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PubMed1986

Expert systems in dermatology: the computer potential. The example of facial tumour diagnosis.

The expert system approach to computer diagnosis uses a non-algorithmic method to represent and manipulate an expert's knowledge and reasoning. This information, which may be provided by a dermatologist, is represented by rules in a logic-based computer language in order to provide interactive and explanatory features. The major advantages of using expert system techniques for computer-aided diagnosis in dermatology are that knowledge is made explicit, the heuristic nature of an expert's knowledge can be more easily captured and the more easily readable programs make modification easier. In the example described, the differential diagnosis of nine facial skin tumours was considered. The program, using the language 'micro-PROLOG' in an expert system shell 'APES', consists of 'rules' and 'facts' which define a relationship between patient and symptom, symptom and disease or disease and therapy. The 'strength' of each relationship is defined and 'key symptoms' identified. The system finally offers a diagnosis, an estimate of certainty and simple management advice.

Diagnosis, Computer-Assisted↗

PubMed1993

Comparison of discriminant analysis and probabilistic expert system in VCG data classification.

Our previous studies (Valová et al. 1992) have dealt with the possibilities of expert system utilization for electrocardiologic data interpretation. The results obtained in these studies provided evidence that the selected probabilistic expert system is suitable for the solution of VCG data interpretation problems. The aim of this paper was to compare the results obtained by stepwise discriminant analysis with that obtained by a probabilistic expert system. These classification methods were applied to VCG data measured by Frank's lead system. Five groups of patients were investigated: 76 healthy subjects, 36 patients with angina pectoris, 112 patients with old posterior myocardial infarction, 107 patients with old anterior myocardial infarction and 35 patients with old anteroseptal myocardial infarction. The classification was carried out by the leaving-one-out technique. Results of the classification obtained in five groups by a probabilistic expert system are evidently better than those obtained by stepwise discriminant analysis.

Angina Pectoris↗

PubMedDate not supplied

Building nursing expert systems using automated rule induction.

Previous articles in this journal testify that nursing expert systems have become a feature of nursing information technology and nursing theory. Regretfully, experts' knowledge is not easy to elicit and computer systems are not easy to program. Fortunately, expert system shells bypass most of the problems associated with programming expert systems. Lesser known but with no less impact are shells that bypass the other difficult phase of expert system construction, namely knowledge elicitation. The major aim of this article is to provide a nontechnical description of such a shell and the use to which we have put it within three quite different areas of nursing. In addition, the article introduces expert systems in nursing as a potentially unifying development in a profession rapidly undergoing specialization.

Expert Systems↗

PubMed1989

Expert systems in histopathology. III. Representation of knowledge as "structured objects".

In designing expert systems for histopathology, the representation of knowledge as "structured objects" may offer advantages over the use of a set of rules. A structured-object representation using straightforward, descriptive, declarative statements can allow consideration of the relationships between the facts and constraining conditions of a domain in a richer, more complex and more complete manner. The use of associative (semantic) networks and frames to organize histopathologic knowledge as structured objects is discussed and demonstrated with examples that consider the complex tissue structures found in the kidney and thyroid. As an example, the design and function of a structured object-based expert system for follicular thyroid aspirates is discussed, including a consideration of the types of information that such a system would require. Such a system utilizes model-based reasoning rather than the rule-based reasoning of rule-based expert systems.

Diagnosis, Computer-Assisted↗

PubMed2006

[An expert system neurology--possibilities and limitations].

We present an expert system Neurology, which was developed completely on basis of the commercial available data base program Filemaker-7.0. At present it covers approximately 400 diagnoses of neurological and psychiatric diseases. After the input of cardinal symptoms, course and localisation of the disease the program calculates a first set of possible differential diagnoses and asks for additional symptoms or the performance of apparative diagnostics to investigate the final diagnose. At first, the performance of the expert system was tested with 15 predetermined neurological case reports. Users with different previous knowledge of Neurology performed the input. In this test the program was able to identify the correct diagnose in nearly all cases and the diagnostic proposals were superior to those of the users with minor neurological training. In a second test with real patient data, the rate of correct diagnoses was approximately 80%. In summary, the used computer algorithms proved as appropriate for the aim of giving Neurological diagnoses. Possible additional applications could be student training or the use as interdisciplinary reference work.

