nurse experts. The relevance of questions, value of recommendations and overall performance were rated by an evaluation panel. In the second study, UNIS was implemented on two nursing units in a nursing home. The number of wet occurrences of patients residing on units where UNIS was consulted by nurses was compared to the number of wet occurrences of patients residing on units where UNIS was not consulted by nurses. The knowledge of urinary incontinence of nurses who consulted UNIS and those who did not consult UNIS were also compared. The results indicate that when judged by an evaluation panel, the relevance of the questions and value of the recommendations generated by UNIS were not rated significantly different than ratings assigned to nurse experts consulting on the same case studies. There was a significant difference between assigned ratings for overall performance; F.01 (4,16) = 10.4. UNIS scored the highest on four out of five case studies. In the second study, the number of wet occurrences of patients residing on units where nurses consulted UNIS decreased significantly; F.01 (2,9) = 34.67. The knowledge of urinary incontinence also improved significantly when nurses' consulted UNIS; F.001 (2,157) = 19.46. The methods and results of these two studies are presented.

Aged↗
PubMed1989

Knowledge-based decision support for diagnosis and therapy: on the multiple usability of patient data.

Expert systems in medicine are frequently restricted to assisting the physician to derive a patient-specific diagnosis and therapy proposal. In many cases, however, there is a clinical need to use these patient data for other purposes as well. The intention of this paper is to show how and to what extent patient data in expert systems can additionally be used to create clinical registries and for statistical data analysis. At first, the pitfalls of goal-oriented mechanisms for the multiple usability of data are shown by means of an example. Then a data acquisition and inference mechanism is proposed, which includes a procedure for controlling selection bias, the so-called knowledge-based attribute selection. The functional view and the architectural view of expert systems suitable for the multiple usability of patient data is outlined in general and then by means of an application example. Finally, the ideas presented are discussed and compared with related approaches.

Decision Support Techniques↗
PubMed1988

Clinical reasoning in nursing.

This paper traces the development of the concept of nursing diagnosis and the various approaches being used to explain the cognitive processes used by practitioners in diagnosing patient problems. Three main types of explanation are compared; hypothesis generation/testing, decision analysis, and the information processing model. The recent development of the latter approach within the field of artificial intelligence is described and expert system research introduced. Finally, the potential benefits of the advent of expert systems into nursing are discussed.

Decision Support Techniques↗
PubMed1996

An artificial intelligence program for the radiologic diagnosis of brain lesions.

A software program has been developed that uses a frame-based expert system for differential diagnosis in neuroradiology. A frame-based expert system is used to store the magnetic resonance (MR) and computed tomographic (CT) imaging characteristics of over 100 known brain disorders in object-like entities. The frames are organized in a hierarchic structure in which lower order frames inherit attributes from higher order frames, with the highest frame containing information that applies to all the other frames. Program execution follows a consultation paradigm with a dynamic database. A decision tree menu provides a user-friendly interface with which to navigate through the network, based on features of the lesion as depicted on MR and CT images. The system can provide a differential diagnosis based on the MR imaging findings alone with information criteria including the signal intensity of the lesion on T1- and T2-weighted images, the location of the lesion, and the presence or absence of mass effect. The differential diagnosis may be further refined by adding CT-related information, including CT attenuation and the presence or absence of calcification and contrast enhancement.

Brain↗
PubMedDate not supplied

Knowledge-based understanding of radiology text.

A data acquisition tool which will extract pertinent diagnostic information from radiology reports has been designed and implemented. Pertinent diagnostic information is defined as that clinical data which is used by the HELP medical expert system. The program uses a memory-based semantic parsing technique to 'understand' the text. Moreover, the memory structures and lexicon necessary to perform this action are automatically generated from the diagnostic knowledge base by using a special purpose compiler. The result is a system where data extraction from free text is directed by an expert system whose goal is diagnosis.

Computer-Assisted Instruction↗