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At least 523 records · Page 29Linked to original sources
PubMed2003

[An expert system for controlling the physical training program of crews on long-term space missions].

The concept of in-flight expert system for controlling (ESC) the physical training program during extended, including Martian, space missions has been developed based on the literature dedicated to the microgravity countermeasures and a retrospective analysis of effectiveness of the known ESC methods. This concept and the principle of crew autonomy were used as prime assumptions for defining the structure of ESC-based training in long-duration and planetary missions.

Astronauts↗
PubMed1994

Real-time expert system for advising anesthesiologists in the cardiac operating room.

This paper describes the initial work towards building a distributed real-time expert system for advising anesthesiologists in the cardiac operating room. The goal of this project is to build a vigilant system that contains knowledge relevant to the practice of cardiac anesthesiology. The system is being designed to use this knowledge in conjunction with continuous automated patient data acquisition in order to provide clinically useful differential diagnoses and treatment recommendations in real time.

Anesthesiology↗
PubMed1984

An expert system which critiques patient workup: modeling conflicting expertise.

PHEO-ATTENDING is an expert system designed to critique a physician's workup of a patient with a suspected pheochromocytoma. To use PHEO-ATTENDING, a physician describes his patient and indicates any tests or procedures which he has ordered or plans to order and any results already obtained. PHEO-ATTENDING then critiques the workup, discussing its appropriateness for the patient described, and mentioning any other approaches which might be preferred. The goal is to explore the design issues involved in building a computer system which critiques patient workup. A major focus is how best to incorporate conflicting expertise, which occurs when domain experts themselves advocate different approaches to a problem.

Adrenal Gland Neoplasms↗
PubMed1998

"EXPATH" an expert system for pathology for the diagnosis of jaundice.

Computers are increasingly playing an important role in the field of medicine. A computer aided expert system has been developed for the differential diagnosis of jaundice. In this article the major features and application of this modular software are discussed.

Diagnosis, Computer-Assisted↗
PubMedDate not supplied

Expert system computer program for troubleshooting the gel-clot Limulus amebocyte lysate (LAL) assay.

This article describes a knowledge-based or expert system computer program that can assist in troubleshooting the gel-clot Limulus Amebocyte Lysate (LAL) method for the assay of endotoxin in parenteral products and in-process materials. The program may also be used to train new personnel and/or to assist in the development of an LAL method for a new product or material. The program includes LAL assay information for 31 parenteral products and materials. The program uses rules-of-thumb and intuition to diagnose 155 possible LAL assay problems and logically reduce them to 37 recommended problem solving processes. The program also provides references to support its recommendation.

Endotoxins↗
PubMed1996

[The development of expert systems for forensic psychiatric evaluation].

Possible methodological approaches to elaboration of the expert systems in forensic psychiatry are presented. These systems are intended for doubtful "borderline" cases. The comparison of psychopathologic parameters with peculiarities of criminal situation (character of the delinquency, the role of the examined person, other factors) is the base of such approach. The advantage of clinical logical method over the mathematical one at the present stage of forensic psychiatry development is demonstrated.

Expert Testimony↗
PubMed1994

An expert system for the analysis and interpretation of evoked potentials based on fuzzy classification: application to brainstem auditory evoked potentials.

EPEXS is an expert system for evoked potential analysis and interpretation (a medical examination performed in clinical neurophysiology laboratories), working from available clinical records and numerical data extracted from evoked potential traces. EPEXS integrates two formalisms of knowledge representation: rules and structured objects. The rules represent the elementary concepts (shallow knowledge) and include a model of possibility based on the Dubois and Prade default reasoning and possibility theory. The structured objects (prototypes) are organized as hierarchical taxonomies (underlying knowledge). These allow the description of both the objects and their relationships. The heuristics used to interpret knowledge are based on two hypotheses: the unicity of the pathological process leading to several given symptoms and the progression from the general to the specific, leading to the adoption or rejection of a class of diagnoses. This avoids the problem of the differential diagnosis. These sources of knowledge are used in a dynamical way that could be described as a four-step process: acquisition of clinical data in order to define the nosological frame of the pathology, production of hypotheses about the nature and topography of lesions, interpretation of data in accordance with these hypotheses, and finally evaluation of their likelihood. The validation shows that EPEXS topographic diagnoses were correct in 100% of cases and 92% of it nosologic diagnoses were correct, and no pathological record was interpreted as normal. When examined on a given pathology basis EPEXS was not significantly different from human experts as regards to performance, specificity, and sensitivity.

Computer Simulation↗
PubMed1997

Computational predictive programs (expert systems) in toxicology.

The increasing number of pollutants in the environment raises the problem of the toxicological risk evaluation of these chemicals. Several so called expert systems (ES) have been claimed to be able to predict toxicity of certain chemical structures. Different approaches are currently used for these ES, based on explicit rules derived from the knowledge of human experts that compiled lists of toxic moieties for instance in the case of programs called HazardExpert and DEREK or relying on statistical approaches, as in the CASE and TOPKAT programs. Here we describe and compare these and other intelligent computer programs because of their utility in obtaining at least a first rough indication of the potential toxic activity of chemicals.

Animals↗
PubMed1984

An expert system for the evaluation of liver functional assessment.

