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PubMed1995

Evaluation of an expert system producing geometric solids as output.

This paper reports the evaluation of an expert system whose output is a three-dimensional geometric solid. Evaluating such an output emphasizes the problems of establishing a comparison standard, and of identifying and classifying deviations from that standard. Our evaluation design used a panel of physicians for the first task and a separate panel of expert judges for the second. We found that multi-parameter or multi-dimensional expert system outputs, such as this one, may result in lower overall performance scores and increased variation in acceptability to different physicians. We surmise that these effects are a consequence of the higher number of factors which may be deemed unacceptable. The effects appear, however, to be equal for computer and human output. This evaluation design is thus applicable to other expert systems producing similarly complex output.

Computer Graphics↗

PubMed1991

Validation, clinical trial, and evaluation of a radiology expert system.

The PHOENIX Radiology Consultant is a rule-based expert system which assists physicians in planning radiological work-up strategies. This article describes the methods used to create and validate the system's knowledge base. The feasibility and acceptability of PHOENIX were tested for two years in a clinical trial. During this period, the system was used 1,421 times, an average of 13.7 times per week, primarily by medical students and nonradiologist physicians. Much of the system's use occurred at night and on weekends, when the radiology department was not fully staffed. Several physicians were enlisted to further evaluate the utility of the system. The results of their evaluation indicate that an expert system that helps physicians select diagnostic-imaging studies can serve as a useful and informative component of a radiology information system, and is particularly useful for medical students and physicians in training.

Algorithms↗

PubMed1991

Differential diagnosis of dementia: a comparison between the expert system EVINCE and clinicians.

The diagnostic performance of the expert system EVINCE was compared with that of 85 clinicians in diagnosing 10 patients suspected of suffering from dementia. A multidisciplinary expert committee provided a standard diagnosis as reference for comparison. The results showed that the syndrome and etiologic diagnoses made by EVINCE were in very close agreement with those of the expert committee and that the diagnostic performance of EVINCE was better than that of the average clinician. The present findings indicate that expert systems, especially those within the realm of complex multidimensional medical problems, could be a valuable aid in medical practice.

Aged↗

PubMed2000

Effectiveness of an expert system for astronaut assistance on a sleep experiment.

BACKGROUND: Principal Investigator-in-a-Box ([PI]) is an expert system designed to train and assist astronauts with the performance of an experiment outside their field of expertise, particularly when contact with the Principal Investigators on the ground is limited or impossible. In the current case, [PI] was designed to assist with the calibration and troubleshooting procedures of the Neurolab Sleep and Respiration Experiment. [PI] displays physiological signals in real time during the pre-sleep instrumentation period, alerts the astronauts when a poor signal quality is detected, and displays steps to improve quality. METHODS: Two studies are presented in this paper. In the first study 12 subjects monitored a set of prerecorded physiological signals and attempted to identify any signal artifacts appearing on the computer screen. Every subject performed the experiment twice, once with the assistance of [PI] and once without. The second part of this study focuses on the postflight analysis of the data gathered from the Neurolab Mission. After replaying the physiological signals on the ground, the frequency of correct [PI] alerts and false alarms (i.e., incorrect diagnoses by the expert system) was determined in order to assess the robustness and accuracy of the rules. CONCLUSIONS: Results of the ground study indicated a beneficial effect of [PI] and training in reducing anomaly detection time and the number of undetected anomalies. For the in-flight performance, excluding the saturated signals, the expert system had an 84% detection accuracy, and the questionnaires filled out by the astronauts showed positive crew reactions to the expert system.

Adult↗

PubMed1991

Expert systems for efficient handling of medical information. I. Lung cancer.

