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At least 307 records · Page 17Linked to original sources
PubMed1998

An expert system for EEG monitoring in the pediatric intensive care unit.

OBJECTIVES: was to design a warning system for the pediatric intensive care unit (PICU). The system should be able to make statements at regular intervals about the level of abnormality of the EEG. The warnings are aimed at alerting an expert that the EEG may be abnormal and needs to be examined. METHODS: A total of 188 EEG sections lasting 6 h each were obtained from 74 patients in the PICU. Features were extracted from these EEGs, and with the use of fuzzy logic and neural networks, we designed an expert system capable of imitating a trained EEGer in providing an overall judgment of abnormality about the EEG. The 188 sections were used in training and testing the system using the rotation method, thus separating training and testing data. RESULTS: The EEGer and the expert system classified the EEGs in 7 levels of abnormality. There was concordance between the two in 45% of cases. The expert system was within one abnormality level of the EEGer in 91% of cases and within two levels in 97%. CONCLUSIONS: We were therefore able to design a system capable of providing reliably an assessment of the level of abnormality of a 6 h section of EEG. This system was validated with a large data set, and could prove useful as a warning device during long-term ICU monitoring to alert a neurophysiologist that an EEG requires attention.

Algorithms↗
PubMed1989

An expert system for early detection of cancer of the breast.

In order to study the relevancy of exploring complex expert systems for use by the lay public, an expert system for the early detection of cancer of the breast has been developed. The system consists of a conversion between a microcomputer and a woman who is anxious about breast cancer. The conversation is divided into two parts: one is listening to the woman's symptoms regarding the breast then giving advice, the other is an explanation of breast cancer and how to detect it in its early stages. After listening to the woman's symptoms, the computer presents its conclusion and suggests courses of action the woman should take. The system is written in Prolog. It was tried by women in a small town at a festival for health, by female patients who visit our hospital, and received a positive reaction.

Algorithms↗
PubMed1989

The PARTICLE expert system for tumor grading by automated image analysis.

While automated microscopic image analysis of histologic sections has been helpful in objectivizing histologic tumor grading and investigating the relationships between grading and prognosis, expert systems have the potential of linking image analysis and other data for statistical analysis and application to a wider range of tumors. One such expert system is PARTICLE, whose development was based on many years of experience in resolving histologic problems by image analysis. This paper discusses the philosophy of expert system for tumor grading and describes its implementation in the PARTICLE system. The system's structure, operations and applications are briefly presented. PARTICLE is essentially based on the evaluation of karyometric data.

Cell Nucleus↗
PubMed2004

An expert system for chemical speciation of individual particles using low-Z particle electron probe X-ray microanalysis data.

An electron probe X-ray microanalysis (EPMA) technique, using an energy-dispersive X-ray detector with an ultrathin window, designated a low-Z particle EPMA, has been developed. The low-Z particle EPMA allows the quantitative determination of concentrations of low-Z elements, such as C, N, and O, as well as chemical elements that can be analyzed by conventional energy-dispersive EPMA, in individual particles. Since a data set is usually composed of data for several thousands of particles in order to make environmentally meaningful observations of real atmospheric aerosol samples, the development of a method that fully extracts chemical information contained in the low-Z particle EPMA data is important. An expert system that can rapidly and reliably perform chemical speciation from the low-Z particle EPMA data is presented. This expert system tries to mimic the logic used by experts and is implemented by applying macroprogramming available in MS Excel software. Its feasibility is confirmed by applying the expert system to data for various types of standard particles and a real atmospheric aerosol sample. By applying the expert system, the time necessary for chemical speciation becomes shortened very much and detailed information on particle data can be saved and extracted later if more information is needed for further analysis.

Journal Article↗
PubMed1995

[MEDRISK--an expert system for medical risk assessment].

The Munich Reinsurance Company has developed a rule-based expert system for assessing substandard risk in life, disability and accidental death benefit. It is one of the most comprehensive medical expert systems yet conceived and currently includes entries for over 7500 impairment terms. Based on the most up-to-date insurance medical knowledge MEDRISK allows underwriters, irrespective of their level of experience, to process both simple and highly complex cases. The system which takes account of the interactive effect that can exist between different impairments as well as the influence which occupational factors can exert, always produces consistent and case-specific decisions. The number of impairments and types of insurance included in MEDRISK can be expanded. After tests at Munich Re and at a number of insurance companies, the system ist now ready to be launched in German speaking markets.

Artificial Intelligence↗
PubMed1996

[The application of the expert system in prosthedontics].