Algorithms↗

PubMed1995

Assessment of the severity of asthma by an expert system. Description and evaluation.

Asthmaexpert, an expert system (ES), was produced at the special request of several clinicians in order to better understand the medical decisions made clinical experts in managing an asthmatic patient. We describe and evaluate this knowledge base, focusing mainly on assessment of the severity of asthma. After compiling data from a patient, Asthmaexpert assesses the severity of the disease and identifies the trigger factors involved, suggests any further investigations that may be required, and offers a treatment strategy. Implemented with Nexpert and Hypercard, it runs on a MacIntosh personal computer. The validation stage involved eight clinical experts who provided 20 case report forms (CRF) with their conclusions about management of asthma. The CRF were then programmed into the ES, which provided its own conclusions about the same subjects. Afterward, all the experts evaluated the conclusions given by ES or by their colleagues in a double-blind manner. One hundred twenty-seven CRF were available. The reliability of the experts' opinions was good, with a substantial consensus between them when assessing severity scores (kappa = 0.27 to 0.54). There was no difference in concordance of opinions on severity scores either between the experts who designed the system and ES or between the other experts and ES (weighted kappa = 0.72 and 0.69, respectively). Experts judged that the severity scores given by ES were as good as those proposed by their colleagues, and that the overall conclusions given by ES were as good as or better than those given by their colleagues. The conclusions drawn by ES were given a good rating.(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗

PubMedDate not supplied

Liability for medical expert systems: an introduction to the legal implications.

Some of the possible legal implications of the production, marketing and use of expert systems are examined in this paper. The relevance of a legally useful definition of expert systems, comprising systems designed for use by both laymen and professionals, is related to the distinctions inherent in the legal doctrine underlying provision of goods and provision of services. The liability of the sellers and users of, and contributors to, expert systems are examined in terms of professional malpractice as well as product liability. A recurring theme indicates that legislators may be inclined to restrict possibilities of liability suits in order to avoid disincentives to the creation of expert systems.

Confidentiality↗

PubMedDate not supplied

Development of an expert system for preoperative assessment of female urinary incontinence.

We developed a simple expert system for preoperative assessment of women complaining of involuntary loss of urine and scheduled to undergo surgery for incontinence. The aim of the system was to use the parameters obtained at urodynamic investigation to arrive at the correct diagnosis. We used an IBM-PC with two disk drives and 256k RAM, and the expert system shell EXSYS, a rule based system with the possibility of assigning probabilities to the different solutions. To write the rules forming the knowledge base we used a two-fold approach: we constructed tree diagrams for each diagnosis and calculated the corresponding predictive values (statistical approach), and we added rules based on our experience (heuristic approach). The expert system has been found reliable in a clinical setting and is useful for teaching purposes.

Diagnosis, Computer-Assisted↗

PubMed1990

A personal computer-based maintenance management expert system for hospitals.

In this paper a Maintenance Management Expert System (MMES) is developed. This system is suitable for hospitals as well as for manufacturing organizations. This expert system consists of five sections. These are work order management, equipment management, craftsmen management, material and supply control, and monitoring of maintenance activities. In addition the MMES provides several reports that can be utilized for improving maintenance effectiveness.

Expert Systems↗

PubMed1988

Spontaneous subarachnoid haemorrhage: expert system for appraisal of the prognosis and computer-supported decision for therapy.