The paper describes an expert system for the assessment of the liver function. Since the system must act as an intelligent assistant for a general physician, a major emphasis has been laid upon its interactive capabilities. In particular, the user can ask the system how a given conclusion has been reached (explanation facilities) and can alter the normal operation flow. In the design of the system, the different significance and availability of the clinical data and the laboratory tests have been taken into account: The investigations about a given patient start from the clinical data, and only when there is some evidence of hepatopathy are the results of some laboratory tests requested. The final result of the investigations consists of the assessment of the liver function in terms of four aspects (biosynthesis, cholestasis, cytolysis, reactivity), each of which is assigned a linguistic value describing its impairment degree. The techniques adopted in the system are based on Artificial Intelligence methodologies augmented with linguistic terms handled according to the fuzzy set theory.

Diagnosis, Computer-Assisted↗
PubMed1990

An expert system for the labeling and 3D reconstruction of the coronary arteries from two projections.

In this paper we present a rule-based expert system for the automatic delineation and 3D reconstruction of the left coronary artery on standard RAO and LAO angiographic projections. The approach is based on the application of a general blood vessel model and on anatomical models which take into account the normal variations of the coronary artery structure. In a first step, the arteries are delineated by detecting the maximum intensity on the centerline of the vessels. Then, we label the blood vessel segments according to an anatomical model of the left coronary artery. In general, only 1-2 labels remain for each blood vessel segment. Finally, these results are used for an automatic 3D reconstruction of the left coronary artery from two projections. Results from clinical RAO and LAO angiograms will be presented.

Algorithms↗
PubMed1997

Neural networks in neurotologic expert systems.

Artificial intelligence donates new possibilities to neurotologic research. Neural networks are a computer-based reasoning method which can be applied in expert systems created for clinical decision support. Neural networks have been used in medical imaging, in medical signal processing and to analyze both clinical and laboratory data. Principally, neural networks simulate the function of the brain. They have to be taught to make correct decisions from the input data. This learning process can be either supervised or unsupervised. The decision making is based on mathematical transformations and it occurs on a hidden level. Calculations are made on parallel manner and the decision making simulates pattern recognition method. Neural networks suit well in medical problems which cannot be defined in simple rules. A drawback of neural networks is that the decisions are irrational and cannot be motivated to the user. Another problem is neural networks' difficulty to handle incomplete input data, i.e., how to define some default or expected values for unknown input parameters. In a complex medical area, which would require multilayered neural networks, the neural networks require a large amount of solved cases for the learning process. In our experience neural networks seem not suitable for diagnosing vertigo and a better choice would be either case-based reasoning or possibly genetic algorithms or a combination of these.

Diagnosis, Computer-Assisted↗
PubMed1985

Decision support systems for the outpatient office. Two examples of medical expert systems.

Although definitive decision-making systems remain beyond the scope of present-day applications available on the market, effective and efficient decision support systems have been developed and are entering the market place. These systems focus on very specific tasks in limited domains and can be valuable adjuncts in primary care settings. There are also a few large-scale applications with well-defined goals that have proved themselves. It is imperative to validate the organizational support and expertise underlying any expert decision support system.

Ambulatory Care↗
PubMed1989

OVERSEER: a prototype expert system for monitoring drug treatment in the psychiatric clinic.

This paper describes the development of OVERSEER: a prototype knowledge-based system that monitors the drug treatment of psychiatric patients in real time. Using treatment protocols developed by the expert clinician, OVERSEER monitors the drug treatment process and issues alerts when standard clinical practices are not followed or when laboratory results are abnormal. Written in Prolog, OVERSEER is designed to interface directly with the hospital's database, and, thereby, utilizes all available pharmacy and laboratory data. Moreover, unlike the interactive expert systems developed for the psychiatric clinic, OVERSEER does not require extensive data entry by the clinician. Consequently, the chief benefit of OVERSEER's monitoring approach is the unobtrusive manner in which it evaluates psychopharmacological treatment and provides information regarding patient management to the hospital.

Drug Therapy, Computer-Assisted↗
PubMedDate not supplied

Expert systems in medicine: academic exercise or practical tool?

From the earliest stages of computer development, scientists have aspired to the creation of advanced systems which could simulate human thought and reasoning. Of all the modern technological quests, this research to create artificially intelligent computer systems has been one of the most ambitious and fascinating. Although attempts have been made for the last 30 years to develop and apply such systems to the medical sciences, only limited progress has been made. This paper reviews and discusses the important role of expert systems in medicine and outlines future trends in this area.

Artificial Intelligence↗
PubMed1995

Integration of a data dictionary and a clinical database in an expert system for acute abdominal pain.

Despite promising results, computer-aided diagnosis in acute abdominal pain is rarely used in the clinic. We therefore developed an expert system for acute abdominal pain to be used in clinical routine. The system is based on a new approach integrating a data dictionary, a clinical database and the knowledge base. A data dictionary editor has been developed (C++, WINDOWS, IBM-compatible PC) and a data dictionary for acute abdominal pain has been built up. The clinical database has been linked to a documentation program providing three modes of data entry. The documentation program has been evaluated extensively by clinicians. The integrated approach clearly separates clinical data from knowledge, but guarantees high consistency of data.

Abdominal Pain↗