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Results for “Expert Systems”

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At least 343 records · Page 19Linked to original sources

PubMed1997

Parameter evaluation of the differential diagnosis of female urinary incontinence for the construction of an expert system.

Female urinary incontinence is a difficult problem for a patient but also for a physician. In the differential diagnosis of female urinary incontinence the physician has to determine a diagnostic class for the patient. This task is complex because of the unreliable patient history and the overlapping class boundaries. In order to develop an expert system to help the physician, a retrospective investigation on the incontinent women was performed to detect the potential expert system parameters. Also a diagnosis table was constructed from the expected values of parameters and the diagnostic classes. The results from K-means cluster analysis indicate that it is possible to develop the expert system on basis of the defined parameters and classes.

Adolescent↗

PubMed1999

Evaluation of pediatric nephropathies by a computerized Urine Protein Expert System (UPES).

A computerized Urine Protein Expert System (UPES) measuring creatinine, total protein, albumin, IgG, alpha(1)-microglobulin, alpha(2)-macroglobulin, and N-acetyl-beta-D-glucosaminidase, together with urine dipstick testing for granulocyte esterase and hemoglobin pseudoperoxidase, and measurement of serum creatinine had been found to be useful in adults for differentiating between renal disorders. The objective of this study was to investigate UPES for identifying the different types of proteinuria and their underlying prerenal, glomerular, tubular, and postrenal causes in 146 children characterized by routine and invasive nephrological investigations. UPES proved to be a useful tool in pediatric renal patients after refinements were implemented in the program. Comparing UPES with the pediatric nephrologist's interpretation of all available clinical and laboratory data, UPES diagnosed glomerulopathies in 46 (75%) of 61 patients. In a further 23% it suggested glomerular involvement by indicating either a disturbed glomerular permeability or increased excretion of albumin. Tubular proteinuria was correctly described by UPES in 23 (100%) patients with different tubulopathies. UPES revealed normal kidney function in all healthy children and all children with remission of renal disorders. Therefore, UPES can be regarded as a useful tool in the automated differentiation of renal diseases in children.

Adolescent↗

PubMed2006

[Assessment of giant panda habitat based on integration of expert system and neural network].

To conserve giant panda effectively, it is important to understand the spatial pattern and temporal change of its habitat. Mapping is an effective approach for wildlife habitat evaluation and monitoring. The application of recently developed artificial intelligence tools, including expert systems and neural networks, could integrate qualitative and quantitative information for modeling complex systems, and built the information into a GIS, which could be helpful for giant panda habitat mapping. This study built a mapping approach for giant panda habitat mapping, which integrated expert system and neural network classifiers (ESNNC), and used multi-type data within GIS. The giant panda habitat types and their suitability were mapped by ESNNC. The results showed that the habitat types and their suitability in Foping Nature Reserve were assessed with a higher accuracy (> 80 %) by ESNNC, compared with non-integrated classifiers, i. e., expert system, neural network, and maximum likelihood. Z-statistic test showed that ESNNC was significantly better than the other three non-integrated classifiers. It was recommended that the integrated approach could be widely applied into wildlife habitat assessment.

Animals↗

PubMed1989

[Education of patients with arterial disease. A plan for the use of Expert System in angiology via Minitel].

We are presenting a computer program requiring the use of an Expert System accessible by Minitel, intended for patients with arterial disease and their attending physician. The objective is not to make a diagnosis, but to train the patients in applying health measures to the handling and prevention of his/her disease. While remaining anonymous, the patient may consult the program at home, without any time constraint. Only the patient's physician can authorize him/her to have access to the program. The Expert System may adjust the answers to the patients according to the past history and recent data. Contrary to a computer program, new knowledge does not impair the functioning of the system. The physician has access to simplified modules regarding his patient, and specific modules regarding the treatment. Such a system would help general practitioners in following his patient and would facilitate the Doctor-Patient relationship during consultations.

