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SCOUT: information retrieval from full-text medical literature.

SCOUT is an expert system designed to aid the practicing physician in searching a full-text database of biomedical literature for information in the domain of oncology. In the clinical setting, an information-retrieval system needs to provide only a few relevant documents that address a patient-specific query. SCOUT models the expertise of an oncologist and an information specialist in a probabilistic inference system; it applies expert heuristics both in selecting the best source of information for a particular question and in developing an effective, source-specific search statement that retrieves only a minimal number of pertinent documents.

Expert Systems↗

Automated knowledge acquisition from clinical databases based on rough sets and attribute-oriented generalization.

Rule induction methods have been proposed in order to acquire knowledge automatically from databases. However, conventional approaches do not focus on the implementation of induced results into an expert system. In this paper, the author focuses not only on rule induction but also on its evaluation and presents a systematic approach from the former to the latter as follows. First, a rule induction system based on rough sets and attribute-oriented generalization is introduced and was applied to a database of congenital malformation to extract diagnostic rules. Then, by the use of the induced knowledge, an expert system which makes a differential diagnosis on congenital disorders is developed. Finally, this expert system was evaluated in an outpatient clinic, the results of which show that the system performs as well as a medical expert.

Algorithms↗

Finite state model of locomotion for functional electrical stimulation systems.

A finite state model of locomotion was developed to simplify a controller design for motor activities of handicapped humans. This paper presents a model developed for real time control of locomotion with functional electrical stimulation (FES) assistive systems. Hierarchical control of locomotion was adopted with three levels: voluntary, coordination and actuator level. This paper deals only with coordination level of control. In our previous studies we demonstrated that a skill-based expert system can be used for coordination level of control in multi-joint FES systems. Basic elements in this skill-based expert system are production rules. Production rules have the form of If-Then conditional expressions. A technique of automatic determination of these conditional expressions is presented in this paper. This technique for automatic synthesis of production rules uses fuzzy logic and artificial neural networks (ANN). The special class of fuzzy logic elements used in this research is called preferential neurons. The preferential neurons were used to estimate the relevance of each of the sensory inputs to the recognition of patterns defined as finite states. The combination of preferential neurons forms a preferential neural network. The preferential neural network belongs to a class of ANNs. The preferential neural network determined the set of finite states convenient for a skill-based expert system for different modalities of locomotion.

Ankle Joint↗

Exploring subjective vs. objective issues in the validation of computer-based critiquing advice.

Evaluation is an important part of the development of computer-based medical expert systems. Such evaluation may be particularly difficult when judging a critiquing system which responds to a proposed management strategy with a discussion of the advisability of that approach. DxCON is an expert system which produces a prose critique discussing the radiologic workup of obstructive jaundice. This paper describes DxCON, and its experimental validation by three independent judges. A central component of the validation involved allowing the judges to react to the system's advice in a quite flexible, unstructured fashion. This project provides a case study of how subjective issues impact both the design and implementation of a validation of a medical expert system whose output is explanatory prose.

Cholestasis↗

Probabilistic assessment of biodegradability based on metabolic pathways: catabol system.

A novel mechanistic modeling approach has been developed that assesses chemical biodegradability in a quantitative manner. It is an expert system predicting biotransformation pathway working together with a probabilistic model that calculates probabilities of the individual transformations. The expert system contains a library of hierarchically ordered individual transformations and matching substructure engine. The hierarchy in the expert system was set according to the descending order of the individual transformation probabilities. The integrated principal catabolic steps are derived from set of metabolic pathways predicted for each chemical from the training set and encompass more than one real biodegradation step to improve the speed of predictions. In the current work, we modeled O2 yield during OECD 302 C (MITI I) test. MITI-I database of 532 chemicals was used as a training set. To make biodegradability predictions, the model only needs structure of a chemical. The output is given as percentage of theoretical biological oxygen demand (BOD). The model allows for identifying potentially persistent catabolic intermediates and their molar amounts. The data in the training set agreed well with the calculated BODs (r2 = 0.90) in the entire range i.e. a good fit was observed for readily, intermediate and difficult to degrade chemicals. After introducing 60% ThOD as a cut off value the model predicted correctly 98% ready biodegradable structures and 96% not ready biodegradable structures. Crossvalidation by four times leaving 25% of data resulted in Q2 = 0.88 between observed and predicted values. Presented approach and obtained results were used to develop computer software for biodegradability prediction CATABOL.