Rule-based expert systems offer the ability to quickly retrieve considerable amounts of information relevant to specific situations; the technology can be potentially very useful to physicians in an era characterized by an explosion of medical data. A simple rule-based expert system that allows a microcomputer user to obtain staging, prognostic and therapeutic information relevant to patients with lung cancer is presented. The program was constructed by utilizing an inexpensive expert system development package (EXSYS). Using information from the literature, 194 rules were formulated; these rules can be expanded or updated at any time. The computer user interacting with the system is asked sequential questions regarding the characteristics of the tumor (T) of a particular patient, the nodal status (N), the presence or absence of metastasis (M), how the staging information was derived (clinically or at surgery), the tumor cell type and the therapeutic options being considered (different surgical procedures, radiotherapy, chemotherapy and others). The system selects the appropriate answers and displays the stage of the tumor and relevant prognostic information. The microcomputer user can also examine the rules that were selected by the system. These rules have relevant comments that apply to the specific condition, as well as appropriate references. The microcomputer user can change all or some of the conditions (i.e., therapeutic options) and compare the results of the various "WHAT-IF" simulations.

Expert Systems↗

PubMed1995

Expert system for management of urinary incontinence in women.

The purpose of this nursing informatics and outcomes research study was to determine the effectiveness of an expert system for disseminating knowledge to ambulatory women health care consumers with urinary incontinence. Clinical knowledge from the Agency for Health Care Policy and Research (AHCPR) patient guideline for urinary incontinence and research literature for behavioral treatments provided the knowledge base for the expert system. Two experimental groups (booklet and expert system) and one control group were utilized. Study results suggest the use of an expert system as one effective communication means for disseminating clinical information in an advisory capacity to ambulatory women with urinary incontinence.

Adult↗

PubMed1996

An expert system based on causal knowledge: validation on post-cardiosurgical patients.

A new expert system for the analysis of post-cardiosurgical patients in Intensive Care Units is described, and a preliminary validation performed. The inference engine employs a hybrid reasoning method which integrates quantitative and qualitative simulation techniques in an original manner. The long-term knowledge consists of a causal network which reproduces the main relationships between physiological quantities involved in the course after cardiac surgery. Emphasis has been given to respiratory and metabolic, as well as cardiovascular quantities both in the systemic and pulmonary circulations. Preliminary system validation has been performed on a set of 40 cardiosurgical patients, previously classified either at normal-risk (17 patients) or at high-risk (23 patients) by means of statistical classification techniques. In most cases, predictions of the expert system substantially agree with those provided by the more traditional statistical method. The system, however, is also able to furnish detailed explanations on the possible physiological causes responsible for the patient status. In particular, simulation results indicate that a reduction in the cardiac index (19 cases) and an increase in the oxygen utilization coefficient (19 cases) are the most critical alterations in the high-risk patients. The system imputes the reduced cardiac index to a rise in total systemic resistance (15 high-risk patients), a decrease in cardiac strength (2 high-risk patients) or an insufficient filling volume of the systemic circulation (4 high-risk patients). Furthermore, in 6 high-risk patients the depressed cardiac outflow occurs with a reduction in the arterial oxygen content, mainly imputable to an insufficiency of blood hemoglobin content. Finally, two examples of the complete expert system explanatory capabilities are shown with reference to a pair of high-risk patients and discussed.

Blood Circulation↗

PubMed1992

Toward normative expert systems: Part I. The Pathfinder project.

Pathfinder is an expert system that assists surgical pathologists with the diagnosis of lymph-node diseases. The program is one of a growing number of normative expert systems that use probability and decision theory to acquire, represent, manipulate, and explain uncertain medical knowledge. In this article, we describe Pathfinder and our research in uncertain-reasoning paradigms that was stimulated by the development of the program. We discuss limitations with early decision-theoretic methods for reasoning under uncertainty and our initial attempts to use non-decision-theoretic methods. Then, we describe experimental and theoretical results that directed us to return to reasoning methods based in probability and decision theory.

Artificial Intelligence↗

PubMed1994

Using an expert system to ensure accurate third-party reimbursement.

Many hospitals experience difficulties with Medicare reimbursements tied to payments strictly related to a diagnosis related group (DRG) in which a patient's stay is grouped or assigned. Potentially compounding these difficulties, many large third-party payers have also adopted the DRG system. A lack of knowledge of the intricacies of the DRG system on the part of physicians, administrators, or medical records managers often can lead to hospitals receiving considerably less reimbursement than they are entitled. A computerized coding system that incorporates an expert system can substantially increase reimbursements by simplifying the coding process, identifying overlooked diagnoses, and identifying any missing procedures.

Diagnosis-Related Groups↗

PubMed1989

HEPAR: an expert system for the diagnosis of disorders of the liver and biliary tract.

