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PubMed1998

A medical expert system approach using artificial neural networks for standardized treatment planning.

PURPOSE: Many radiotherapy treatment plans involve some level of standardization (e.g., in terms of beam ballistics, collimator settings, and wedge angles), which is determined primarily by tumor site and stage. If patient-to-patient variations in the size and shape of relevant anatomical structures for a given treatment site are adequately sampled, then it would seem possible to develop a general method for automatically mapping individual patient anatomy to a corresponding set of treatment variables. A medical expert system approach to standardized treatment planning was developed that should lead to improved planning efficiency and consistency. METHODS AND MATERIALS: The expert system was designed to specify treatment variables for new patients based upon a set of templates (a database of treatment plans for previous patients) and a similarity metric for determining the goodness of fit between the relevant anatomy of new patients and patients in the database. A set of artificial neural networks was used to optimize the treatment variables to the individual patient. A simplified example, a four-field box technique for prostate treatments based upon a single external contour, was used to test the viability of the approach. RESULTS: For a group of new prostate patients, treatment variables specified by the expert system were compared to treatment variables chosen by the dosimetrists. Performance criteria included dose uniformity within the target region and dose to surrounding critical organs. For this standardized prostate technique, a database consisting of approximately 75 patient records was required for the expert system performance to approach that of the dosimetrists. CONCLUSIONS: An expert system approach to standardized treatment planning has the potential of improving the overall efficiency of the planning process by reducing the number of iterations required to generate an optimized dose distribution, and to function most effectively, should be closely integrated with a dosimetric based treatment planning system.

Expert Systems↗

PubMed1992

[Self-instruction expert systems in medicine. Presentation of knowledge, acquisition of knowledge, prediction of suicide as an example].

AIMS: 1. Description of how self-learning expert systems work, and 2. comparison of various algorithms for the establishment of a data base for suicide prediction. POINTS DISCUSSED: from exemplary patient data, self-learning expert systems obtain their expert knowledge which they subsequently employ to establish the diagnosis in new patients. Various possibilities of storing knowledge may be employed, for example the decision tree, classes of rules or neuronal networks. The various forms of representing knowledge are also presented. Taking the prediction of suicidal risk as an example, the effectiveness of the algorithm is tested with the aid of a patient questionnaire. CONCLUSIONS: Different fields of application require different systems. The diagnosis of such expert systems can at least prompt the care-providing physician to reconsider his own diagnosis.

Artificial Intelligence↗

PubMedDate not supplied

The CAREPLAN knowledge base. A prototype expert system for postpartum nursing care.

With the growth of scientific knowledge in the health care disciplines, it has become increasingly difficult for practicing health care professionals to store and manipulate the information required to make clinical decisions. Where the decision making process can be defined and validated, expert systems will be able to assist beginning professionals to learn appropriate patterns of decision making. This article discusses the rationale for utilization of expert systems in a nursing environment. A prototype expert system called CAREPLAN, developed for use in an obstetrical environment, was built using Personal Consultant Plus, a software tool based on the LISP language. The project demonstrates that it is feasible to develop and validate an expert system for a specialized clinical environment in a relatively short period of time in comparison to traditional development methods. Graphics were used to enhance textual information and to increase user appeal. Strategies for future implementation and evaluation are outlined.

Expert Systems↗

PubMedDate not supplied

A computer expert system prototype for mechanically ventilated neonates development and impact on clinical judgment and information access capability of nurses.

A computer expert system is an alternative method of training and providing real-time clinical decision support for nurses to advance their practices from a novice to a proficient level. The purpose of this study was twofold: (1) to develop a prototype of a computer expert system for mechanically ventilated neonates (ES-MVN) and (2) to assess the impact of the ES-MVN on the clinical judgment and information access capability of nurses. Five steps used in developing the prototype are described in this article. The ES-MVN is a multimedia interactive consultation-based program that contains 2 major parts: the nursing diagnosis and the knowledge base on nursing care of mechanically ventilated neonates. A rule-based (Boolean frame) was chosen for the nursing diagnosis decision model. The prototype was developed on the Web server and a combination of computer applications operated in a Microsoft Windows environment. A quasi-experimental, 1-group pretest-posttest design was used to measure the efficacy of the ES-MVN in 16 neonatal intensive care unit registered nurses. Case simulations were used to test the nurses' clinical judgment performance. The results showed a significant increase in the nurses' performance scores of diagnoses and managed care after using the ES-MVN (t[15] = 17.21, P = .0001). The nurses' scores for perceptions of their information access capability and clinical judgment ability after receiving the ES-MVN were significantly higher than before installing the ES-MVN in the neonatal intensive care unit (ts[15] = 6.91 and = 17.53, Ps = .0001, respectively). The findings suggest the usefulness of the computer expert system as an effective tool to support nurses' clinical judgment in critical care situations and to provide access to information at the practice site.

