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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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↗
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↗
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↗
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↗
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↗
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↗
Some observations on the development of a 'scoring system' in an expert system for prediction of ovulation.
As part of the construction of an expert system for the diagnosis of infertility we have developed a scoring system for the identification of conditions which can interfere with ovulation. The scores were obtained from a group of clinicians, and the arithmetic means of these scores were used in the actual system. Correlation between the scores obtained from individual clinicians was very poor (r = 0.345 +/- 0.134). Correlation improved when groups of clinicians were compared. Thus scores derived from a group of clinicians will reflect more reliably the 'general medical opinion'. Scores derived from the opinion of clinicians should be regarded with caution until probabilities or weighting factors derived from real clinical data become available.
Expert Systems↗
Expertise: the basis for expert system development.
Expert systems in nursing are developed with traditional knowledge engineering techniques. These techniques focus on the behavior and logic of the expert, not qualities of expertise. Expertise has been described but not explained. This article proposes a theoretical framework for the study of expertise that can be used to facilitate the development of expert systems.
Clinical Competence↗
[Contribution of expert systems in clinical practice: apropos of the Penelope experience, an expert system in assisted diagnosis and therapy of ovarian adenocarcinoma].
An expert system (ES) for Diagnosis and Therapy of ovarian adenocarcinoma has been developed at the Institut Gustave-Roussy. From surgical and histological results, clinical examination and additional investigative reports, the system presents a synthesis and then determines the stage of the disease. The system than proposes therapeutic indications adapted to the characteristics of the illness and of the patient, and edits a report at the end of the ES consultation. This experience allowed us to specify the field of ES applications in oncology. As tools for diagnosis and therapy, they cannot act as a substitute for the know-how of the physician, as too many medical decisions remain difficult to formalize in the ES. On the other hand the use of artificial intelligence techniques appears to be useful for establishing coherent data bases, which are necessary pre-requisites for clinical research in oncology. The integration of the system in the Hospital Information System is the guarantee of its use in current clinical practice.
Adenocarcinoma↗
Analysis of criteria for grading bladder cancer in urine cytological tumor diagnosis by means of an expert system.
An inductive expert system was used for the analysis of criteria for grading bladder carcinoma in urine cytological tumor diagnosis. This analysis seems necessary in order to provide a better standardization of grading and to avoid tumor grades, which are rather inhomogeneous with respect to morphology and prognosis. The analysis of the database by the inductive system shows a considerable variation of the cytomorphology of different bladder carcinomas graded as G2 tumors, whereas G1 and G3 tumors are more homogeneous groups respectively. Especially nuclear morphological criteria are important features for the detection of highly differentiated carcinomas, whereas nucleolar features might be helpful to assess the proliferative nature of the carcinoma. The future goal of avoiding a grading system with prognostically inhomogeneous tumor grades seems possible when using an inductive expert system for consultation.
Carcinoma, Transitional Cell↗
Workplace--worker--environment. An expert system.
In the expert system described stresses due to the workplace and the life environment are represented by characteristics, which are graded from 0 to 5. The same is true of capabilities of the man. The algorithm looks for the compatibility of one with the other. Examples of characteristics, the function of the certainty factor and of steps of examination depth are shown, with particular reference to the cardiovascular subsystem. The expert system "Workplace--Worker--Environment" is proposed to serve as a basic tool in occupational health care.
Cardiovascular Physiological Phenomena↗
[Interpretative reading and quality control of an antibiotic sensitivity test using an expert system. Application to the API ATB system and Enterobacteriaceae].
An expert system (cadi-yac), written in Turbo-Prolog and working on IBM PC and Bacanal + (a management software of microbiology laboratory) was used to recognize and correct the phenotype of antibiotic sensibility. The results were there of API ATB system. The knowledge was adapted from two references works. A routine use of the expert system give a correct recognition of enzymatic profile in more than 80% cases for the beta-lactams and more than 98% cases for the aminosides. The mistakes detected by cadi-yac, were often interpreted as deficiency of API system by humans experts. The expert system mistakes (1.5%) were due composites phenotypes.
Anti-Bacterial Agents↗
[Clinical laboratory and expert system].
Computer-based expert systems are designed to incorporate the knowledge of a human expert such that the computer program will solve problems in a manner similar to the human expert. The systems can be applied fruitfully to many phases of the laboratory testing, including order entry, specimen collection, analysis, result verification, and reporting. Interpretive reporting by means of expert systems is useful for influencing physician's use of laboratory and they offer potential help for clinical decision making. Although much of the early work in the clinical laboratory involved stand-alone expert systems, approaches that integrate more closely with laboratory information system or hospital information system are being used. A short review on the technology of expert system with an emphasis on the roles played by clinical laboratory is made. A trial system dealing with thyroid diseases, based on interpretation of hormonal levels (TSH, Total and Free T3, T4), is described.
Clinical Laboratory Techniques↗
Integration of a stand-alone expert system with a hospital information system.
A stand-alone PC expert system for evaluating the appropriateness of inpatient admissions has been integrated with an existing hospital information system. The expert system supports preadmission screening for appropriateness of inpatient admissions. The HIS provides extensive clinical data in a coded electronic form, permitting high-level decision support. The integrated system was developed for a 20 week randomized clinical trial to evaluate the effects of preadmission screening on inappropriate inpatient admissions. Three factors of the integration are considered: programmatic integration of the expert system, seamless presentation of mixed platform applications, and integration of coded data from the stand-alone application into the HIS data structure.
Evaluation Studies as Topic↗
Expert systems in histopathology. II. Knowledge representation and rule-based systems.
Two aspects of expert systems for use in diagnostic histopathology and cytopathology are examined: knowledge representation and the structure and operation of rule-based systems. Knowledge may be represented, e.g., in semantic networks, frames, multiple contexts and model-based structures; the choice of structure should be matched to the type of information to create an efficient and logically adequate expert system. In a rule-based system, knowledge is represented as "rules," often in the form of "IF (condition)-THEN (conclusion)" rules. The anatomy of such rules and their operation is explored via the use of examples. Uncertainty in rules is briefly addressed, and their processing by the symbolic reasoning of the "inference engine" of the expert system is described, including both "forward-chaining" ("data-driven") operations and "backward-chaining" ("goal-driven") operations.
Algorithms↗
Development and application of simple expert systems in obstetrics and gynecology.
Expert systems have become increasingly popular in medicine for the support of medical decisions, e.g. diagnosis or treatment. We describe the development and application of 2 simple rule-based expert systems, one used for cycle stimulation in our in vitro fertilization program and the other for preoperative assessment of urinary incontinence. The programs were written using a commercially available expert system shell, are run on a standard personal computer, and are in actual use in our department. Though it is doubtful that simple expert systems can be superior to a human expert we feel that expert systems are very useful in the standardization of protocols and are a valuable teaching instrument.
Decision Making, Computer-Assisted↗