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PubMedDate not supplied

A clinical molecular scanner: the Melanie project.

We developed an expert system to analyze and interpret protein maps. This system, Melanie (medical electrophoresis analysis interactive expert), can distinguish between normal and cirrhotic liver and identify various types of cancer on the basis of protein patterns in biopsy specimens. Our findings suggest that some diseases associated with toxic compounds or modifications of the human genome can be diagnosed by expert systems that analyze protein maps. The combination of protein mapping and computer analysis could result in a clinically useful "molecular scanner". The massive amount of information analyzed and stored in such studies requires new strategies, including centralized databases and image transmission over networks. Increased understanding of protein expression and regulation will enhance the importance of the human genome project in medicine and biology.

Computer Systems↗
PubMed2006

Prioritization of congenital cardiac surgical patients using fuzzy reasoning--a solution to the problem of the waiting list?

Surgical waiting lists are of high importance in countries, where the national health system is unable to deliver surgical services at a rate that would allow patients to avoid unnecessary periods of waiting. Prioritization of these lists, however, is frequently arbitrary and inconsistent. The objective of our research was to analyze the medical decision-making process when prioritizing patients with congenital cardiac malformations for cardiac surgical procedures, identifying an appropriate representation of knowledge, and transferring this knowledge onto the design and implementation of an expert system ("PrioHeart"). The medical decision-making process was stratified into three stages. The first was to analyze the details of the procedure and patient to define important impact factors on clinical priority, such as the risk of adverse events. The second step was to evaluate these impact factors to define an appropriate "timing category" within which a procedure should be performed. The third, and final, step was to re-evaluate the characteristics of individual patients to differentiate between those in the same timing category. We implemented this decision-making process using a rule-based production system with support for fuzzy sets, using the FuzzyCLIPS inference engine and expert system shell as a suitable development environment for the knowledge base. The "PrioHeart" expert system was developed to give paediatric cardiologists a tool to allow and facilitate the prioritization of patients on the cardiosurgical waiting list. Evaluation of "PrioHeart" on limited sets of patients documented appropriate results of prioritization, with a significant correlation between the prioritization made using "PrioHeart" and those results obtained by the individual consultant specialist. We conclude that our study has demonstrated the feasibility of using an expert system approach with a fuzzy, rule-based production system to implement the prioritization of cardiac surgical patients. The approach may potentially be transferable to other medical subspecialities.

Cardiac Surgical Procedures↗
PubMed1992

How an expert corporate fitness program might be designed.

In this paper, the ideas and concepts of expert systems were combined with the principles of fitness to demonstrate how an expert system could be designed to prescribe exercise routines for corporate employees. Since the examples and the rules of fitness used herein were just a small fraction of what could be used in building such a system, it would be possible to spend years developing a huge system that employs thousands of rules. However, as the supply of skilled labor continues to fall short of corporations' needs, more effort will be brought to bear on maintaining fitness in the work force. Such expert systems may be a reality in the not-too-distant future.

Exercise↗
PubMed2002

ASExpert: an integrated knowledge-based system for activated sludge plants.

The activated sludge process is commonly used for secondary wastewater treatment worldwide. This process is capable of achieving high quality effluent. However it has the reputation of being difficult to operate because of its poorly understood biological behaviour, variability of input flows and the need to incorporate qualitative data. To augment this incomplete knowledge with experience, knowledge-based systems were introduced in the 1980s however they didn't receive much popularity. This paper presents the Activated Sludge Expert system (ASExpert), which is a rule-based expert system plus a complete database tool proposed for use in activated sludge plants. The paper focuses on presenting the system's main features and capabilities to revive the interest in knowledge-based systems as a reliable means for monitoring plants. Then it presents the methodology adopted for ASExpert validation along with an assessment of testing results. Finally it concludes that expert systems technology has proved its importance for enhancing performance, especially if in the future it is integrated to a modern control system.

Bioreactors↗
PubMed1991

Expert learning system network for diagnosis of breast calcifications.

