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PubMed1995

Prudent expert systems with credentials: managing the expertise of decision support systems.

'Black box' expert systems (ES) are mistrusted by clinicians. Errors generated by medical ES are also a significant cause for concern. We report new ES properties--prudence and credentials--that improve error management and underpin a new approach for improving the credibility of ES for clinical users. Prudent ES modify their output according to past experience. For a knowledge base built from 1610 cases, feature exception prudence (FEP) detected all interpretation errors (100% sensitivity for error detection). Although the false positive rate for FEP was high (47%), the 100% sensitivity meant that the 53% of cases that did not produce flags could be exempted from human validation. As more cases are processed, fewer cases should need human validation. Feature recognition prudence (FRP), a property of ripple down rules (RDR), proposed the correct alternative conclusion in 14% of incorrectly interpreted cases. Human expert validation of the flagged cases enabled context-sensitive credentials (accuracy, incidence and specificity of a given conclusion) to accumulate. Credentials should enable the user to judge the credibility of the ES output. An error management strategy based on credentialed, prudent ES should reduce the impact of error in the clinical environment. The empowerment of clinicians to critically evaluate ES credibility may facilitate greater confidence in, and acceptance of, ES by clinicians.

Artificial Intelligence↗

PubMed1990

Medical expert systems: design and applications in pulmonary medicine.

Expert systems are computer applications with a built-in knowledge of a special field to solve problems like a human expert on this subject. An expert system consists of an inference component for problem specification and solving, a knowledge base for storage of data, facts, rules and heuristics, an interface for knowledge acquisition, another for the interaction with the user, and a component for reasoning. Pulmonary expert systems are used for automatic interpretations of lung function data which are measured on-line and directly computed. There are also some expert systems for consultation in difficult pulmonary cases and for protection against overlooking rare diseases. Perhaps the most successful applications of pulmonary expert systems are for educational purposes and a large variety of teachware is available. Pulmonary expert systems are presently limited to small applications; often it is necessary to type in large amounts of data and to follow a deeply structured cumbersome dialog. Also, the problem of responsibility for computer decisions has to be mentioned. But pulmonary expert systems as integrated supplements of normal pulmonary measuring devices, as decision support, and as parts of pulmonary teachware will be helpful tools for the pneumological physician.

Artificial Intelligence↗

PubMed1989

Problems in applying expert system technology to radiographic image interpretation.

A prototype expert system was developed to study the problems applying expert system technology to radiographic image interpretation. The Radiographic Image Interpretation System (RIIS) was developed on a microcomputer using Turbo Prolog, a low cost implementation of the prolog programming language. The present implementation of RIIS was developed to highlight potential problems in applying expert system technology in the evaluation of radiographic images. It was believed that the evaluation of this prototype expert system should include a large number of users unfamiliar with the program's use as this would probably be the case in clinical use of an image interpretation expert system. At present, the expert system deals with a limited domain of focal bony lesions. Twenty cases of pathologically proven bony lesions of varying difficulty were used to evaluate potential problems in the use of this expert system technology. RIIS, with the 20 sample cases, was presented as an exhibit at the 1987 Radiological Society of North America (RSNA) meeting to evaluate the potential problems with inexperienced users. These results were compared with those of experienced users. When a musculoskeletal radiologist, familiar with the programs use, provided the "proper description," the program averaged the correct diagnosis in the top five 80% of the time. During the program's use at the RSNA meeting, the program selected the correct diagnosis in the top five 22% of the time.

Bone Neoplasms↗

PubMedDate not supplied

Preferences for specific work schedules: foundation for an expert-system scheduling program.

An expert system was developed to schedule nurses according to their preferences for specific schedules. The first step in this project was to determine the reliability and stability of nurses' schedule preferences. If nurses' preferences changed frequently, the proposed expert system would not have been feasible. Our results show that for a 3- to 6-month period most nurses are consistent in their preferences of schedules. While each nurse is unique, cluster analysis indicates that it is possible to group nurses according to desire for tour.

Choice Behavior↗

PubMed1990

The recent development and evaluation of a medical expert system (ABVAB).

A medical expert system for the diagnosis of abnormal vaginal bleeding named as ABVAB had been reported. This paper will describe the recent development of ABVAB and its clinical evaluation. The overall testing results are quite satisfactory in spite of the limitations of time and small domain. This expert system, by using the fuzzy and certainty factor concepts, is able to handle imprecise and incomplete medical knowledge which has become informative. The paper also analyses the relative degrees of importance of the history and physical examination data in making a medical diagnosis.

Data Interpretation, Statistical↗

PubMed1990

Diabetes expert systems: planning for long term use.