In this study, we developed an expert system for designing of frame work of Removable Partial Denture by the method of Artificial Intelligence. Both of Quick Basic and Tuber C language are used in this program. This system consider more other relative factors in removable partial denture designing except the amount and position of missing teeth. Base the idea of multy-layer knowledge we divide the process RPD designing into different layers according to their logical relationships. So, this system could imitate better the expert of prosthedontics to do the clinical examination, diagnosis, treatment plan and denture designing. There are 11 modules in this system totally. They are used to accomplish the case register, first visit examination, treatment plan, abutment examination, denture designing, biomechanic analysis of abutment, reference, explanation and etc. We used this system for 20 partial edentulous patients and compared the results with the designing plans of expert of prosthedontics, the rate of conformity is 83.6%.

Denture, Partial, Removable↗
PubMed2005

Telephone triage, expert systems and clinical expertise.

This paper reports on a qualitative study of the use of an expert system developed for the British telephone triage service NHS Direct. This system, known as CAS, is designed to standardise and control the interaction between NHS Direct nurses and callers. The paper shows, however, that in practice the nurses use CAS in a range of ways and, in so doing, privilege their own expertise and deliver an individualised service. The paper concludes by arguing that NHS Direct management's policy of using CAS as a means of standardising service delivery will achieve only limited success due not only to the professional ideology of nursing but also to the fact that rule-based expert systems capture only part of what 'experts' do.

England↗
PubMedDate not supplied

EXTEND: a prototype expert system for teaching nursing diagnosis.

In this article, the development and uses of EXTEND (Expert system for Education in Nursing Diagnosis), a computer assisted instruction package for teaching nursing diagnosis using the categories proposed by the North American Nursing Diagnosis Association, are described. EXTEND provides a knowledge base that is able to deduce a nursing diagnosis from case factors and extends the normal use of expert systems beyond diagnosis to diagnosis education by providing answers to student questions about a simulated client and by guiding them through the diagnostic task. Furthermore, EXTEND allows the teacher to record the steps taken by students in the assessment and diagnosis of the simulated client, and use this as the focus of further discussion. The system is a response to the problems of teaching nursing diagnoses cited in the professional literature.

Computer-Assisted Instruction↗
PubMed1991

Expert systems and the clinical laboratory information system.

Many of the tasks of the clinical laboratory involve the application of knowledge and experience to patient-specific problems. Expert systems that have ready access to patient data and expert knowledge have the potential to support many laboratory processes, especially those that involve the repetitive and reasonably predictable application of knowledge. Quality assurance and utilization improvement efforts requiring constant vigilance and surveillance of an array of laboratory processes especially may benefit. Expert system technologies, trends of their use in the clinical laboratory, their role in the total testing process, and their potential for influencing physician use of the laboratory through timely feedback are discussed.

Clinical Laboratory Information Systems↗
PubMed1989

SESAM-DIABETE, an expert system for insulin-requiring diabetic patient education.

SESAM-DIABETE is an interactive educational expert system that provides personalized advice and therapeutic recommendations for insulin-requiring diabetic patients. Because of its sophisticated explanation facilities, this system is intended to complete the more traditional educational tools for diabetic patients. It has been developed using an original essential expert system, namely SESAM, itself implemented in an upper-layer of Lisp, MBX. Its control structure uses a top-down strategy to solve a problem; i.e., it decomposes the current problem into subproblems easier to solve, this method being recursively applied to each subproblem. All information about patients is kept in a Patient Medical Record, which allows their follow-up. This system is currently available from their home for selected patients through the French telematic network TELETEL and is under clinical evaluation.

Computer Communication Networks↗
PubMedDate not supplied

Expert systems as computer assisted instruction systems for nursing education and training.

The ever-increasing specialization of nursing care may require nursing professionals to provide nursing care outside of their specialty. Nurses will have to familiarize themselves with a new specialization area at short notice. Fortunately, expert-systems technology can prove particularly helpful in achieving this familiarity. As such, this technology can prove a valuable tool for education and training of nursing professionals and students. This article describes the unique edge that expert systems technology provides in this context. Using VP-Expert, an expert system shell, the authors present two examples to illustrate the development of a computer aided instruction system and the unique benefits such a system offers.

Computer-Assisted Instruction↗
PubMed1989

[An experimental expert system for the differential diagnosis of the dementia syndrome. Initial evaluation].

EVINCE-I is an expert system for neuropsychiatric diagnostics implemented on a personal computer. It is developed to assess the possibilities of an expert system which models the procedures used for medical decision taking and data analysis by a neuro-psychiatrist when making a differential diagnosis in the domain of dementia syndromes. The knowledge of the system centers around detection of Alzheimer's disease, multi-infarct dementia and depression-induced dementia. EVINCE-I and the human expert were compared in their ability to diagnose 29 patients, i.e. 19 patients with early stages of dementia and 10 patients showing varying disorders except dementia. It is shown that EVINCE-I is able to detect dementia and its main causes, and produce diagnoses that agree with those of the human expert.