Breast calcification diagnosis was studied by using clinical findings and computerized image processing of a mammogram in a network of trained expert learning systems (Outcome Advisor [OA]). The system was tested with records not used for training and performance was compared with radiologist. The network was 72% accurate in classifying clusters of calcifications as malignant or benign over a set of test cases radiologists had considered "hard-to-diagnose calcifications," and referred for biopsy. The radiologists had decided to conduct biopsy by selecting an equal number of positive and negative cases for the test group; thus the radiologists' performance with respect to categories of benign versus malignant was constrained to be 50/50. Statistical analysis shows only a 2% probability that the observed accuracy of 72% was a chance performance in recognizing whether a cluster is benign or malignant. The feasibility of developing a network of OAs for diagnosing breast cancer integrating digital image processing of mammograms is promising.

Breast Diseases↗
PubMed1996

The expert surgical assistant. An intelligent virtual environment with multimodal input.

Virtual Reality has made computer interfaces more intuitive but not more intelligent. This paper shows how an expert system can be coupled with multimodal input in a virtual environment to provide an intelligent simulation tool or surgical assistant. This is accomplished in three steps. First, voice and gestural input is interpreted and represented in a common semantic form. Second, a rule-based expert system is used to infer context and user actions from this semantic representation. Finally, the inferred user actions are matched against steps in a surgical procedure to monitor the user's progress and provide automatic feedback. In addition, the system can respond immediately to multimodal commands for navigational assistance and/or identification of critical anatomical structures. To show how these methods are used we present a prototype sinus surgery interface. The approach described here may easily be extended to a wide variety of medical and non-medical training applications by making simple changes to the expert system database and virtual environment models. Successful implementation of an expert system in both simulated and real surgery has enormous potential for the surgeon both in training and clinical practice.

Artificial Intelligence↗
PubMed1997

Cardiac risk stratification in renal transplantation using a form of artificial intelligence.

The purpose of this study was to determine if an expert network, a form of artificial intelligence, could effectively stratify cardiac risk in candidates for renal transplant. Input into the expert network consisted of clinical risk factors and thallium-201 stress test data. Clinical risk factor screening alone identified 95 of 189 patients as high risk. These 95 patients underwent thallium-201 stress testing, and 53 had either reversible or fixed defects. The other 42 patients were classified as low risk. This algorithm made up the "expert system," and during the 4-year follow-up period had a sensitivity of 82%, specificity of 77%, and accuracy of 78%. An artificial neural network was added to the expert system, creating an expert network. Input into the neural network consisted of both clinical variables and thallium-201 stress test data. There were 5 hidden nodes and the output (end point) was cardiac death. The expert network increased the specificity of the expert system alone from 77% to 90% (p < 0.001), the accuracy from 78% to 89% (p < 0.005), and maintained the overall sensitivity at 88%. An expert network based on clinical risk factor screening and thallium-201 stress testing had an accuracy of 89% in predicting the 4-year cardiac mortality among 189 renal transplant candidates.

Adult↗
PubMed1988

Machine learning in quantitative histopathology.

The role of expert systems functioning as process controllers in learning image understanding systems is discussed. Numeric learning systems already have found a number of applications in cytologic and histopathologic diagnosis. Depending on the required capabilities, systems of increasing complexity are needed. Expert systems to guide scene segmentation in histopathologic imagery require model-based reasoning. Diagnostic image interpretation with learning capability demands a full model of the human expert's competence, including a considerable variety of knowledge representation schemes and inference strategies, coordinated by a meta-process controller.

Artificial Intelligence↗
PubMed1990

Using hindsight in medical decision making.