Arterial Occlusive Diseases↗

PubMed1991

SARAH: an expert system for training non-hygienists in carrying out occupational hygiene assessments.

The London Research Station of British Gas plc has developed an expert system as a training aid for managers responsible for assessments of the risks to health from exposure to harmful substances. SARAH (System for Advising on the Regulations for Assessing Hazards) uses an expert system shell with hypertext capabilities and runs on IBM compatible personal computers. Expert advice on occupational hygiene is not readily available: SARAH is therefore of value since it can free an experienced occupational hygienist for more complex tasks. It is used by British Gas, helping the company to comply with the requirements of the COSHH (Control Of Substances Hazardous to Health) Regulations.

Computer-Assisted Instruction↗

PubMed1995

Development and retrospective evaluation of Hepaxpert-I: a routinely-used expert system for interpretive analysis of hepatitis A and B serologic findings.

Hepaxpert-I is an expert system that interprets the results of routine serologic tests for infection with hepatitis A or B virus. The tests measure antibody to the hepatitis A virus (anti-HAV), IgM antibody to the hepatitis A virus (IgM anti-HAV), hepatitis A virus (HAV) in the stool, hepatitis B surface antigen (HBsAg) and antibody (anti-HBs), antibody to hepatitis B core antigen (anti-HBc and IgM anti-HBc), and hepatitis B envelope antigen (HBeAg) and antibody (anti-HBe). The knowledge base of Hepaxpert-I contains 13 If-Then rules for hepatitis A and 106 If-Then rules for hepatitis B serology. Formally, knowledge acquisition was done by forming a partition of each of the two sets of possible serologic finding patterns that contain patterns of serologic test results, 64 for hepatitis A and 4096 for hepatitis B, respectively. After entering an input pattern of serologic test results in Hepaxpert-I, a rule pattern matching algorithm based on indexing is internally employed as efficient access method for providing the respective interpretive text. Since 1 September 1989, Hepaxpert-I has been routinely applied at the Hepatitis Serology Laboratory of the 2nd Department of Gastroenterology and Hepatology at the University of Vienna Medical School (Vienna General Hospital). Beforehand, a retrospective evaluation of the expert system based on 23,368 hepatitis A and 24,071 hepatitis B serology requests was carried out.

Expert Systems↗

PubMed2002

Expert system support using a Bayesian belief network for the classification of endometrial hyperplasia.

Accurate morphological classification of endometrial hyperplasia is crucial as treatments vary widely between the different categories of hyperplasia and are dependent, in part, on the histological diagnosis. However, previous studies have shown considerable inter-observer variation in the classification of endometrial hyperplasias. The aim of this study was to develop a decision support system (DSS) for the classification of endometrial hyperplasias. The system used a Bayesian belief network to distinguish proliferative endometrium, simple hyperplasia, complex hyperplasia, atypical hyperplasia and grade 1 endometrioid adenocarcinoma. These diagnostic outcomes were held in the decision node. Four morphological features were selected as diagnostic clues used routinely in the discrimination of endometrial hyperplasias. These represented the evidence nodes and were linked to the decision node by conditional probability matrices. The system was designed with a computer user interface (CytoInform) where reference images for a given clue were displayed to assist the pathologist in entering evidence into the network. Reproducibility of diagnostic classification was tested on 50 cases chosen by a gynaecological pathologist. These comprised ten cases each of proliferative endometrium, simple hyperplasia, complex hyperplasia, atypical hyperplasia and grade 1 endometrioid adenocarcinoma. The DSS was tested by two consultant pathologists, two junior pathologists and two medical students. Intra- and inter-observer agreement was calculated following conventional histological examination of the slides on two occasions by the consultants and junior pathologists without the use of the DSS. All six participants then assessed the slides using the expert system on two occasions, enabling inter- and intra-observer agreement to be calculated. Using unaided conventional diagnosis, weighted kappa values for intra-observer agreement ranged from 0.645 to 0.901. Using the DSS, the results for the four pathologists ranged from 0.650 to 0.845. Both consultant pathologists had slightly worse weighted kappa values using the DSS, while both junior pathologists achieved slightly better values using the system. The grading of morphological features and the cumulative probability curve provided a quantitative record of the decision route for each case. This allowed a more precise comparison of individuals and identified why discordant diagnoses were made. Taking the original diagnoses of the consultant gynaecological pathologist as the 'gold standard', there was excellent or moderate to good inter-observer agreement between the 'gold standard' and the results obtained by the four pathologists using the expert system, with weighted kappa values of 0.586-0.872. The two medical students using the expert system achieved weighted kappa values of 0.771 (excellent) and 0.560 (moderate to good) compared to the 'gold standard'. This study illustrates the potential of expert systems in the classification of endometrial hyperplasias.

