A computer-based consultation system (expert system) for the classification and diagnosis of dizziness.
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Diagnosis, Computer-Assisted↗
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Artificial intelligence systems (expert systems) as diagnostic consultants for the cytologic and histologic diagnosis of cancer. Report from the Second International Conference, March 13-15, 1988, Chicago, Illinois.
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Diagnosis, Computer-Assisted↗
A gene mapping expert system.
Expert systems are now commonly developed to solve practical problems. Nevertheless, genetics has just begun to benefit from this new technology, since genetic expert systems are extremely rare and often purely experimental. A prototype for risk calculation in pedigrees was developed at the University of Utah, using a commercial frames/rules developmental shell (Intelligence Compiler), which runs on an IBM PC. When small data sets were used, the implementation functioned well, but it could not handle larger data sets. Performance became a major issue, with two possible solutions. The first possibility would have been to port the system to a more powerful machine, and the second would have been to use several different shells or languages, each efficiently representing a specific type of knowledge. Neither of these solutions was applicable in this case. From this experience, we learned that performance, portability, and modifiability were three major requirements for genetic expert systems. To achieve these goals, we implemented the gene mapping expert system GMES: (GMES is unrelated to the gene mapping system, GMS in Lisp combined with a frame/object shell (FROBS). We were able to efficiently represent, control, and optimize a gene mapping experiment, achieving portability by building GMES on top of a C-based version of Common Lisp. Lisp combined with the FROBS expert system shell permitted a declarative representation of each of the components of the experiment, resulting in a transplant specification of the problem within a maintainable system.
Algorithms↗
[Medical expert systems].
Expert systems are software systems that can successfully compare to human experts. Their purpose is mostly advisory. Besides, they give explanation and advice to human experts when performing certain tasks. They are intelligent information systems, and are capable to explain and justify their conclusions. Knowledge systems are smaller software systems, and are usually less successful than human experts. Main reasons for expert systems development in medicine are: need for justification of decisions, need for enhancing performances in many uncertain relations; need for explaining of decision making process etc. One of the reasons of developing knowledge-based systems was that conventional statistic formalisms have not provided satisfactory solutions in medical decision making (MDM). Also, today, the relations between cases and conclusions are not universally valid. So, few causes can provide the same conclusion. Besides, data are not necessarily absolutely accurate. The area of applying expert systems is very wide: diagnosis, prognosis, self-education, directing etc. Basic structure of expert system consists of: knowledge, data base, inferring mechanism, explaining mechanism and user-interface. Though, expert systems also have certain bad features: primarily, they are not physicians i.e. they can not examine a patient. Furthermore, expert system that is good for one certain area is often not good for another one. There are some cases, when these systems can confuse a physician and make him to make a wrong decision. This occurs very often in two specific cases: when the clinical situation is urgent; and when accuracy of clinical information is not definite.
Expert Systems↗
Automated integration of external databases: a knowledge-based approach to enhancing rule-based expert systems.
Expert system applications in the biomedical domain have long been hampered by the difficulty inherent in maintaining and extending large knowledge bases. We have developed a knowledge-based method for automatically augmenting such knowledge bases. The method consists of automatically integrating data contained in commercially available, external, online databases with data contained in an expert system's knowledge base. We have built a prototype system, named DBX, using this technique to augment an expert system's knowledge base as a decision support aid and as a bibliographic retrieval tool. In this paper, we describe this prototype system in detail, illustrate its use, and discuss the lessons we have learned in its implementation.
Artificial Intelligence↗
Automated integration of external databases: a knowledge-based approach to enhancing rule-based expert systems.
Expert system applications in the biomedical domain have long been hampered by the difficulty inherent in maintaining and extending large knowledge bases. We have developed a knowledge-based method for automatically augmenting such knowledge bases. The method consists of automatically integrating data contained in commercially available, external, on-line databases with data contained in an expert system's knowledge base. We have built a prototype system, named DBX, using this technique to augment an expert system's knowledge base as a decision support aid and as a bibliographic retrieval tool. In this paper, we describe this prototype system in detail, illustrate its use and discuss the lessons we have learned in its implementation.
Asthma↗
Embedding expert systems in laboratory information systems.
Expert systems (ESs) may enhance decision making throughout most steps in the clinical laboratory testing process. By embedding ES capabilities in a laboratory information system (LIS), the LIS gains the capability of symbolic reasoning whereas the ES gains use of the LIS database and communications resources. Design considerations include modular integration, performing timely inferences, sparing LIS resources, and providing a syntax that facilitates knowledge base development and verification by laboratorians. The LIS notifies the ES of specimen status changes through an event log. An event scanner finds events that are relevant to prestored knowledge frames and passes this information to an inference processor through instance records. If conditions specified in the knowledge frames are satisfied, an alert processor sends a message to a CRT, printer, file, or report. Examples of applications during order entry, specimen distribution, processing, and results reporting are presented.