Adult↗

PubMed1989

Performance evaluation of medical expert systems using ROC curves.

This paper presents a performance evaluation of the diagnostic accuracy of the medical expert system CADIAG-2/PANCREAS. The study included 47 clinical cases from a university hospital with 51 diagnosis of pancreatic diseases (four patients had two pancreatic diseases). As gold standard, the histologically or clinically confirmed diagnoses were assumed. Performance was studied along three lines: (a) each case was evaluated twice, first, by restricting patient data to history, physical examination, and basic laboratory tests and, second, by utilizing the complete set of data including also special laboratory tests. US. X ray, CT-scan, ECG, and biopsy, if available: (b) considering CADIAG-2's hypotheses generation, each evaluation series was also carried out twice, first, by testing whether the gold standard was the first diagnosis in the ranked list of hypothesis and, second, whether the gold standard was among the hypotheses: (c) receiver operating characteristic (ROC) curves were determined by varying an internal threshold which determined the extent of CADIAG-2's diagnostic hypotheses generation. The evaluation showed that CADIAG-2's initial list of diagnostic hypotheses, based on patient history, physical examination, and basic laboratory tests usually has already included the gold standard diagnosis and thus an application of CADIAG-2 at a very early stage of the diagnostic process seems achievable. Moreover, it turned out that given the complete set of patient's medical data the gold standard is usually ranked at the first place in the list of hypotheses. except for patients with chronic diseases where only unspecific findings are available. The last test series showed that ROC curves do not only allow optimal adjustment of the expert system's internal ad hoc decision criteria such as thresholds, weights, and scores but also provide a basis for better comparing the performance of different medical expert systems.

Diagnosis, Computer-Assisted↗

PubMed2000

Expert system assisted pharmacophore identification

An expert system for automatic perception of pharmacophoric groups is presented. Important features include consideration of the protonation state at physiological pH and detection of potential tautomerism. This perception information is used in the generation of pharmacophores using clique detection.

Journal Article↗

PubMed2004

Computerized expert system for evaluation of automated visual fields from the Ischemic Optic Neuropathy Decompression Trial: methods, baseline fields, and six-month longitudinal follow-up.

PURPOSE: To validate a computerized expert system evaluating visual fields in a prospective clinical trial, the Ischemic Optic Neuropathy Decompression Trial (IONDT). To identify the pattern and within-pattern severity of field defects for study eyes at baseline and 6-month follow-up. DESIGN: Humphrey visual field (HVF) change was used as the outcome measure for a prospective, randomized, multicenter trial to test the null hypothesis that optic nerve sheath decompression was ineffective in treating nonarteritic anterior ischemic optic neuropathy and to ascertain the natural history of the disease. METHODS: An expert panel established criteria for the type and severity of visual field defects. Using these criteria, a rule-based computerized expert system interpreted HVF from baseline and 6-month visits for patients randomized to surgery or careful follow-up and for patients who were not randomized. RESULTS: A computerized expert system was devised and validated. The system was then used to analyze HVFs. The pattern of defects found at baseline for patients randomized to surgery did not differ from that of patients randomized to careful follow-up. The most common pattern of defect was a superior and inferior arcuate with central scotoma for randomized eyes (19.2%) and a superior and inferior arcuate for nonrandomized eyes (30.6%). Field patterns at 6 months and baseline were not different. For randomized study eyes, the superior altitudinal defects improved (P = .03), as did the inferior altitudinal defects (P = .01). For nonrandomized study eyes, only the inferior altitudinal defects improved (P = .02). No treatment effect was noted. CONCLUSIONS: A novel rule-based expert system successfully interpreted visual field defects at baseline of eyes enrolled in the IONDT.

Aged↗

PubMedDate not supplied

An inductive algorithm approach to knowledge acquisition for expert system development. A pilot study.

