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PubMedDate not supplied

An essay on power of expert systems versus human expertise.

In connection with several recent studies of medical informatics, the usefulness and use of expert systems have been both criticized and defended. We have examined the issue of the inference power of expert systems compared to that of human experts. At an abstract level we have shown that there is no doubt that expert systems could successfully complement human experts within strictly limited and well-defined specialties, and actually be of reasonable aid in diagnosis, provided that the expert systems have been correctly and effectively elaborated. Also practical experiments were conducted with our recently implemented expert system.

Brain Diseases↗
PubMed1996

[Electronic expert system in medicine. For example ILIAD].

Expert systems are knowledge data bases founded on patient data, literature searches and opinions of experts. With these systems it is for instance possible to test or to take clinical decisions. ILIAD, USA version 4.2, an adaptable software using probabilistic strategies, was investigated. This system is, among other uses, employed for quality assurance and documentation, as a teaching instrument as well as a knowledge base. It comprises the ICD-9 index of diagnoses and a selected and commented up-to-date review of the literature from 'Mosby's Year Book of Medicine'. The features of ILIAD are presented, and the possibilities and limitations of its use as well as that of expert systems in general are discussed.

Diagnosis, Computer-Assisted↗
PubMed1991

HF-Explain: a natural language generation system for explaining a medical expert system.

Causal models have been used, with considerable success, to reason in the medical domain. While these systems typically have a robust reasoning mechanism and knowledge base about their specific area of expertise, their ability to satisfactorily explain their results in a meaningful, coherent and concise manner has been less impressive then their diagnostic capabilities. This paper describes a program, HF-Explain, that generates natural language explanations of one such system--the Heart Failure Program. HF-Explain, is loosely based on work done by McKeown in the Text system, using augmented transition networks (ATN) as a formalism to guide the explanation process. The result is a coherent, concise, accurate and rich explanation of Heart Failure Programs' diagnostic hypotheses.

Cardiac Output, Low↗
PubMedDate not supplied

[Methodology for the development of expert systems of viral epidemiology].

The proposed methodology for the elaboration of the base of knowledge uses a tree of the hierarchical entities and a simplified variant of the natural language. The resolution system is based on an extension of the predicate calculation containing, in an explicit way, entities of different nature, and among these the correlations giving the rules of deduction.

Artificial Intelligence↗
PubMed1991

An expert system for simulation of coronary heart disease risk factor interventions.

The feasibility of using an expert system to support intervention studies within CRISPERS was investigated. A prototype expert system named CRISPERT was designed to accept user inputs, adjust the values to CRISPERS requirements, start a sequence of simulations, and analyze and interpret the results. The rule-based system was implemented using the expert system development language OPS5 combined with FORTRAN, as well as SAS procedures and DEC VMS system service routines. Results of initial tests suggest that using an expert system as an interface between users and CRISPERS is a viable approach. The development of CRISPERT facilitates the usability of CRISPERS for intervention studies of coronary heart disease.

Computer Simulation↗
PubMedDate not supplied

Techniques in evaluating nursing expert systems: A case study.

This study addresses the problems in evaluating nursing diagnostic artificial intelligence (AI) expert systems. Two separate experiments (N = 49) were conducted using a computer expert system. The first experiment, the "white box" experiment (n = 9), compared the diagnostic techniques applied by experience RNs against the programmed techniques used by the expert system. The second experiment, the "black box" experiment (n = 40), compared diagnostic results of beginning nurses against the computer expert systems results. In some cases the computer outperformed the nurses and vice versa. The evaluation techniques, as applied in both experiments, enhance the ability of nurses to evaluate and select AI expert systems to be used in computer-assisted diagnosis of nursing problems.

Clinical Competence↗
PubMed1989

Expert systems in histopathology. I. Introduction and overview.

An introduction to, and overview of, expert systems is presented, along with some preliminary comments on their application in diagnostic and analytical histopathology and cytopathology. The terminology common to expert systems is defined, and the nature of expert systems is discussed. In particular, the differences between expert systems and other types of computer programs (e.g., algorithms) or means of solving problems are explored. The rationale for their use and the types of tasks for which they are appropriate are also discussed.