Clinical Laboratory Information Systems↗
Knowledge acquisition and verification tools for medical expert systems.
Expert systems require large amounts of domain knowledge for non-trivial problem solving. Experience in developing such systems has shown that the processes of acquiring domain knowledge (knowledge acquisition) and of determining whether the knowledge is consistent, complete, and correct (knowledge verification) are major problems. This paper discusses various tools developed to assist in these two processes. These tools bring additional knowledge to bear, or provide better interfaces between a knowledge engineer and the expert system's knowledge.
Artificial Intelligence↗
Adjuncts to the nurse's skills? Advantages and disadvantages of expert systems.
Expert systems can, if properly devised, help nurses make clinical decisions. This, the final article in the series, looks at the advantages and disadvantages to their use.
Clinical Competence↗
Expertise in expert systems: knowledge acquisition for biological expert systems.
In this paper it is argued that an expert system requires more than factual knowledge before it can display expertise in a given domain. The additional knowledge consists of the heuristics or 'rules of thumb' used by an expert to manipulate and interpret the factual knowledge. The knowledge acquisition phase of an expert system project involves determining the factual knowledge (which may be obtained from published sources) and the heuristics used by an expert to manipulate that knowledge--these heuristics can only be obtained from an expert. In reviewing existing biological expert systems it is apparent that many contain only the factual knowledge relating to the domain, and lack the heuristics that enable such systems to show expertise. This paper reviews a number of knowledge acquisition techniques which could be used for acquiring heuristic knowledge and discusses when their use is appropriate. The knowledge acquisition techniques discussed are those suitable for the development of small-scale expert systems as these are most likely to be of interest to biologists. The techniques include the use of questionnaires, interview techniques and protocol analysis; particular emphasis is placed on a modification to the 'twenty questions' interview technique which was developed specifically to elicit taxonomic knowledge relating to water mite identification.
Biology↗
Expert systems and expert behavior.
Iliad 4.0 and QMR 2.03 are computer-based diagnostic knowledge bases that can play many roles in decision support and other areas of medical practice, but neither appears ready to assume the role of an expert diagnostic consultant. In contrast to human experts, these programs have problems related to recognition of their own limitations, interpretation of continuous data, recognition of dependent findings, selection of tests, and description of the impact of certain tests. Suggestions to improve these aspects of knowledge bases are offered.
Artificial Intelligence↗
Introducing expert systems to medical students using ESTA, expert system teaching aid.
Medical students are allocated little curriculum time for exposure to expert systems. ESTA, a computer model of an expert system, was developed to make best use of this time. The nature of the students' interaction with ESTA is described, and their reactions to the expert system concepts and the place of expert systems in medicine are presented. A discussion of these reactions draws some conclusions about teaching expert systems in particular, and computers in general, in the basic medical course.
Attitude of Health Personnel↗
Medical expert systems developed in j.MD, a Java based expert system shell: application in clinical laboratories.
Growing complexity of diagnostic tests, combined with increased workload, stringent laboratory accreditation demands, continuous shortening of turn-around-time and budget restrictions have forced laboratories to automate most of their iterative tasks. Introduction of artificial intelligence by means of expert systems has gained an important place in this automation process. Different parts of clinical laboratory activity can benefit from their implementation and the present project deals with one aspect, namely the clinical interpretation of diagnostic tests. This paper describes how j.MD, a new Java based expert system shell, was used to reprogram the expert system for interpretation of amylase isoenzyme patterns that has been in use for many years in our laboratory, and that was originally programmed in Pro.MD, a Prolog based expert system shell. One of the most important advantages of the j.MD system is its bidirectional link with the laboratory information system. This project shows how expert systems for the interpretation of complex diagnostic tests that demand specific expertise can become an integrated part of the automated clinical chemistry lab.
Alkaline Phosphatase↗
Expert systems for the prediction of ovulation: comparison of an expert system shell (Expertech Xi Plus) with a program written in a traditional language (BASIC).