Bayes Theorem↗

PubMed1999

Otoneurological expert system for vertigo.

We have developed an otoneurological expert system (ONE) to aid the diagnostics of vertigo, to assist teaching and to implement a database for research. The ONE database is set to harvest data on patient history, signs and test results necessary for diagnostic work with vertiginous patients. A method based on pattern recognition was used in the reasoning process. Questions about symptoms, signs and test results are weighted and scored for each disease and the most likely disease is recognized from defined disease profiles. Missing information and uncertainties are solved with a method resembling fuzzy logic. ONE was validated by comparing diagnoses assessed by physicians with those provided by the system. It proved to be a valid decision-maker by solving 65% of the cases correctly, while the physicians' mean was 69%. To improve ONE further, a follow-up should be implemented for the patients, since diagnosing sudden deafness and Meniere's disease during the first visit is often impossible. We aim to obtain new information on diseases involving vertigo by applying adaptive computer applications, such as genetic algorithms, to the reasoning process.

Algorithms↗

PubMed1995

Reasoning in expert system ONE for vertigo work-up.

An otoneurological expert system (ONE) was developed to help collect data and diagnose the work-up of vertigo of both central and peripheral diseases causing vertigo. Patient history and otoneurological and other examination results are used in the reasoning process. The history is interactively collected and is complemented with clinical examination results. The case history data can be either mandatory or supportive. Mandatory questions are used to confirm a diagnosis, and conflicting answers are used to reject an unlikely disease. Supportive questions support or suppress a diagnosis, but their presence is not obligatory. The reasoning procedure of ONE scores every question independently for different diagnoses, depending on how well they agree with the symptom entity of a disease. Diagnostic criteria are set for each disease, in Meniere's disease, for example, the full triad is required. Graphic displays illustrate the linear and nonlinear correlation between the symptoms and diseases. For instance, both second-long Tumarkin-type attacks and attacks lasting hours give a high score while intermediately long attacks score much lower in Meniere's disease. To be able to take even rare diseases into consideration we try to diminish the possibility of a wrong decision rather than maximize the likelihood of reaching only one right decision.

Artificial Intelligence↗

PubMed1986

Feasibility study concerning the use of expert systems for the development of procedures in pharmaceutical analysis.

The feasibility of using expert systems for the development of analytical procedures is investigated. A system for the computer generation of procedures to determine active drug substances in commercial formulations is proposed. It is shown that in nearly 85% of the cases investigated the present system immediately yields a correct procedure or conclusion. It is concluded that selecting methods and developing procedures with the use of expert systems is difficult but feasible.

Journal Article↗

PubMed2000

Acceptability of a stage-matched expert system intervention to increase condom use among women at high risk of HIV infection in New York City.