Diagnosis, Computer-Assisted↗
PubMed1990

New screening system for unruptured cerebral aneurysms--combination of an expert system and DSA examination.

We have designed a screening system to diagnose unruptured aneurysms, including the use of digital subtraction angiography (DSA). We surveyed 115 patients who had undergone clipping procedures after subarachnoid hemorrhage (SAH) and questioned them with regard to the subjective symptoms. Sixty-eight of 92 patients who returned the questionnaire reported, prior to rupture, headache, eye pain, and neck pain most frequently, and also impairment of extraocular movements, ptosis, visual field defects, and motor and sensory disturbances. Nineteen (47.5%) of 40 patients who had complete pain relief after surgery complained of headache from 1 week to 1 month before SAH. In addition, nine patients (22.5%) complained of headache for several years, and were also pain-free after surgery. For the indication of DSA, we employed an expert system based on fuzzy set theory. Seven groups of parameters are: Group 1, a basic questionnaire concerning age, sex, and past and family histories; Group 2, 15 warning signs selected on the basis of retrospective study; and Groups 3-7, detailed questions concerning each sign. Scoring weights assigned to each condition based on the results of the retrospective study, and threshold values were determined by several neurosurgeons. The certainty factors for intermediate hypotheses were calculated from these weights and threshold values and summed up, from which the conclusion was obtained. Twelve new cases of unruptured cerebral aneurysm were diagnosed using this screening system. This system may improve the ability to diagnose cerebral aneurysms before rupture.

Aged↗
PubMed1996

Application of a case-based expert system to orthodontic diagnosis and treatment planning: a review of the literature.

Computer expert systems are being utilised increasingly in medical fields to assist diagnosis and treatment planning. Traditional rule-based expert systems have some limitations when applied to orthodontic diagnosis and treatment planning. These limitations may be avoided by using a case-based system which is a particular type of expert system that uses a stored data bank of previously-treated cases to provide the knowledge for solving new treatment problems. This article reviews the use of expert systems for orthodontic diagnosis and treatment planning, outlines the rationale, processes and advantages of case-based systems, and gives examples of the application of this technology in medical fields.

Case-Control Studies↗
PubMed1991

The use of induction in the design of an expert system for thyroid function studies.

In this study, the role of induction in the design of an expert system for diagnosing thyroid disorders is evaluated. An expert system was first designed conventionally, based on interaction between a knowledge engineer and a thyroid specialist. This involved weighting three tests (T3, free T4 and TSH) according to the reliability of the test and the presence or otherwise of influencing factors. Compatibility was then tested with known ranges for the parameter values, and a diagnosis made of one of three possible outcomes (euthyroid; hypothyroid; hyperthyroid). Two expert systems were then induced using, firstly, a set of rules and, secondly, a sample set chosen from a database. These systems were then tested against the expert designed system. Both induced systems produced results which were superior to the expert designed system and, in addition, provided insight into the decision-making process. It is concluded that induction is very useful in the design of expert systems of this nature.

Expert Systems↗
PubMed1988

An expert system on the diagnosis of ascites.

We constructed an expert system on the diagnosis of ascites, using a combination of case reports and unpublished patient data. Rule production was by induction from examples, and the program operated on an algorithm which was a modification of Quinlan's ID3. The result was a small, but formally complete expert system. When tested against a new data set of patients, our expert system predicted the clinical diagnosis 82% of the time.

Ascites↗
PubMed2003

Internet based expert system for the management of gallstones, renal, ureteric and bladder calculi.

An Internet based expert system for the management of gallstones, Renal, Ureteric and bladder calculi based on ultrasound images is presented in this paper. Calculi are due to abnormal collection of certain chemicals like oxalate, phosphate and Uric acid. These calculi can be present in kidney, Ureter or in Urinary bladder and also in gall bladder. The expert system is designed to assist the physician to detect, extract, classify and diagnose calculi with greater accuracy. It also helps physicians in the management of calculi based on the etiological analysis of calculi. The Expert system takes an ultrasound image as input along with the symptoms of the patients. The expert system extracts the renal calculi and analyzes it using different image processing techniques to extract the image features like size, location and texture. These image features along with the clinical data of the patient enable the expert system to provide the decisions to decide the future course of treatment with more accuracy.

Artificial Intelligence↗