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PubMed2004

Development of an expert system knowledge base: a novel approach to promote guideline congruent asthma care.

Existing guidelines for the clinical management of asthma provide a good framework for such tasks as diagnosing asthma, determining severity, and prescribing pharmacological treatment. Guidance is less explicit, however, about establishing a patient-provider partnership and overcoming barriers to asthma management by patients in a way that can be easily adopted in clinical practice. We report herein the first developmental phase of the "Stop Asthma" expert system. We describe the establishment of a knowledge base related to both the clinical management of asthma and the enhancement of patient and family self-management (including environmental management). The resultant knowledge base comprises 142 multilayered decision rules that describe clinical and behavioral management in three domains: 1) determination of asthma severity and control; 2) pharmacotherapy, including prescription of medicine for chronic maintenance, acute exacerbation, exercise pretreatment, and rhinitis relief; and 3) patient self-management, including the process of intervening to facilitate the patient's asthma medication management, environmental control, and well-visit scheduling. The knowledge base provides a systematic and accessible approach for intervening with family asthma-related behaviors.

Asthma↗
PubMed2005

Predicting drug metabolism--an evaluation of the expert system METEOR.

The paper begins with a discussion of the goals of metabolic predictions in early drug research, and some difficulties toward this objective, mainly the various substrate and product selectivities characteristic of drug metabolism. The major in silico approaches to predict drug metabolism are then classified and summarized. A discrimination is, thus, made between 'local' and 'global' systems. In its second part, an evaluation of METEOR, a rule-based expert system used to predict the metabolism of drugs and other xenobiotics, is reported. The published metabolic data of ten substrates were used in this evaluation, the overall results being discussed in terms of correct vs. disputable (i.e., false-positive and false-negative) predictions. The predictions for four representative substrates are presented in detail (Figs. 1-4), illustrating the interest of such an evaluation in identifying where and how predictive rules can be improved.

Analgesics, Opioid↗
PubMed1996

Expert systems for the evaluation of data quality for establishing the Recommended Dietary Allowances.

In view of the important role that nutrient intake assessments play in establishing the Recommended Dietary Allowances (RDAs), the quality of food composition data must be assured for accuracy and representativeness. Assurance of data quality requires the definition of critical parameters in the data generation process and the evaluation of specific data for foods and components according to these parameters. An expert systems approach for evaluating the quality of analytical data has been developed by scientists at the Beltsville Human Nutrition Research Center to determine the quality of food composition data for five parameters: sampling plan, sample handling, number of samples, analytical method and analytical quality control. A rating scale for each parameter was developed with 0 representing poor or inadequately documented data and 3 representing optimal data. Specific criteria for each parameter and rating have been developed and incorporated into expert systems software to facilitate the objective assignment of ratings for each data source by the reviewer. After all ratings for a specific food-nutrient combination are assigned, the system calculates a composite score called the "confidence code" which indicates to the user the relative level of confidence in the data. By identifying and rating the important steps in the data generation process, one can begin to partition the possible sources of error or variability in the process. Limitation of the data set relative to specific purposes (e.g., setting the RDAs) can be identified. The evaluation process can provide the basis for focussed research to improve the most critical areas of the data generation process. A similar process could be established to evaluate the quality of analytical data for clinical measurements used to establish the RDAs.

Documentation↗
PubMed1999

Notification of real-time clinical alerts generated by pharmacy expert systems.

We developed and implemented a strategy for notifying clinical pharmacists of alerts generated in real-time by two pharmacy expert systems: one for drug dosing and the other for adverse drug event prevention. Display pagers were selected as the preferred notification method and a concise, yet readable, format for displaying alert data was developed. This combination of real-time alert generation and notification via display pagers was shown to be efficient and effective in a 30-day trial.

Clinical Pharmacy Information Systems↗
PubMedDate not supplied

Medical expert systems based on causal probabilistic networks.

Causal probabilistic networks (CPNs) offer new methods by which you can build medical expert systems that can handle all types of medical reasoning within a uniform conceptual framework. Based on the experience from a commercially available system and a couple of large prototype systems, it appears that CPNs are now an attractive alternative to other methods. A CPN is an intensional model of a domain, and it is therefore conceptually much closer to qualitative reasoning systems and to simulation systems than to rule-based or logic-based systems. Recent progress in Bayesian inference in networks has yielded computationally efficient methods. The inference method used follows the fundamental axioms of probability theory, and gives a sound framework for causal and diagnostic (deductive and abductive) reasoning under uncertainty. Experience with the prototypes indicates that it may be possible to use decision theory as a rational approach to test planning and therapy planning. The way in which knowledge is acquired and represented in CPNs makes it easy to express 'deep knowledge' for example in the form of physiological models, and the facilities for learning make it possible to make a smooth transition from expert opinion to statistics based on empirical data.

Artificial Intelligence↗
PubMed1990

Development of an expert system for bacterial identification: study of a prototype for identifying beta-galactosidase positive enterobacteria.

A prototype of an expert system for the identification of beta-galactosidase positive Enterobacteriaceae has been developed, for use with the API 20 EC kit. The system is implemented in Prolog on an IBM PC AT with 640 K of central memory and 20 megabytes of secondary memory. Its objectives are to highlight errors that can occur when the kit is in use. It can indicate the presence of new groups or species and give advice or suggest additional tests for the differentiation of the new species from those included in the kit.

Enterobacteriaceae↗
PubMed1991

Prospects of expert systems in psychiatric diagnostics.

The paper deals in particular with the theoretical foundations of introducing computer technology and techniques into psychiatric diagnostics. It is done from the aspect of present expert systems of the type FEL-EXPERT, PCE expert, CODEX, etc., as well as from the aspect of a prospect for the future in connection with the development of systems of the so called artificial intelligence.