The use of an expert system shell (EXPERTECH Xi Plus) in the construction of an expert system for the diagnosis of infertility has been evaluated. A module was devised for predicting ovulation from the medical history alone. Two versions of this system were constructed, one using the expert system shell, and the other using QuickBASIC. The two systems have been compared with respect to: (1) ease of construction; (2) ease of knowledge base update; (3) help and explanation facilities; (4) diagnostic accuracy; (5) acceptability to patients and clinicians; (6) user-friendliness and ease of use; (7) use of memory space; and (8) run time. The responses of patients and clinicians were evaluated by questionnaires. The predictions made by the computer systems were compared to the conclusions reached by clinicians and to the "gold standard" of day 21 progesterone. The conclusions of this pilot study are: (1) the construction of this expert system was NOT facilitated by the use of this expert system shell; (2) update of the knowledge base was not facilitated either; (3) the expert system shell offered built-in help and explanation facilities, but as the system increased in complexity these became less useful; (4) after initial adjustment of decision thresholds the diagnostic accuracy of the system equalled that of the clinician; (5) the patient response to computer history-taking was very favorable but much less favorable to computer diagnosis; (6) the clinicians took a positive attitude to computer diagnosis; (7) the systems were easy to use; (8) the expert systems shell required much more memory space and had a much slower response time than the system written in BASIC.
Diagnosis, Computer-Assisted↗
Iliad and Medical HouseCall: evaluating the impact of common sense knowledge on the diagnostic accuracy of a medical expert system.
Diagnostic expert systems are gaining acceptance among physicians. Recently, a comparative study of the performance of four major commercial diagnostic programs demonstrated that the information they produce contains a certain amount of irrelevancy that the trained physician ignores. Medical HouseCall is a consumer health information expert system based on a medical expert system for physicians, Iliad. To enhance the usefulness of Medical HouseCall to health care consumers, we are interested in significantly reducing the amount of irrelevancy contained in the diagnostic differential list. Testing with over 470 'textbook' cases revealed that a large part of the irrelevancy can be eliminated by adding universal and medical 'common sense' knowledge. Using four performance measures, we compared, on a subset of cases, the differential lists from two versions of the program: the refined knowledge base (1995) and an older version (1994) 'pre-common sense'. The results suggest that the performance of a diagnostic expert system can be significantly improved with the addition of common sense knowledge.
Artificial Intelligence↗
The diagnosis of microcytic anemia by a rule-based expert system using VP-Expert.
We describe our experience in creating a rule-based expert system for the interpretation of microcytic anemia using the expert system development tool, VP-Expert, running on an IBM personal computer. VP-Expert processes data (complete blood cell count results, age, and sex) according to a set of user-written logic rules (our program) to reach conclusions as to the following causes of microcytic anemia: alpha- and beta-thalassemia trait, iron deficiency, and anemia of chronic disease. Our expert system was tested using previously interpreted complete blood cell count data. In most instances, there was good agreement between the expert system and its pathologist-author, but many discrepancies were found in the interpretation of anemia of chronic disease. We conclude that VP-Expert has a useful level of power and flexibility, yet is simple enough that individuals with modest programming experience can create their own expert systems. Limitations of such expert systems are discussed.
Anemia↗
Efficient production of desulfurizing cells with the aid of expert system.
An expert system was used to achieve the high production of desulfurizing cells of Rhodococcus erythropolis KA 2-5-1. By adding a proper amount of sulfur containing component with the aid of the expert system, we could avoid excess feeding which resulted in the lowering of desulfurizing activity and starvation which caused serious damage to cell growth. In order to determine the addition amount by the expert system, the data of the amount of chemical elements contained in the cells were used as a reference for comparison with the medium components present. Culture experiments were carried out using a 5l jar fermentor with several kinds of media whose components were determined based on the inferred results with the aid of the expert system. We could restrict the amount of the sulfur component addition so that sulfur was a growth-limiting factor; in contrast, the amounts of other elements were sufficient for growth.As a result, the maximum specific production rate of 2-hydroxy biphenyl (2HBP) and the maximum cell concentration were 20mmolkg-drycells(-1)h(-1) and of 45g-drycellsl(-1), respectively. At 100h of cultivation, 1g/l of dibenzothiophene (DBT) was converted to 2HBP within 20h, i.e., 10mmolkg-drycells(-1)h(-1) of specific desulfurization activity, and the specific activity remained stable for a long period in the culture experiment.
Journal Article↗
Multiple risk expert systems interventions: impact of simultaneous stage-matched expert system interventions for smoking, high-fat diet, and sun exposure in a population of parents.
Three stage-based expert system interventions for smoking, high-fat diet, and unsafe sun exposure were evaluated in a sample of 2,460 parents of teenagers. Eighty-four percent of the eligible parents were enrolled in a 2-arm randomized control trial, with the treatment group receiving individualized feedback reports for each of their relevant behaviors at 0, 6, and 12 months as well as a multiple behavior manual. At 24 months, the expert system outperformed the comparison condition across all 3 risk behaviors, resulting in 22% of the participants in action or maintenance for smoking (vs. 16% for the comparison condition), 34% for diet (vs. 26%), and 30% for sun exposure (vs. 22%). Proactive, home-based, and stage-matched expert systems can produce significant multiple behavior changes in at-risk populations where the majority of participants are not prepared to change.
Adolescent↗