There is an urgent need to develop and implement effective methods for sexual behavior change to curb the spread of HIV. Condoms remain one of the most effective strategies for achieving this, yet consistent condom use is generally low, especially among those at highest risk. This article describes the acceptability of an interactive computer-based expert system designed to increase condom use in women at high risk of HIV infection. The expert system is based on the transtheoretical stages of change model. Using a computer, participants respond to questions about their attitudes and behavior toward using condoms and receive immediate feedback which is matched to their readiness to use condoms. The women were found to be at all stages of change for condom use, although a large proportion of the women (42%) were at early stages of behavior change because they were considering but not using condoms every time during sex with men. The expert system was found to be acceptable to this high-risk group of women. They almost unanimously agreed that they found the feedback useful, would return to use the system again, and would recommend it to a friend. These findings indicate that traditional intervention strategies which assume individuals are ready to use condoms consistently would be appropriate for only about one third of these women, underscoring the importance and potential utility of stage-matched interventions.

Adolescent↗

PubMed1999

REF Select: expert system software for selecting restriction endonucleases for restriction endonuclease fingerprinting.

REF Select, expert system software, has been developed to assist in the selection of optimal restriction endonucleases for restriction endonuclease fingerprinting (REF), a method for rapid and sensitive mutation screening of long DNA segments (1-2 kb). The REF method typically involves six separate digestions with up to two restriction endnonucleases used in each digestion. If done manually, performing a comprehensive review of the large number of possible sets of restriction endonucleases that could be used (over 10(19) in the example presented here) and making an optimal choice is not feasible. Furthermore, the typical nonoptimal manual selection takes approximately 8 h by someone experienced with REF. REF Select enables a comprehensive review of the possible sets and a consistent, objective and fast selection of an optimal set by using a two-step strategy: the selection of sets that meet specific constraints, which is followed by a ranking of those sets by an optimality score. Based on our experience with REF, we chose default selection and ranking parameters to help the user get started quickly. These parameters form a knowledge base that can be customized and then saved by the user. In conclusion, REF Select facilitates the general application of REF by serving as an expert system for the selection of optimal restriction endonucleases. We demonstrated REF Select using an example segment from the human p53 gene.

Algorithms↗

PubMed1991

[Expert systems and antibiotic sensitivity test].

Artificial intelligence is a part of computer science that deals with programs mimicking intelligence of man. Artificial intelligence is now used to check the quality of the determination of antibiotics susceptibility of bacteria. This application is useful because antibiotic susceptibility is subject to biological and technical variation that have to be detected. Three types of reasoning are used either by the biologist or by expert systems: low level quality checking dealing with individual results, microbiological interpretation of the whole set of results and medical interpretation of the results. The use of artificial intelligence in these fields is sustained by the structured nature of the knowledge. Two type of expert systems are already of routine use, either based on production rules (ATB plus EXPERT, bioMerieux, La Balme-les-Grottes, France and SIR, 12A, Montpellier, France), or on object-oriented representation of the knowledge (EXPRIM from our laboratory). The main problem is, as usually in artificial intelligence applications, to transfer human expertise into an adapted knowledge base. The advantage of experts systems over man are their reproducibility of answer and their availability.

Artificial Intelligence↗

PubMed1999

An expert system for monitor alarm integration.

OBJECTIVE: Intensive care and operating room monitors generate data that are not fully utilized. False alarms are so frequent that attending personnel tends to disconnect them. We developed an expert system that could select and validate alarms by integration of seven vital signs monitored on-line from cardiac surgical patients. METHODS: The system uses fuzzy logic and is able to work under incomplete or noisy information conditions. Patient status is inferred every 2 seconds from the analysis and integration of the variables and a unified alarm message is displayed on the screen. The proposed structure was implemented on a personal computer for simultaneous automatic surveillance of up to 9 patients. The system was compared with standard monitors (SpaceLabs PC2), using their default alarm settings. Twenty patients undergoing cardiac surgery were studied, while we ran our system and the standard monitor simultaneously. The number of alarms triggered by each system and their accuracy and relevance were compared. Two expert observers (one physician, one engineer) ascertained each alarm reported by each system as true or false. RESULTS: Seventy-five percent of the alarms reported by the standard monitors were false, while less than 1% of those reported by the expert system were false. Sensitivity of the standard monitors was 79% and sensitivity of the expert system was 92%. Positive predictive value was 31% for the standard monitors and 97% for the expert system. CONCLUSIONS: Integration of information from several sources improved the reliability of alarms and markedly decreased the frequency of false alarms. Fuzzy logic may become a powerful tool for integration of physiological data.