As the clinical picture of a patient evolves over time, more information becomes available. Certain procedure require time to perform, causing delay between the time when the tests are ordered and when the results are available. Furthermore, as the patient's condition changes over time, serial measurements can be made. The availability of more data allows a more accurate assessment of the patient. Uncertainties, guesses or errors that were made early in the clinical course of patient care can also be identified and resolved when more information is available. Reasoning with a stream of data that changes over time presents a challenge to the designers of expert systems. The use of hindsight in expert system requires that appropriate attention be paid to the temporal relations of the data and that care is exercised in revising decision. I present a data-dependency system, the Temporal Control Structure (TCS), designed to support reasoning with data changing over time and show how it can be used to implement reasoning by hindsight.

Artificial Intelligence↗
PubMed2000

Overview of CARMEM: a new dynamic quantitative cardiac model for ECG monitoring and its adaptation to observed signals.

Different approaches have been proposed in order to achieve knowledge integration for coronary care monitoring applications, usually in the form of expert systems. The clinical impact of these expert systems, which are based only on "shallow" knowledge, has not been remarkable due to the difficulties associated with the construction and maintenance of a complete knowledge base. Model-based systems represent an alternative to these problems because they allow efficient integration of the "deep" knowledge on the underlying physiological phenomena being monitored. In this work, a brief review of existing model-based systems for cardiac rhythm interpretation is presented, followed by the description of a new system for Cardiac Arrhythmia Recognition by Model-Based ECG Matching (CARMEM). Fundamental characteristics of CARMEM are presented; in particular, its ability to provide online parameter adaptation to simulate complex rhythms and to match observed ECG signals. The proposed model can be useful for the explanation of the origin of cardiac arrhythmias and contribute towards their robust characterization in the context of coronary care units.

Artificial Intelligence↗
PubMed1992

The development of a decision support system for diagnosing nasal allergy.

This paper deals with the problem of improving the capability of the medical decision support system (MDSS) for diagnosing nasal allergy by integrating the previously developed expert system with the neural network approach. Three knowledge acquisition methods were used to develop the expert system: statistical, rule-based, and the combined approach. Among the three, a combined approach showed the best prediction rate based on discriminant analysis. Using the results of a combined approach as input values, the neural network was developed using back-propagation method. Unlike the expert system, the neural network system provides the resulting allergy status in probabilistic terms. Managerial as well as legal issues were also discussed in this paper.

Decision Support Techniques↗
PubMed2003

Decision support systems to identify different species of malarial parasites.

In this project, medical expert systems were designed to aid technicians, students and scientists in identifying various species of malarial parasite from blood smears. The rule based system was found to intelligently ask pertinent questions to accurately and efficiently identify the species of malarial parasite. The probabilistic system provided quantitative feedback as to the likelihood of a diagnosis of a species of malaria. Medical expert systems can potentially streamline the process of malarial species identification, and can aid in training new technicians and scientists in this important skill.

Animals↗
PubMed1997

An artificial intelligent diagnostic system on differential recognition of hematopoietic cells from microscopic images.

Despite their advantages, none of the automated white blood cell differentiated counters have replaced the conventional microscopic evaluations of blood and bone marrow slides by hematologists. We have analyzed the smears of 39 patients and 8 control subjects to develop an artificial expert system that recognizes 16 different types of nucleated hematopoietic cells during the stages of differentiation. A charge coupled television camera and a special frame grabber were used for data acquisition, and 247 nucleated cell images were transferred from a microscope to an IBM 386 computer to be processed. One hundred sixty-five and 82 of these images were used for training and testing, respectively. Our system is composed of image processing and analysis (enhancement, thresholding/smoothing, edge detection), pattern recognition (feature extraction and classification with supervised artificial neural network), and expert system development. Image processing and analysis were used to obtain 13 cellular features to be used as the input parameters (neurons) of the artificial neural network. A supervised artificial neural network (back-propagation learning algorithm) was used in the classification of 16 different cells (output neurons of the neural network), which is the second step of pattern recognition. A confusion matrix has been developed to compare the similarities and dissimilarities between the differential recognitions of the hematologist and the expert system. The discriminatory power of the procedure is statistically significant: Q = (N - n.K)2/N.(K - 1) = 28.2. The sensitivity and the specificity of the expert system were 71.4% and 90.9%, respectively.