An expert system is presented which allows appraisal of the prognosis and a computer-supported decision for the therapy of patients with acute spontaneous subarachnoid haemorrhage (SAH). The knowledge of physicians as a synthesis of their own and other clinicians' experience is simulated with methods of artificial intelligence by setting up two data banks. In one data bank, selected information on the correlation between initial clinical parameters, on the one hand, and mortality, outcome and complications, on the other, from about 250 neurological publications is stored, taking into consideration the therapeutic regimens applied. A second data bank receives clinical and laboratory data profiles of a patient population which has already been treated. The expert system is able to compare the individual initial findings with the corresponding parameter combination stored in the data banks regarding the decision for therapy of a patient to be treated. This enables both calculation of the probable complications and prediction of the expected outcome in relation to various possible forms of therapy. The expert system can thus indicate the kind of therapy where the lowest number of complications and the best outcome can be expected, thus supporting the decision of the physician. As each new patient is treated, the volume of stored information increases, so the system possesses self-learning characteristics. To check the validity of the prognoses, the outcome estimated by the expert system for 51 patients with spontaneous SAH was compared with the actual outcome, and a high level of agreement was attained.

Decision Making, Computer-Assisted↗

PubMed1991

An expert system for inferring structures of organic compounds from their mass spectra.

Development of an 'expert system' for elucidation of structures of acyclic organic compounds is described. An expert system is a computer program that embodies some of the heuristic problem-solving knowledge of human experts so that it can effectively be used as an aid to decision making. The expert system described in this paper is intended to assist a chemist in arriving at plausible structures, the input data being the mass spectrum, molecular formula and presence (if known) of functional groups. The program generates chemically possible structures for the given molecular formula and can use, where available, the constraints imposed by the mass spectrum of the compound and by any known functional groups. The program makes use of a new algorithm for obtaining a canonical representation of structures and a new heuristic for incorporating constraints of the mass spectrum. This work constitutes a case study of the application of artificial intelligence techniques in chemistry and the material presented highlights this motivation.

Chemistry, Organic↗

PubMed2003

Project of an expert system supporting risk stratification and therapeutic decision making in acute coronary syndromes.

The aim of the project was to create a computer program--expert system, which will support a doctor when a management for patients with acute coronary syndrome needs to be chosen. The expert system consists of four modules: knowledge base, previous cases database, inference engine and explanation module. Knowledge base was created with support of clinical experts, based on current management standards, guidelines and results of clinical trials according to evidence-based medicine rules. Data from new patient are added to the case database. Inference engine integrates two types of reasoning rule-based and case-based reasoning. Computer expert system gives unambiguous and objective answer. Recommendation given by an expert system can be reliable. At present the system is tested in clinical practice. Strategies recommended by the system are compared to the management applied in patients treated in Cardiology Clinic.

Acute Disease↗

PubMed1994

Induction of medical expert system rules based on rough sets and resampling methods.

Automated knowledge acquisition is an important research issue in improving the efficiency of medical expert systems. Rules for medical expert systems consists of two parts: one is a proposition part, which represent a if-then rule, and the other is probabilistic measures, which represents reliability of that rule. Therefore, acquisition of both knowledge is very important for application of machine learning methods to medical domains. Extending concepts of rough set theory to probabilistic domain, we introduce a new approach to knowledge acquisition, which induces probabilistic rules based on rough set theory (PRIMEROSE) and develop a program that extracts rules for an expert system from clinical database, using this method. The results show that the derived rules almost correspond to those of medical experts.

Artificial Intelligence↗

PubMed1999

Expert-system classification of sleep/waking states in infants.

This work is part of a project to develop an expert system for automated classification of the sleep/waking states in human infants; i.e. active or rapid-eye-movement sleep (REM), quiet or non-REM sleep (NREM), including its four stages, indeterminate sleep (IS) and wakefulness (WA). A model to identify these states, introducing an objective formalisation in terms of the state variables characterising the recorded patterns, is presented. The following digitally recorded physiological events are taken into account to classify the sleep/waking states: predominant background activity and the existence of sleep spindles in the electro-encephalogram; existence of rapid eye movements in the electro-oculogram; and chin muscle tone in the electromyogram. Methods to detect several of these parameters are described. An expert system based on artificial ganglionar lattices is used to classify the sleep/waking states, on an off-line minute-by-minute basis. Algorithms to detect patterns automatically and an expert system to recognise sleep/waking states are introduced, and several adjustments and tests using various real patients are carried out. Results show an overall performance of 96.4% agreement with the expert on validation data without artefacts, and 84.9% agreement on validation data with artefacts. Moreover, results show a significant improvement in the classification agreement due to the application of the expert system, and a discussion is carried out to justify the difficulties of matching the expert's criteria for the interpretation of characterising patterns.