Blood Gas Analysis↗

PubMed1995

Validation of expert systems: examples and considerations.

The problem of a medical expert system validation is generally complex. It requires a rigorous methodology of validation and must show proof of its practical competency in order to be used currently. Validation concerns the quality of conclusions provided by the system, the quality of the deductive process leading to these conclusions as well as the validity of its utilization. In this paper, some reflections, questions, and requirements are exposed that must be addressed to proceed to the validation of a knowledge base system in the field of medicine, especially the psychiatric field.

Computer Systems↗

PubMed1997

Health-2000: an integrated large-scale expert system for the hospital of the future.

Decision making and management are problems which plague health systems in developing countries, particularly in Sub-Saharan Africa where there is significant waste of resources. The need goes beyond national health management information systems, to tools required in daily micro-management of various components of the health system. This paper describes an integrated expert system, Health-2000, an information-oriented tool for acquiring, processing and disseminating medical knowledge, data and decisions in the hospital of the future. It integrates six essential features of the medical care environment: personnel management, patient management, medical diagnosis, laboratory management, propharmacy, and equipment management. Disease conditions covered are the major tropical diseases. An intelligent tutoring feature completes the package. Emphasis is placed on the graphical user interface to facilitate interactions between the user and the system, which is developed for PCs using Pascal, C, Clipper and Prolog.

Expert Systems↗

PubMed1993

Design and integration of a graphic interface for an expert system in oncology.

We describe a graphic user interface for an expert system in oncology. The main objectives of our work has been to facilitate the adaptation of the system to different clinical environments and potentiate the factors which more directly determine the acceptance of the system by its users. We present the design principles derived from the features of the clinical domain chosen and from the objectives of the system. These principles are reflected on the design of the screen and of the interaction and in the style of integration of the interface with the other components of the system. Underlying the application we describe is a graphic user interface management system which provides facilities for the fast prototyping and integration of interfaces. We describe here those features of this tool which make the practical application of the design principles we consider possible.

Clinical Protocols↗

PubMed2005

Stage-based expert systems to guide a population of primary care patients to quit smoking, eat healthier, prevent skin cancer, and receive regular mammograms.

BACKGROUND: Treating multiple health behavior risks on a population basis is one of the most promising approaches to enhancing health and reducing health care costs. Previous research demonstrated the efficacy of expert system interventions for three behaviors in a population of parents. The interventions provide individualized feedback that guides participants through the stages of change for each of their risk behaviors. This study extended that research to a more representative population of patients from primary care practice and to targeting of four rather than three behaviors. METHODS: Stage-based expert systems were applied to reduce smoking, improve diet, decrease sun exposure, and prevent relapse from regular mammography. A randomized clinical controlled trial recruited 69.2% of primary care patients (N = 5407) at home via telephone. Three intervention contacts were delivered for each risk factor at 0, 6, and 12 months. The primary outcome measures were the percentages of at-risk patients at baseline who progressed to the action or maintenance stages at 24-month follow-up for each of the risk behaviors. RESULTS: Significant treatment effects were found for each of the four behaviors, with 25.4% of intervention patients in action or maintenance for smoking, 28.8% for diet, and 23.4% for sun exposure. The treatment group had less relapse from regular mammography than the control group (6% vs. 10%). CONCLUSION: Proactive, home-based, and stage-matched expert systems can produce relatively high population impacts on multiple behavior risks for cancer and other chronic diseases.

Adult↗