Biodegradation, Environmental↗

Structure-activity relationships for rat and mouse LD50 of miscellaneous alcohols.

Structure-activity relationships between acute toxicities of 95 alcohols to rat and mouse (oral LD50) and four special sub-structure factors, hydroxyl number, carbon atom number were examined by means of expert system method. The results showed that the expert system based QSAR model was excellent for classification for miscellaneous alcohols (only 9 of them were wrong classified). It was also used to predict the toxicity of other 25 alcohols, and the false prediction rate was only 12%.

Alcohols↗

Preparing for change: concepts and education in medical informatics.

Medical informatics as a medical discipline has developed over the last decades in parallel with an even more amazing proliferative development in medicine. The question is raised whether this new science, based on formalized and methodological approaches, may contribute to the development of a general theory in medicine as a consequence of the recognition of the influence of control mechanisms and structured information in molecular biology. It is suggested that medical informatics dedicates research to problems 'inside medicine' and that curricula are developed which bring a basic understanding for medical informatics to the medical student. The following teaching is suggested: basic mandatory courses, electives and inclusions of aspect of medical informatics in the various parts of clinical teaching. The possibility is discussed that the resulting teaching approaches may also be used to convey knowledge in medicine: teaching concepts versus teaching details. Finally, a description of the functional topology of expert systems as they develop is attempted and brought into relation to the architecture of hospital information systems. The increasing importance of expert systems also raises the question of 'decisional trials' as verification procedures when these new tools enter medical practice.

Artificial Intelligence↗

Applications: how far have we come? Part II.

In this second of a two-part look at the evolution of critical issues and application areas, Computers in Healthcare Associate Editor Ellen Pollock examines how far integration, nursing systems, executive decision support and expert systems have come during the 1980s. Her top industry sources discuss the outlook for these applications in the near-term future, as well.

Computer Communication Networks↗

Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: a feasibility study.

BACKGROUND: Differentiation of melanoma from melanocytic nevi is difficult even for skin cancer specialists. This motivates interest in computer-assisted analysis of lesion images. OBJECTIVE: Our purpose was to offer fully automatic differentiation of melanoma from dysplastic and other melanocytic nevi through multispectral digital dermoscopy. METHOD: At 4 clinical centers, images were taken of pigmented lesions suspected of being melanoma before biopsy. Ten gray-level (MelaFind) images of each lesion were acquired, each in a different portion of the visible and near-infrared spectrum. The images of 63 melanomas (33 invasive, 30 in situ) and 183 melanocytic nevi (of which 111 were dysplastic) were processed automatically through a computer expert system to separate melanomas from nevi. The expert system used either a linear or a nonlinear classifier. The "gold standard" for training and testing these classifiers was concordant diagnosis by two dermatopathologists. RESULTS: On resubstitution, 100% sensitivity was achieved at 85% specificity with a 13-parameter linear classifier and 100%/73% with a 12-parameter nonlinear classifier. Under leave-one-out cross-validation, the linear classifier gave 100%/84% (sensitivity/specificity), whereas the nonlinear classifier gave 95%/68%. Infrared image features were significant, as were features based on wavelet analysis. CONCLUSION: Automatic differentiation of invasive and in situ melanomas from melanocytic nevi is feasible, through multispectral digital dermoscopy.

Diagnosis, Differential↗

Evaluation of Epilepsy Expert--a decision support system.