Expert Systems↗
PubMedDate not supplied

[Expert system of choosing lower limb (thigh) prosthesis and diagnosis of the quality of prosthetics (II)].

The authors review an expert system (ES) of diagnosing the prosthetics quality, elucidate problems of the ES functioning within the framework of the general ES. Special attention is paid to the description of the knowledge and datum surface, to the algorithms of direct and reverse illegible logical data output in taking decisions as to defects of the patient's prosthesis in accordance with the signs observable. The work of the ES is demonstrated with special reference to concrete cases. Analysis is made of the estimates of the sensitivity to the initial data. The ES presents knowledge in depth, being designed in two versions: for practical (individual and mass) prosthetics and training purposes.

Algorithms↗
PubMed2005

Transcranial ultrasonography system for visualizing skull and brain surface aided by fuzzy expert system.

A conventional ultrasonography system can noninvasively provide human tissue and blood flow velocity information with real-time processing. In general, since the human skull prevents the disclosure of brain anatomy, we usually placed the sensor at the anterior and superior attachment site of the upper ear (the posterior temporal window) in adults. Due to this limitation, the conventional system cannot obtain transcranial information from arbitrary places in the skull. This paper describes a transcranial sonography system that can visualize the shape of the skull and brain surface from any point to examine skull fracture and brain disease such as cerebral atrophy and epidural or subdural hematoma. In this system, we develop anatomical knowledge of the human head, and we employ fuzzy inference to determine the skull and brain surface. To evaluate our method, three models are applied: the phantom model, the animal model with soft tissue, and the animal model with brain tissue. In all models, the shapes of the skull and the brain tissue surface are successfully determined. Next, the method is applied to two adults. As a result, we have determined the skin surface, skull surface, skull bottom, and brain tissue surface for the subjects' foreheads. Consequently, our system can provide the skull and brain surface information using three-dimensional shapes.

Algorithms↗
PubMedDate not supplied

Building an expert system: a systematic approach to developing an instrument for data extraction from the literature.

When building an expert system that will be acceptable to clinicians in their practice, it is imperative that the knowledge engineer identifies, defines, and describes a clinical problem precisely. This can be accomplished by eliciting private knowledge from expert clinicians or by analyzing public knowledge available in the scientific literature. This study describes a systematic method for examining public knowledge found in health care textbooks and practice guidelines surrounding the concept of oral feeding in premature infants in a neonatal intensive care unit. It includes the development of an instrument for extracting data from these sources to standardize definitions of terminologies. Preliminary results and plans for further analysis are reported. This method can be applied to other clinical problems deemed appropriate for decision support system development.

Decision Making, Computer-Assisted↗
PubMedDate not supplied

HYDRA: a knowledge acquisition tool for expert systems that critique medical workup.

HYDRA is a computer-based knowledge acquisition tool under development to assist in the creation of expert systems which critique medical workup. To use HYDRA, a domain expert first outlines the recommended approaches to the workup of a chosen medical problem, using the Augmented Transition Network formalism. From this model, HYDRA produces a list of the various conditions for which critiquing comments may be required to react to all possible approaches that might be proposed by the user of the critiquing system. Domain-specific constraints can be used to restrict the number of conditions suggested. In this way, HYDRA assists the domain expert by providing a model for structuring the problem, and by breaking down the domain expert's work into a set of small, easily understood tasks.

Algorithms↗
PubMed1991

[Medical expert systems and clinical needs].

The rapid expansion of computer-based systems for problem solving or decision making in medicine, the so-called medical expert systems, emphasize the need for reappraisal of their indication and value. Where specialist knowledge is required, in particular where medical decisions are susceptible to error these systems will probably serve as a valuable support. In the near future computer-based systems should be able to aid the interpretation of findings of technical investigations and the control of treatment, especially where rapid reactions are necessary despite the need of complex analysis of investigated parameters. In the distant future complete support of diagnostic procedures from the history to final diagnosis is possible. It promises to be particularly attractive for the diagnosis of seldom diseases, for difficult differential diagnoses, and in the decision making in the case of expensive, risky or new diagnostic or therapeutic methods. The physician needs to be aware of certain dangers, ranging from misleading information up to abuse. Patient information depends often on subjective reports and error-prone observations. Although basing on problematic knowledge computer-born decisions may have an imperative effect on medical decision making. Also it must be born in mind that medical decisions should always combine the rational with a consideration of human motives.

Artificial Intelligence↗
PubMed1988

[Expert systems in medical emergencies].

Computers have already been introduced into the field of medical applications and many support systems have been developed. Recently expert or knowledge-based systems have been employed in some specific fields. A project for the creation of an expert system for use in toxicological emergencies is presented. This system is designed to give the physician continuous support and to employ computer formulae as similar as possible to medical reasoning. For this reason, medical knowledge was examined and the event graphs, frames and production rules to represent it were chosen.

Amanita↗
PubMed1997

Five steps from a simple data base to an expert system in nutrition.

The paper describes the experience from the Medical Informatics Department--ITC in the field of the human nutrition. The informatic systems were developed staring from a minimal data base and simple programs. The final versions are expert systems running within complex medical informatic products.

Decision Making, Computer-Assisted↗
PubMedDate not supplied

Temporal propositions in a HIS-oriented expert system shell.

This paper describes one of the most important aspects in our recent research on building a HIS-oriented expert system shell: representation and processing of temporal propositions about time-series. With respect to time attribute, medical objects have been classified into three categories: time-independent objects, pointwise time-dependent objects and durationwise time-dependent objects. Temporal propositions can be applied only to time-dependent objects. Through implementation of fuzzy relational operators and their uses, the representation and processing of such propositions become more reasonable and more natural.

Algorithms↗