Aged↗
PubMed1999

The influence of an expert system for test ordering and interpretation on laboratory investigations.

BACKGROUND: The Laboratory Advisory System (LAS) is an expert system interface that works interactively with clinicians to assist them with test selection and result interpretation throughout the laboratory investigation of a patient. METHODS: To study the influence of the LAS on laboratory investigations, a repeated-measures experiment using clinical vignettes was conducted. To collect baseline data on how laboratory investigations are currently conducted, clinicians investigated one-half of the vignettes using a conventional (noncomputer) approach. To determine the influence of the LAS on clinicians' behavior, the other half of the vignettes were investigated using the LAS. RESULTS: Clinicians using the LAS (compared with conventional practice) ordered fewer laboratory tests during the diagnostic process (mean, 17.8 vs 32.7), completed the diagnostic workup with fewer sample collections (mean, 5.8 vs 7.5), generated lower laboratory costs (mean, $194 vs $232), shortened the time required to reach a diagnosis (mean, 1 day vs 3.2 days), showed closer adherence to established clinical practice guidelines, and exhibited a more uniform and diagnostically successful investigation. CONCLUSION: The LAS enhances the outcome of the investigation and improves laboratory utilization.

Clinical Laboratory Information Systems↗
PubMed2000

Validation of ICTERUS, a knowledge-based expert system for Jaundice diagnosis.

The study aimed to describe an example of the assessment and validation of knowledge-based clinical expert systems. The paper focuses on ICTERUS, an expert system for jaundice diagnosis. It describes system design, the methodology applied for upgrading and validating the program, and the most important outcomes of the validation procedure. The clinical validation of the system on a very large European database (Euricterus Project) shows that diagnostic conclusions are reliable in about 70% of eligible cases. This figure appears acceptable for a system which provides decision support only on the basis of clinical data, assuming that the final decision is achieved under user responsibility. Expected biases, limitations and inconsistencies in the practical application of the system are discussed.

Analysis of Variance↗
PubMed1988

The introduction of expert systems in animal husbandry.

The intensive nature of modern animal husbandry has changed the environmental conditions, so that protection against diseases, especially those caused by secondary pathogenic organisms, becomes an increasing concern. Production inefficiencies become more and more important. Because of their complex nature, many associated problems cannot be solved by farmers alone. With the introduction of information technology in the form of expert systems, new possibilities emerge for maximal productivity and animal health. Expert systems are a new type of computer programmes which are able to handle problems in limited domains at an expert level, using expert knowledge and reasoning processes, within strictly defined problem limits. Expert systems as they presently exist have the following components: a knowledge base (rule- and data-base), inference engine (deductive mechanism), user-interface (explanatory facilities, etc.). The application of knowledge engineering in animal husbandry may result in improvements in the general health and productivity of the herd and the financial returns of the farm, derived from the improved managerial control. At the same time the use of knowledge engineering may lead to greater understanding of the various epidemiological factors that influence farm results, either positively or negatively.

Animal Husbandry↗
PubMed1988

An expert system for determining Medicaid eligibility.

The eligibility requirements for AFDC Medicaid are so extensive and complicated that most health care providers do not attempt to ascertain whether or not a particular patient is eligible for the program, even when no other source of payment is available. This results in lost revenue for health service providers nationwide amounting to hundreds of millions of dollars per year. Computer technology, in the form of expert systems, offers an opportunity to rationalize the Medicaid eligibility determination process and to do real-time assessments of patient eligibility. This article presents an expert system called MEDELEX (MEdicaid ELigibility EXpert) for determining Medicaid eligibility. The program (when run on an 8 MHz MS-DOS microcomputer with at least 640 KB of RAM) requires about 20 min for data entry and 5 sec for the actual eligibility determination. The expert system was written in Prolog and has been designed in such a way that it can be readily modified to take into account the state-to-state variability in eligibility requirements for AFDC Medicaid.

Aid to Families with Dependent Children↗
PubMed1991

[An expert system for the diagnosis of risk of ischemic heart disease].

The RIBS expert system was created by using the means and tools of the OBSES software complex (an expert system shell). RIBS is designed for determining the risk level in the occurrence of ischemic heart disease. The knowledge base is structured and is broken down into 13 hierarchical clusters. Three types of attributes are used for knowledge base cluster representation. To calculate the significance of facts and rules, the methods of polar and score ratings and others are used. A consultation fragment report with delivery of ischemic heart disease risk integral value is provided.

Coronary Disease↗