Electroencephalography↗

PubMed1990

[Development and clinical application of an expert system for supporting diagnosis of 201Tl stress myocardial SPECT].

A consultation expert system which supports our computer aided reporting system was developed. The system was used for the evaluation of the two dimensional polar (bull's eye) display of 201Tl myocardial SPECT. The system consists of patients management (PM) and consultation expert systems (ES). The former is connected to image processors coupled with scinticameras. The bull's eye display of myocardial SPECT is transferred from image processor to the data base of PM. When inference request is made, the feature extraction program extracts information on localization, extent and severity of focal defects comparing count-rates pixel by pixel with the reference obtained from seven normal controls. The inference engine is activated to determine presence of focal defects utilizing diagnostic rules in the knowledge base. The results are sent back to PM and reported with the probability of assurance. Fifty eight patients with old myocardial infarction (OMI), angina pectoris (AP) and other diseases as well as normal controls were included in the study. The decision for presence or absence of focal defects by ES agreed with that by nuclear physicians (NP) in 301 segments among 330 (91%) in stress images. The presence of redistribution in delayed images agreed in 43 segments among 67 (64%). Image interpretation by ES agreed well with that of NP in patients with OMI (19/20) and AP (9/11). Seven were interpreted as normal by both ES and NP. The system is useful, as it provides NP with complementary and supportive information applicable to decision making and reporting. Further clinical experiences can improve knowledge base for better ES function.

Computer Systems↗

PubMedDate not supplied

[Informatics in mental sciences: clinical data management and "expert systems"].

The role of information systems and particularly that of expert systems in medicine is analysed in brief. It is pointed out that, unlike other branches of medicine, in mental sciences the role of expert systems has so far been more limited because of certain intrinsic problems of programming on the one hand, and the scientific models of the psychiatrist on the other. An improvement in information technology and programming languages and better empirical classification of mental disturbances could provide useful bases for the realisation of computerised consultancy systems in mental sciences.

Algorithms↗

PubMed2006

Intention to adopt a smoking cessation expert system within a self-selected sample of Dutch general practitioners.

To investigate intention to adopt a new smoking cessation expert system as well as outline perceived barriers by general practitioners (GPs) to adopt this expert system, a written questionnaire was sent to 771 registered GPs. Respondents, representing 34.8% of the registered GPs, were classified as adopters (34.2%), doubters (36.2%) or non-adopters (29.2%). Adopters and doubters were less negative about the time investment for the GP when adopting the expert system than non-adopters. Adopters expected a more positive reaction from their patients than non-adopters. Smoking cessation was mostly considered to be a task for the practice assistant. The authors discuss the relevance of barriers mentioned not to implement the expert system and give recommendations for further steps into implementing primary prevention activities in Dutch general practice.

Adult↗

PubMed1996

An expert system for diagnosis and therapy in lung transplantation.

An expert system for diagnosis and therapy after lung transplantation has been developed and evaluated by domain experts. The system captures a total of 21 diagnoses encompassing rejection, pulmonary infection, and some diseases of gastrointestinal origin. The disease hypotheses are scored and ranked by their ability to explain the patient findings. A hypothesis is accepted as a candidate disease if it is ranked high on the list and is able to account for the cardinal findings of the disease. The therapy knowledge is captured in the form of rules. The results demonstrate the feasibility of an expert system for diagnosis and therapy after lung transplantation.

Evaluation Studies as Topic↗