The HEPAR system is a medical expert system intended as a supportive tool for the diagnosis of disorders of the liver and biliary tract. In the system, the role of information from the medical interview, physical examination, and simple non-invasive diagnostic methods, such as ultrasonography, is emphasized in order to reduce the number of diagnostic procedures to be applied. Medical diagnosis is modelled in the system using the formalism of if-then rules. Based on patient data available, the system determines whether the data indicate a hepatocellular or a biliary obstructive disorder, and whether or not benign or malignant features are present. Moreover, the system produces a subset of possible diagnoses out of a set of more than 70 diagnostic categories. The system is still under development. In a preliminary study, the HEPAR system has been evaluated using data from 94 jaundiced patients. Conclusions with respect to the hepatocellular or biliary obstructive nature of the disorder, the benign or malignant nature of the disorder, and the final diagnosis, were reached in 95%, 65%, and 80% of the cases, respectively. These conclusions were correct in 85%, 92%, and 80% of the classified cases, respectively. The system was shown not to be sensitive to incompleteness of information.

Adult↗

PubMed1991

Ecological applications using a novel expert system shell.

Much of the information used by ecologists in modelling and decision making is imprecise. The imprecision arises both from data that are inexact or incomplete and from the use of ecological principles that are sometimes less than fully reliable and may be conflicting. Nevertheless, expert ecologists are able to construct usable models and make decisions that are used to manage and control ecological resources. This paper describes a unique expert system shell, developed in conjunction with user ecologists, which incorporates features enabling ecologists to represent knowledge and uncertainty in their expert systems in a way that is natural and appropriate. The reasoning mechanism was similarly developed in conjunction with user ecologists. It produces solutions to a class of expert level problems along with explanatory mechanisms and an appropriate analysis of the reasoning process. Three expert systems have been constructed by ecologists using this expert system shell. This enabled the shell designers to evaluate features for inclusion in the shell. The successful use of the shell by the ecologists has shown that significant economies arise when expert system shell design is tailored to use by a specific class of experts, in this case ecologists.

Ecology↗

PubMedDate not supplied

Converting a rule-based expert system into a belief network.

The theory of belief networks offers a relatively new approach for dealing with uncertain information in knowledge-based (expert) systems. In contrast with the heuristic techniques for reasoning with uncertainty employed in many rule-based expert systems, the theory of belief networks is mathematically sound, based on techniques from probability theory. It therefore seems attractive to convert existing rule-based expert systems into belief networks. In this article we discuss the design of a belief network reformulation of the diagnostic rule-based expert system HEPAR. For the purpose of this experiment we have studied several typical pieces of medical knowledge represented in the HEPAR system. It turned out that, due to the differences in the type of knowledge represented and in the formalism used to represent uncertainty, much of the medical knowledge required for building the belief network concerned could not be extracted from HEPAR. As a consequence, significant additional knowledge acquisition was required. However, the objects and attributes defined in the HEPAR system, as well as the conditions in production rules mentioning these objects and attributes, were useful for guiding the selection of the statistical variables for building the belief network. The mapping of objects and attributes in HEPAR to statistical variables is discussed in detail.

Artificial Intelligence↗

PubMed1994

Expert systems. Assisting formulary decision making in the ambulatory setting.

In this article, the author reviews the application of a computer-assisted decision support system to their formulary decision-making process. Basic information is presented describing expert systems, which are a type of computer-assisted decision support system, and their advantages and disadvantages. A specific example of an expert system, 'RXPERT', is described. 'RXPERT' is a prototype expert system that models the decision-making process for an ambulatory (nonhospital) formulary. This formulary is the underpinning of the prescription drug benefit programme for the nearly 1 million residents of Saskatchewan, Canada. In the current formulary decision process, each drug product is evaluated by 2 separate committees, with the third and final decision resting with the Ministry of Health. The first committee, the Drug Quality Assessment Committee (DQAC), comprises members with expertise in medicine, pharmacology, clinical pharmacy, pharmaceutics, statistics, and regulatory processes. The DQAC evaluates information from the drug manufacturer and other independent sources, and makes an initial assessment with respect to clinical aspects of alternative therapies and generic interchangeability. The committee then makes its recommendation to the Saskatchewan Formulary Committee (SFC). The SFC reviews the recommendation of the DQAC and considers the administrative and economic implications of accepting the product for the patient, the programme, and healthcare professionals' practice. The SFC either reaffirms the recommendation of the DQAC or modifies it based on further review, and forwards its recommendation to the Ministry of Health. Finally, the Ministry of Health reviews the evaluation and determines the drug's formulary status.(ABSTRACT TRUNCATED AT 250 WORDS)