Knowledge acquisition, which consists of knowledge elicitation and knowledge representation, often is considered the weakest link in the design of expert systems. Systems frequently are built on the knowledge of one expert and require extensive use of knowledge engineering techniques to elicit this knowledge from the expert. Inductive algorithms are a potential alternative method of knowledge acquisition for expert system development. The aim of this pilot study was to examine the feasibility of applying machine learning techniques, specifically, inductive algorithms, to an existing research database as a method for knowledge elicitation and knowledge representation for expert system development. Two inductive algorithms (C4 and Classification and Regression Trees [CART]) that generate decision trees were selected for the analysis using a data set of 201 patients hospitalized for Pneumocystis carinii pneumonia. Neither C4 nor CART produced trees with an accuracy that was significantly better than the baseline accuracy (71.3%) for prediction of outcome in the data set. The mean accuracy of the C4 decision trees was below baseline and the mean accuracy of CART decision trees was 74.6%. The experts found both algorithms comprehensible, but not adequate, and identified important missing predictor variables. The study findings suggest that additional research is needed to examine the appropriate use of inductive algorithms in the transformation of nursing data and information into nursing knowledge.

Algorithms↗

PubMed2001

Diagnostic performance of an expert system for the interpretation of myocardial perfusion SPECT studies.

UNLABELLED: An expert system (PERFEX) developed for the computer-assisted interpretation of myocardial perfusion SPECT studies is now becoming widely available. To date, a systematic validation of the diagnostic performance of this expert system for the interpretation of myocardial perfusion SPECT studies has not been reported. METHODS: To validate PERFEX's ability to detect and locate coronary artery disease (CAD), we analyzed 655 stress/rest myocardial perfusion prospective SPECT studies in patients who also underwent coronary angiography. The patient population comprised CAD patients (n = 480) and healthy volunteers (n = 175) (449 men, 206 women). Data from 461 other patient studies were used to implement and refine 253 heuristic rules that best correlated the presence and location of left ventricular myocardial perfusion defects on SPECT studies with angiographically detected CAD and with human expert visual interpretations. Myocardial perfusion defects were automatically identified as segments with counts below sex-matched normal limits. PERFEX uses the certainty of the location, size, shape, and reversibility of the perfusion defects to infer the certainty of the presence and location of CAD. The visual interpretations of tomograms and polar maps, vessel stenosis from coronary angiography, and PERFEX interpretations were all accessed automatically from databases and were used to automatically generate comparisons between diagnostic approaches. RESULTS: Using the physician's reading as a gold standard, PERFEX's sensitivity and specificity levels for detection and localization of disease were, respectively, 83% and 73% for CAD, 76% and 66% for the left anterior descending artery, 90% and 70% for the left circumflex artery, and 74% and 79% for the right coronary artery. These results were extracted from a receiver operating characteristic curve using the average optimal input certainty factor. CONCLUSION: This study shows that the diagnostic performance of PERFEX for interpreting myocardial perfusion SPECT studies is comparable with that of nuclear medicine experts in detecting and locating CAD.

Adult↗

PubMedDate not supplied

A new expert system for histopathological diagnosis of human ovarian epithelial cancer.

A new rule-based expert system designed to assist pathologists in the histological diagnosis of human ovarian epithelial cancer has been developed. The system was implemented using the INSIGHT II+ EXPERT System Shell and runs on IBM compatible microcomputers. It operates using knowledge acquired through the literature, research and experience, formulated into a total of 195 IF-THEN rules. The WHO International Histological Classification of Tumours is being used throughout. The system can quickly focus on a differential problem, thereby reducing the time necessary to reach a conclusion. It has been tested on a set of 34 cases, previously examined by pathologists of the Metaxas Memorial Cancer Institute, and has been found to reach good agreement with the pathologists' diagnoses.

Epithelium↗

PubMed2004

A probabilistic expert system that provides automated mammographic-histologic correlation: initial experience.