Epilepsy Expert is a decision support system based on the International Classification of Epilepsies and Epileptic Syndromes (1989). The aim of this study was to evaluate the Epilepsy Expert. First the diagnostic performance was validated. This was done in 3 stages: collection of the patient cases, determination of the 'correct diagnoses' and testing the system. How the users perceived the functionality of the system was studied by using an inquiry. Three physicians, experts of epilepsy, from different hospitals were asked to choose 10 patients. In the patient description was a short history, a detailed description of the seizure, EEG findings and their own diagnosis. Next, each expert made a diagnosis of the cases supplied by other experts by using the International Classification of Epilepsies and Epileptic Syndromes. The 'correct diagnosis' (so-called majority agreement) was the diagnosis given by the majority of the experts. The diagnosis of each expert was compared with the 'correct diagnosis'. The diagnoses obtained by the Epilepsy Expert were then compared with the 'correct diagnoses'. In the evaluation the expert physicians agreed on 37% of cases and all 3 disagreed on 17%. A majority agreed on 25 cases, which were used in the evaluation. In these 25 cases the experts' (A,B,C) diagnoses were correct or partly correct in 100, 64, 80% of cases, respectively. The program's diagnoses were correct or partly correct in 80% (module I) and 76% (modules IV and V) of cases. In the evaluation Epilepsy Expert was found to be only partly successful. The main reason for this was the weakness of the international classification. However, the program seems to be very close to the level of the experts. According to this limited inquiry Epilepsy Expert is not suitable for clinical use, because it is, for example, too simple and does not contain enough information.

Adult↗

Decision tree induction in the diagnosis of otoneurological diseases.

Expert systems have been applied in medicine as diagnostic aids and education tools. The construction of a knowledge base for an expert system may be a difficult task; to automate this task several machine learning methods have been developed. These methods can be also used in the refinement of knowledge bases for removing inconsistencies and redundancies, and for simplifying decision rules. In this study, decision tree induction was employed to acquire diagnostic knowledge for otoneurological diseases and to extract relevant parameters from the database of an otoneurological expert system ONE. The records of patients with benign positional vertigo, Meniere's disease, sudden deafness, traumatic vertigo, vestibular neuritis and vestibular schwannoma were retrieved from the database of ONE, and for each disease, decision trees were constructed. The study shows that decision tree induction is a useful technique for acquiring diagnostic knowledge for otoneurological diseases and for extracting relevant parameters from a large set of parameters.

Algorithms↗

[Classification method of deciduous-conifer mixed forest in jilin province based on GIS-TM remote sensing image].

To improve the accuracy of automatic classification and identification of TM remote sensing images in forest area, an expert system for automatically classifying and identifying deciduous-conifer mixed forest was built up, based on the GIS technique, quantitative analysis on the internal relations between geographic factors such as DEM and slope aspect and environment factors like soil type, and qualitative analysis on the spectrum information and preclassification information of sensing images, aimed to build a classification knowledge system. Taking the TM remote sensing image of Wangqing Forest Bureau in Jilin Province as an example, the study showed that this expert system could obviously reduce the influence of mixed pixel and terrain shadow. The classification precision of this system was increased by 14.22%, compared with that of Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) unsupervised classification, and the Kappa index was 0.7556, which could help to classify needle, deciduous and mixed forests. Introducing GIS data into the expert system could also solve the problem that TM remote sensing image could not do, due to the loss of correct spectrum value in cloudy and shady area.

China↗

Helping patients decide: from Hippocrates to videodiscs--an application for patients with low back pain.

This paper outlines a modular, microcomputer and videodisc expert system, for patients with mechanical low back pain. The system incorporate facts, rules, and methods to extract data, opinions, information, and user preferences. It supports individual patient needs with a broad knowledge base, and analytical modelling techniques that connect flexible inference structures and user choices. Videodisc based expert systems help break the bottleneck in relevant medical knowledge representation. The expert system program serves the user in the capacity of teacher, confidant, interpreter, diagnostician, and adviser.

Adult↗

Computerized diagnosis: implications for clinical education.