Expert systems may play an important role in the future in assisting diabetics to control their disease. However the data available suggest that expert systems are difficult to build and more difficult to maintain over a long period. An examination of the problems in maintaining one expert system, GARVAN-ES1, suggests that the problems arise because experts never report on how they reach a decision, rather they justify why the decision is correct. These justifications vary markedly with the context in which they are required, but in context they are accurate and adequate; the difficulties arise in taking them out of context. It is suggested that expert system building techniques must be able to capture knowledge in context and tools must be available to flexibly change the context in which an expert system knowledge base is examined. Two implementations of such strategies, "ripple down rules" and a "knowledge dictionary", are outlined.

Diabetes Mellitus↗

PubMed2003

Learning to live independently with expert systems in memory rehabilitation.

Expert systems (ES), which are a branch of artificial intelligence, has been widely used in different applications, including medical consultation and more recently in rehabilitation for assessment and intervention. The development and validation of an expert system for memory rehabilitation (ES-MR) is reported here. Through a web-based platform, ES-MR can provide experts with better decision making in providing intervention for persons with brain injuries, stroke, and dementia. The application and possible commercial production of a simultaneously developed version for "non-expert" users is proposed. This is especially useful for providing remote assistance to persons with permanent memory impairment when they reach a plateau of cognitive training and demand a prosthetic system to enhance memory for day-to-day independence. The potential use of ES-MR as a cognitive aid in conjunction with WAP mobile phones, Bluetooth technology, and Personal Digital Assistants (PDAs) is suggested as an avenue for future study.

Activities of Daily Living↗

PubMedDate not supplied

Expert nurses and expert systems. Research and development issues.

Expert systems in nursing have developed primarily through deductive, theoretical approaches and have, for the most part, failed to articulate the need for expert clinical nurse knowledge and heuristics as a basis for developing expert systems. Research using knowledge engineering and nurse experts, as well as multi-method approaches to researching nurse experts, should provide content for development of computer systems, in general, and expert systems, in particular, in nursing.

Clinical Competence↗

PubMed1989

The application of expert systems in the clinical laboratory.

An "expert system" consists of a knowledge base containing information of a general nature and an inference system that receives data from the user and applies the knowledge base to produce advice and explanations. An expert system stripped of its knowledge base (a tool) may be used to build new expert systems. Existing systems relevant for laboratory medicine are reviewed. The role in the laboratory of expert systems and their integration and evaluation are discussed.

Clinical Laboratory Techniques↗

PubMed1987

[Current computerized support for vestibular function tests.I. Expert systems and their clinical applications].

Expert Systems are a new method developed by the branch of Computer Science known as Artificial Intelligence in order to make available the knowledge and the expertise of the specialists in a certain domain of the science to other operators in the same field. Most of their applications belong to the medical domain. In this paper the main features of the expert systems are briefly described. As an example the structure and the characteristics of the shell EXPERT (Weiss and Kulikowski, 1979) are presented in detail. This shell has already been used to develop several expert systems. It is the tool by which we constructed a consultation system (VERTIGO) aimed to classify different types of vertigo.

Expert Systems↗

PubMedDate not supplied

The feasibility of axiomatically-based expert systems.

We distinguish axiomatically-based expert systems, whose design and implementation are guided by one or more axiomatically-based theories of decision-making (e.g., decision theory, Bayesian probability theory, maximum entropy theory), from traditional expert systems. An analysis of the knowledge acquisition and computational needs of axiomatically-based expert systems is presented. An explicit quantitative comparison is made between the actual knowledge acquisition effort required to build an existing expert system, and the effort that would be required to build an analogous axiomatically-based advice system. The costs and benefits of the axiomatic approach are discussed. The analysis suggests that the small additional cost of knowledge acquisition for the axiomatic approach are outweighed by the long-term benefits this approach provides.

Computer Simulation↗

PubMed1988

The potential of expert systems in nursing.

The newly emerging technology of expert systems will not replace nursing decision-makers and problem-solvers, but it does promise to serve them as effective "intelligent assistants." An examination of what expert systems are, where expert systems are being used, whether they are possible and economically feasible in nursing, and the benefits, limitations, and future of expert systems, suggest that there is the potential for the development and use of expert systems in some areas of nursing practice, administration, and education. It is the role of nursing administrators to identify and to support the development of those applications that are the most promising.

Computer-Assisted Instruction↗

PubMed1989

Application of expert systems to mammographic image analysis.

A prototype expert system was designed for differentiation of 16 circumscribed breast abnormalities utilizing findings identified by a human observer from X-ray mammograms, clinical data, and patient history. An off-the-shelf expert system shell served as the foundation for the rule base. The system runs on standard microcomputer hardware. Preliminary results suggest the expert system may be valuable in improving the ability of relatively unskilled observers to screen X-ray mammograms. Overall, radiology residents with limited experience in mammographic image analysis obtained a diagnostic accuracy level of 40% on their own, whereas they attained 73% accuracy level when using the expert system. A group of biomedical engineering students with no prior experience in mammographic image analysis obtained on the average an 80% diagnostic accuracy level utilizing the expert system in comparison with the 70% average accuracy level obtained by practicing radiologists without use of the system.