OBJECTIVE: We sought to determine whether a probabilistic expert system can provide accurate automated imaging-histologic correlations to aid radiologists in assessing the concordance of mammographic findings with the results of imaging-guided breast biopsies. MATERIALS AND METHODS: We created a Bayesian network in which Breast Imaging Reporting and Data System (BI-RADS) descriptors are used to convey the level of suspicion of mammographic abnormalities. Our system is a computer model that links BI-RADS descriptors with diseases of the breast using probabilities derived from the literature. Mammographic findings are used to update pretest probabilities (prevalence of disease) into posttest probabilities applying Bayes' theorem. We evaluated the histologic results of 92 consecutive imaging-guided breast biopsies for concordance with the mammographic findings during radiology-pathology review sessions. First, radiologists with no knowledge of the biopsy results chose BI-RADS descriptors for the mammographic findings. After the histologic diagnosis was revealed, the radiologists assessed concordance between the pathologic results and the mammographic findings. We then input the information gathered from these sessions into the Bayesian network to produce an automated mammographic-histologic correlation. RESULTS: We had a sampling error rate of 1.1% (1/92 biopsies). Our expert system was able to integrate pathologic diagnoses and mammographic findings to obtain probabilities of sampling error, thereby enabling us to identify the incorrect pathologic diagnosis with 100% sensitivity while maintaining a specificity of 91%. CONCLUSION: Our probabilistic expert system has the potential to help radiologists in identifying breast biopsy results that are discordant with mammographic findings and discovering cases in which biopsy sampling errors may have occurred.

Adult↗

PubMed1997

A rule-based expert system for the automatic classification of DNA "ploidy" histograms measured by the CAS 200 image analysis system.

DNA "ploidy" histogram interpretation is one of the most important sources of variation in DNA image cytometry and is influenced by multiple technical factors such as scaling, selection of peaks, and variable classification criteria. A rule-based expert system was developed to automate and eliminate subjectivity from this interpretative process. Ninety-eight Feulgen stained histologic sections from patients with breast, colon, and lung cancer were measured with the CAS 200 image analysis system (Becton Dickinson, Santa Clara, CA); they included diploid (n = 42), aneuploid (n = 46), tetraploid (n = 7), and multiploid (n = 3) examples. The data was converted from listmode format into ASCII with the aid of CELLSHEET software (JVC Imaging, Elmhurst, IL). Individual microphotometric nuclear measurements were sorted to one of 64 bins based on DNA index. The 64 bins were then divided into 5 semi-arbitrarily defined ranges: hypodiploid, diploid, aneuploid, tetraploid, and hypertetraploid. The nuclear percentages in each range were calculated with EXCEL 4.0 (Microsoft, Redmond, WA). The histograms were divided into 2 equal sets: training and testing. The data from the training set were used to develop 16 IF-THEN rules to classify the histograms into diploid, aneuploid, or tetraploid. A macro was programmed in EXCEL to automate all these operations. The rule-based expert system classified correctly 45/50 histograms of the training set. Two tetraploid histograms were classified as aneuploid. Three multiploid histograms were classified as tetraploid. All histograms in the testing set were correctly classified by the expert system. The potential role of rule-based expert system technology for the objective classification of DNA "ploidy" histograms measured by image cytometry is discussed.

Automation↗

PubMed1989

Expert system approach to detection of epileptiform activity in the EEG.

An expert system for the automated detection of spikes and sharp waves in the EEG has been developed. The system consists of two distinct stages. The first is a feature extractor, written in the conventional procedural language Fortran, which uses parts of previously published spike-detection algorithms to produce a list of all spike-like occurrences in the EEG. The second stage, written in the production system language OPS5, reads the list and uses rules incorporating knowledge elicited from an electroencephalographer (EEGer) to confirm or exclude each of the possible spikes. Information such as the time of occurrence, polarity and channel relationship are used in this process. A summary of the detected epileptiform events is produced which is available to the EEGer in interpreting the EEG. The performance of the expert system is compared with an EEGer using a 320s segment from an EEG containing epileptiform activity. The system detected 19 events and missed seven (false negative) which the EEGer considered epileptiform. There were no false positive detections.

Biomedical Engineering↗

PubMed1996

Computer expert system for the histopathologic diagnosis of salivary gland neoplasms.