The advent of the small computer as a basic clinical tool will have a significant impact on clinical practice and medical training. The application of probability theory to clinical diagnosis has led to the development of several practical diagnostic programs which run on small computers. Expert systems--interactive programs which function as 'electronic consultants'--have now been successfully developed for a number of clinical situations. Experience with two of these, INTERNIST/CADUCEUS and MYCIN, has provided insight into problems and prospects for expert systems in medicine. Less complex programs, particularly those employing clinical prediction rules, and expert system shells, seem well suited for clinical environments. Although computerized medical diagnosis holds great promise as an aid to clinicians, its success will largely be determined by the quality of the information that clinicians provide for analysis. A brief review of the status of bedside diagnosis reveals that data-gathering strategies and techniques must be better understood. In order to take full advantage of computer programs for diagnosis, basic diagnostic skills must be more heavily emphasized in clinical training.

Boston↗

FIGENIX: intelligent automation of genomic annotation: expertise integration in a new software platform.

BACKGROUND: Two of the main objectives of the genomic and post-genomic era are to structurally and functionally annotate genomes which consists of detecting genes' position and structure, and inferring their function (as well as of other features of genomes). Structural and functional annotation both require the complex chaining of numerous different software, algorithms and methods under the supervision of a biologist. The automation of these pipelines is necessary to manage huge amounts of data released by sequencing projects. Several pipelines already automate some of these complex chaining but still necessitate an important contribution of biologists for supervising and controlling the results at various steps. RESULTS: Here we propose an innovative automated platform, FIGENIX, which includes an expert system capable to substitute to human expertise at several key steps. FIGENIX currently automates complex pipelines of structural and functional annotation under the supervision of the expert system (which allows for example to make key decisions, check intermediate results or refine the dataset). The quality of the results produced by FIGENIX is comparable to those obtained by expert biologists with a drastic gain in terms of time costs and avoidance of errors due to the human manipulation of data. CONCLUSION: The core engine and expert system of the FIGENIX platform currently handle complex annotation processes of broad interest for the genomic community. They could be easily adapted to new, or more specialized pipelines, such as for example the annotation of miRNAs, the classification of complex multigenic families, annotation of regulatory elements and other genomic features of interest.

Automation↗

The design of a rule-based clinical event monitor in a multi-vendor hospital computing environment.

The Clinical Event Monitor (CEM) described here is a prototype system designed to explore the issues involved in building an institutional CEM that permits rapid, automated evaluation of clinical transactions and notification to clinicians of exceptional events in a multi-vendor computing environment. The CEM uses expert systems, database, and systems integration techniques. Ancillary (departmental) applications, including as Patient Registration, Laboratories, and Pharmacy have been licensed from commercial vendors. Application-to-application and application-to-database interfaces were built to mirror subsets of the ancillary patient databases into an institutional relational database (Oracle). The CEM receives registration updates via an HL7 message and evaluates data dependencies in rules via an interface to the relational database. The CEM engine was built using Nexpert, a commercially available expert system shell. Our short term goals were to: (1) build and maintain a patient census within the expert system environment via net based HL7 update broadcasts; (2) explore the data-driven features of Nexpert, (3) deliver prototype exception reports. This paper describes in general terms the design features of the CEM and in detail the features of a patient registry to NEXPERT bridge (from Oracle via HL7 structured transactions to NEXPERT) and the delivery of exception reports.

Databases, Factual↗

[Pathobiochemistry of galactosemia and usefulness of the Gt system in expert opinions (author's transl)].

We investigated the genetic polymorphism of galactose-1-phosphate-uridyl-transferase (Gt) of 525 blood samples from Southern Germany. The gene frequencies of Gt1 are 0.9581 and of Gt2 0.0623. Comparing the gene frequencies of galactosemia (0.006), GtD (0.06), Gt1 (0.9581) and Gt2 (0.0623), we see that the gene frequencies of GtD and Gt2 are nearly identical. In expert opinions that Gt is a system of insufficient information.

Electrophoresis, Agar Gel↗