Expert Systems↗

PubMed1991

Expert system for evaluation of reproductive performance and management.

A microcomputer expert system for dairy herd reproductive management was developed using an expert system shell and Turbo Pascal. The expert system initially examines the broad areas of days open, days to first breeding, detection of estrus, and conception rate to determine whether a problem exists. Interpretations ranging from "excellent" to "severe" were established for each trait. The system then selects an area for evaluation that has the largest negative influence on days open. Once an area has been selected for further evaluation, the expert system utilizes information from the user and DHI reports developed by the Dairy Records Processing Center in Raleigh, NC. These reports identify problems with conception categorized by production, parity, service number, days in milk, breed, and service sire. In addition, questions are presented by the expert system to isolate problems of accuracy of data, use of natural service, semen handling, AI technique, detection of estrus, signs of estrus, and other management areas. Recommendations and suggestions are given. Ten commercial herds having a conception rate less than 40% were evaluated by the expert system and by an extension reproduction specialist who supplied information for the system. Of 100 areas investigated, the expert system and extension specialist identified 47 as potential problem areas, agreeing on 85% of them. Most discrepancies resulted from the specialist applying a less restrictive standard when values were close to a preselected threshold.

Animal Husbandry↗

PubMedDate not supplied

Expert systems for medical applications.

Expert systems, also known as intelligent knowledge based systems (IKBS), are computer programs which act as decision-support systems. They are currently being applied to a number of medical domains, most notably diagnosis and treatment planning. Their function is to assist the medical practitioner by giving ready access to the levels of skill shown by experts in a particular field. Much research effort has been expended but few systems have reached routine medical use. This paper presents a tutorial introduction to expert systems in medicine, explaining the basis of the technology, its current limitations and its prospective uses.

Expert Systems↗

PubMedDate not supplied

Statistical process control methods for expert system performance monitoring.

The literature on the performance evaluation of medical expert system is extensive, yet most of the techniques used in the early stages of system development are inappropriate for deployed expert systems. Because extensive clinical and informatics expertise and resources are required to perform evaluations, efficient yet effective methods of monitoring performance during the long-term maintenance phase of the expert system life cycle must be devised. Statistical process control techniques provide a well-established methodology that can be used to define policies and procedures for continuous, concurrent performance evaluation. Although the field of statistical process control has been developed for monitoring industrial processes, its tools, techniques, and theory are easily transferred to the evaluation of expert systems. Statistical process tools provide convenient visual methods and heuristic guidelines for detecting meaningful changes in expert system performance. The underlying statistical theory provides estimates of the detection capabilities of alternative evaluation strategies. This paper describes a set of statistical process control tools that can be used to monitor the performance of a number of deployed medical expert systems. It describes how p-charts are used in practice to monitor the GermWatcher expert system. The case volume and error rate of GermWatcher are then used to demonstrate how different inspection strategies would perform.

Evaluation Studies as Topic↗

PubMed1995

Comparison of inference results of two otoneurological expert systems.

In this paper, two different otoneurological expert systems, Vertigo and One, the latter developed by us, are considered. The expert systems are evaluated as regards their correctness in reasoning diagnoses. In the light of our data collected from randomly selected test patients, One, being a newer technique, is more effective, since it could infer more cases than vertigo did. All the data was also evaluated and diagnosed by otoneurological specialists, independently of the expert systems, to guarantee objectivity in evaluation of the results of the expert systems.

Diagnosis, Differential↗

PubMed1994

LAIT-XPERT VACHES: an expert system for dairy herd management.

An expert system called LAIT-XPERT VACHES, developed to evaluate technical management of dairy enterprises, was tested using case data. The expertise of the system was provided from information obtained from interviews of three dairy management or nutrition experts. LAIT-XPERT VACHES contains over 950 rules and runs on IBM-compatible personal computers. It calculates objectives in milk production, fat and protein production, feeding cost, reproduction, and other areas. In addition, it detects problems and high performance according to these objectives; researches the causes of problems in herd management, feeding, genetics, health, housing, and other areas; and lists conclusions by sector. Using a monthly report of 10 farms registered in the DHI program of Quebec, LAIT-XPERT VACHES issued 92.3% of the conclusions also issued by experts. However, the experts revealed only 53.3% of conclusions reached by the expert system. With Agri-Lait reports of three farms, all conclusions of LAIT-XPERT VACHES were validated by the experts. These results demonstrated that use of an expert system makes it possible to obtain analyses of dairy performance data equivalent to those of human experts.

Animal Feed↗