The design, development, and testing of a prototype interactive histopathologic expert system capable of diagnosing 15 types of primary salivary gland neoplasms is described. The system incorporates a multiple subprogram modular design and makes use of multiple reasoning methods including: data-driven and goal-directed rule-based reasoning, linear pattern recognition, and Bayesian classification. Its user interface incorporates both a "hypertext" context-sensitive information assistance facility and the video display of stored and digitized photomicrographic images. The system can report a differential diagnosis of its findings with assessment of its confidence in its diagnosis. The system's performance was evaluated in a series of tests. The results of a weighted kappa analysis of the system's diagnoses versus those of four oral pathologists for 20 salivary gland neoplasms indicated no statistical difference in diagnostic performance between the system and the human experts and each of the experts in relationship to the others (Wilcoxon rank sums test). A modified version of Turing's test of artificial intelligence demonstrated no statistically significant difference in the system's diagnoses versus the diagnosis of four human expert pathologists (Fisher's exact test). The knowledge and experience gained in the development and testing of the expert system described in this study have demonstrated the validity of histopathologic diagnostic expert systems in a selected area of oral pathology.

Bayes Theorem↗

PubMed1993

Exercise countermeasure protocol management expert system.

Exercise will be used primarily to countermeasure against deconditioning on extended space flight. In this paper we describe the development and evaluation of an expert system for exercise countermeasure protocol management. Currently, the system includes two major subsystems: baseline prescription and prescription adjustment. The baseline prescription subsystem is designed to provide initial exercise prescriptions while prescription adjustment subsystem is designed to modify the initial prescription based on the exercised progress. The system runs under three different environments: PC, SUN workstation, and Symbolic machine. The inference engine, baseline prescription module, prescription adjustment module and explanation module are developed under the Symbolic environment by using the ART (Automated Reasoning Tool) software. The Sun environment handles database management features and interfaces with PC environment to obtain physical and physiological data from exercise units on-board during the flight. Eight subjects' data have been used to evaluate the system performance by comparing the prescription of nine experienced exercise physiologists and the one prescribed by the expert system. The results of the validation test indicated that the performance of the expert system was acceptable.

Analysis of Variance↗

PubMed1990

GUUS an expert system in the intensive care unit.

In cooperation with the Rotterdam School of Management of the Erasmus University in Rotterdam, an expert system GUUS was developed in the intensive care unit of the Department of Thoracic Surgery of the Leiden University Hospital. This expert system is able to diagnose postoperative haemodynamic problems in patients after coronary artery surgery and can give suggestions for therapy. GUUS has been developed by using the expert system shell Acquaint. The knowledge base was initially based on existing protocols and interviews with the medical head of the intensive care unit. The first prototype was tested and modified by using actual patient cases collected via special questionnaires. Subsequently the performance of GUUS was tested in a single blind comparison with three human experts. The results of this test were encouraging and initiated further development and implementation of the system.

Artificial Intelligence↗

PubMedDate not supplied

Expert systems for parenteral development.

This article introduces and reviews the use of expert systems in parenteral development. Two case studies are reviewed, one from academia the other from industry. Where introduced and implemented, expert systems have generated significant benefits in terms of knowledge protection, cost reduction, training, consistency and improved communication.

Drug Contamination↗

PubMed2000

PSG-EXPERT. An expert system for the diagnosis of sleep disorders.

This paper describes PSG-EXPERT, an expert system in the domain of sleep disorders exploring polysomnographic data. The developed software tool is addressed from two points of view: (1)--as an integrated environment for the development of diagnosis-oriented expert systems; (2)--as an auxiliary diagnosis tool in the particular domain of sleep disorders. Developed over a Windows platform, this software tool extends one of the most popular shells--CLIPS (C Language Integrated Production System) with the following features: backward chaining engine; graph-based explanation facilities; knowledge editor including a fuzzy fact editor and a rules editor, with facts-rules integrity checking; belief revision mechanism; built-in case generator and validation module. It therefore provides graphical support for knowledge acquisition, edition, explanation and validation. From an application domain point of view, PSG-Expert is an auxiliary diagnosis system for sleep disorders based on polysomnographic data, that aims at assisting the medical expert in his diagnosis task by providing automatic analysis of polysomnographic data, summarising the results of this analysis in terms of a report of major findings and possible diagnosis consistent with the polysomnographic data. Sleep disorders classification follows the International Classification of Sleep Disorders. Major features of the system include: browsing on patients data records; structured navigation on Sleep Disorders descriptions according to ASDA definitions; internet links to related pages; diagnosis consistent with polysomnographic data; graphical user-interface including graph-based explanatory facilities; uncertainty modelling and belief revision; production of reports; connection to remote databases.

Computer Systems↗