Decision Making, Computer-Assisted↗

PubMed1999

[The evaluation of HepatoConsult, a hepatological expert system--the initial results and trends].

BACKGROUND AND OBJECTIVE: HepatoConsult (HC) is a medical expert system, based on the expert system building block D3, designed to aid in the diagnosis of liver and biliary tract disease. It was the aim of this study to evaluate its diagnostic competence in clinical cases prospectively. PATIENTS AND METHODS: The diagnostic accuracy of HC was tested prospectively in 106 consecutive patients with the main diagnosis of liver disease. 57 were ambulant, 49 were in-patients. The data were obtained and stored at defined phases of the diagnosis. The diagnoses put forward by HC were compared with the final clinical diagnosis and, on the basis of the data, checked for plausibility by four experienced physicians. RESULTS: After history taking and physical examination HC put forward the main diagnosis, as established by the doctors in charge, in 60% of patients. After addition of the results of basic laboratory tests and sonography, HC provided the correct diagnosis in 85% and, after inclusion of all the findings, in 93%. In almost all cases HC put forward diagnoses that were, on the basis of the supplied data, considered correct by the four physicians experienced in liver disease. In 56% of cases HC provided more differentiated diagnoses or items in the differential diagnosis than the attending doctors. In the opinion of the four assessors HC had not put forward any seriously wrong diagnoses. CONCLUSION: HC can be useful in solving diagnostic problems and thus in ensuring the quality of medical diagnoses.

Diagnosis, Computer-Assisted↗

PubMed1990

Prospective application of an expert system for the medical history of joint pain.

An expert system with 60 questions about medical history was developed for 32 rheumatologic diseases: 358 outpatients with joint complaints have been examined. The final diagnosis (result of symptoms, signs, and findings) was compared with the computer diagnoses and with the independently assumed diagnoses of the physician. The only source of information available to the physician was the medical history. Misinterpretation of the computer diagnoses occurred in 25.6% of cases compared with 21.5% of the physician. The final clinical diagnosis remained uncertain in 32.6% of cases. The error frequency of the expert system was influenced by the underlying disease, the certainty of the assumed diagnosis by the physician, the user experience in rheumatology, the number of questions asked, and the time of application before or after the doctor-patient contact. Of the errors 44% were produced because of information deficits of the computer using standardized questions. The information of the physician in the diagnostic process is quite different to that of the computer.

Arthritis↗

PubMedDate not supplied

An expert system for guiding image segmentation.

Application of image segmentation to biomedical research is now customary. Due to the existence of a rich heuristic knowledge, many users have no deep experience in this field. It is therefore necessary to integrate knowledge-based techniques with image segmentation operators. The purpose of our expert system is to guide users in image segmentation. Its main functions are: suggest a reasonable overall scheme of processing and recommend appropriate operators and algorithms at each stage. The characteristics of this expert system are presented: (a) interaction with users: through "conversation," the expert system acquires the informations about a given problem; (b) use of belief values which indicate users' descriptions about the image characteristics. By associating image features with belief values, the system gets the informations about the image appearance and makes inference more effectively; (c) local backtracking strategy, which allows the expert system to repeatedly search for a better solution until a satisfactory result is obtained; (d) integrating with an image analysis package, users can directly execute the operations recommended by the expert system. A practical application of the system is then shown in details. Finally, our opinions in designing such a system are discussed.

Algorithms↗

PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗

PubMed1999

Concepts, contexts and expert systems.

This paper describes problems identified in our attempts to develop an expert system for management of urinary tract infections. We found three aspects which we believe are important to consider when developing such systems. The objective of our future work will be to evaluate the impact of these problems on expert system development and usage.

Artificial Intelligence↗