Bone Marrow↗
PubMed2002

[Computer aided intracranial aneurysm embolization with GDC].

OBJECTIVE: To establish an expert system that automatically generates optimal GDC selection program for the embolization of intracranial aneurysm. METHODS: Twenty highly cost-effective cases of intracranial aneurysm embolized with GDC dense packing were collected. Each of them contains information including aneurysm's volume measured by three-dimension digital subtraction angiography (3D DSA), aneurysm's location, maximum transverse diameter, maximum length diameter, and GDC selection program. An expert system made up of a case base, a mathematical model simulating experts' experience (established with the help of data mining techniques combining multi-layer perceptron network with polyhedrons in high dimensional space), and data envelopment analysis (DEA), was implemented. RESULTS: When the user inputted four required parameters (volume, location, maximum transverse diameter, and maximum length diameter) into the expert system and clicked the "program design" button, candidate GDC selection program(s) would be presented in the result box. CONCLUSION: Case base, data mining techniques, and DEA can be used to establish the expert system that automatically generates optimal GDC selection program for the embolization of intracranial aneurysm. Its clinical value needs to be further evaluated.

Adult↗
PubMed2006

Evaluating nicotine replacement therapy and stage-based therapies in a population-based effectiveness trial.

Pharmacological interventions for smoking cessation are typically evaluated using volunteer samples (efficacy trials) but should also be evaluated in population-based trials (effectiveness trials). Nicotine replacement therapy (NRT) alone and in combination with behavioral interventions was evaluated on a population of smokers from a New England Veterans Affairs Medical Center. Telephone interviews were completed with 3,239 smokers, and 2,054 agreed to participate (64%). Participants were randomly assigned to one of four conditions: stage-matched manuals (MAN); NRT plus manuals (NRT + MAN); expert system plus NRT and manuals (EXP + NRT + MAN); and automated counseling plus NRT, manuals, and expert system (TEL + EXP + NRT + MAN). Assessments were completed at baseline, 10, 20, and 30 months. The point prevalence cessation rates at final follow-up (30 months) were MAN, 20.3%; NRT + MAN, 19.3%; EXP + NRT + MAN, 17.6%; and TEL + EXP + NRT + MAN, 19.9%. Stage-matched manuals provided cessation rates comparable with previous studies. The addition of NRT, expert system interventions, and automated telephone counseling failed to produce a further increase in intervention effectiveness.

Adult↗
PubMedDate not supplied

A new paradigm for explaining and linking knowledge in diagnostic problem solving.

Medical expert systems frequently use causal models to capture knowledge and diagnostic-problem-solving expertise. A significant obstacle confronting these systems is providing informative explanations without prohibitive computational expense. The explanations should allow the user to understand the decisions of the expert system and obtain additional details when needed. A new method, called HyperExplain, has been devised to flexibly link explanations with conclusions generated by a causal reasoning system. This approach creates a patient specific explanatory (PSE) model for the medical expert system that provides decision support from a variety of perspectives. A key feature of this method is the ability to alter the focus of explanations depending upon the problem-solving context and patient manifestations. The method has been implemented in a program that provides diagnostic assistance to physicians in the domain of neurophysiology.

Artificial Intelligence↗
PubMed1991

Automated selection of clinical data to support radiographic interpretation.

Modern computerized hospital information systems can store much of the clinical information required for patient care. In some settings the amount of this information is great enough to interfere with its efficient use. Supplying information in support of radiographic interpretation provides an example of this problem. We describe an automated process that abstracts select data from a clinical database and creates a report recounting salient facts designed to assist the radiologist in interpreting the chest x-ray. The reporting system selects data using a combination of expert systems technology and information theory. It produces a brief summary of relevant clinical data. The original version of this process required run-time use of expert systems techniques that were time-prohibitive in the clinical setting. Through the use of pre-compiled tables of finding specific information contents we have reduced the time requirement to a point where the applications will soon be clinically feasible.

Databases, Factual↗