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Biomedical subjects

G F Cooper

Publications and source records attributed to G F Cooper.

13 recordsLinked to original sources
PubMed1992

Evaluation of a Meta-1-based automatic indexing method for medical documents.

This paper describes MetaIndex, an automatic indexing program that creates symbolic representations of documents for the purpose of document retrieval. MetaIndex uses a simple transition network parser to recognize a language that is derived from the set of main concepts in the Unified Medical Language System Metathesaurus (Meta-1). MetaIndex uses a hierarchy of medical concepts, also derived from Meta-1, to represent the content of documents. The goal of this approach is to improve document retrieval performance by better representation of documents. An evaluation method is described, and the performance of MetaIndex on the task of indexing the Slice of Life medical image collection is reported.

Abstracting and Indexing↗
PubMed1992

An evaluation of explanations of probabilistic inference.

Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use. Although many researchers in artificial intelligence have explored the automatic generation of explanations for decision-support systems based on symbolic reasoning, research in automated explanation of probabilistic results has been limited. We present the results of an an evaluation study of INSITE, a program that explains the reasoning of decision-support systems based on Bayesian belief networks. In the domain of anesthesia, we compared subjects who had access to a belief network with explanations of the inference results, to control subjects who used the same belief network without explanations. We show that, compared to control subjects, the explanation subjects demonstrated greater diagnostic accuracy, were more confident about their conclusions, were more critical of the belief network, and found the presentation of the inference results more clear.

Anesthesia↗
PubMed1992

CHARTLINE: providing bibliographic references relevant to patient charts using the UMLS Metathesaurus Knowledge Sources.

A successful medical informatics program helps its users to match their information needs as closely and efficiently as possible to the capabilities of the system. CHARTLINE is a computer program whose input is a free text, "natural language" patient chart in ASCII format. Using the UMLS Metathesaurus Knowledge Sources, CHARTLINE can suggest bibliographic references relevant to the patient case described in the chart. The program does not attempt to "understand" the natural language content of the chart. CHARTLINE only recognizes UMLS Metathesaurus Main Concept terms (or their synonyms) as they occur in the medical text, since those terms represent the tokens used to index the literature. The program depends on user feedback to determine which topics of a large number of potentially relevant subjects are of interest to the user.

Diagnosis, Computer-Assisted↗
PubMed1991

Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms.

In Part I of this two-part series, we report the design of a probabilistic reformulation of the Quick Medical Reference (QMR) diagnostic decision-support tool. We describe a two-level multiply connected belief-network representation of the QMR knowledge base of internal medicine. In the belief-network representation of the QMR knowledge base, we use probabilities derived from the QMR disease profiles, from QMR imports of findings, and from National Center for Health Statistics hospital-discharge statistics. We use a stochastic simulation algorithm for inference on the belief network. This algorithm computes estimates of the posterior marginal probabilities of diseases given a set of findings. In Part II of the series, we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.

Algorithms↗
PubMed1991

Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. II. Evaluation of diagnostic performance.

We have developed a probabilistic reformulation of the Quick Medical Reference (QMR) system. In Part I of this two-part series, we described a two-level, multiply connected belief-network representation of the QMR knowledge base and a simulation algorithm to perform probabilistic inference on the reformulated knowledge base. In Part II of this series, we report on an evaluation of the probabilistic QMR, in which we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.

Algorithms↗
PubMed1991

Algorithms for Bayesian belief-network precomputation.

Bayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference. We first present the necessary background, and then present algorithms for producing caches based on metrics of expected probability and expected utility. We show how these algorithms can be applied to a moderately complex belief network, and present directions for future research.

Algorithms↗
PubMed1990

Hypermedia and randomized algorithms for medical expert systems.

KNET is an environment for constructing probabilistic, knowledge-intensive systems within the axiomatic framework of decision theory. The KNET architecture defines a complete separation between the hypermedia user interface on the one hand, and the representation and management of expert opinion on the other. KNET offers a choice of algorithms for probabilistic inference. We and our coworkers have used KNET to build consultation systems for lymph-node pathology, bone-marrow transplantation therapy, clinical epidemiology, and alarm management in the intensive-care unit. Most important, KNET contains a randomized approximation scheme (RAS) for the difficult and almost certainly intractable problem of Bayesian inference. Our algorithm can, in many circumstances, perform efficient approximate inference in large and richly interconnected models of medical diagnosis. In this article, we describe the architecture of KNET, construct a randomized algorithm for probabilistic inference, and analyze the algorithm's performance. Finally, we characterize our algorithms' empiric behavior and explore its potential for parallel speedups. From design to implementation, then, KNET demonstrates the crucial interaction between theoretical computer science and medical informatics.

Algorithms↗
PubMed1990

EP3, but not EP2, FP, or TP prostanoid-receptor stimulation may reduce intraocular pressure.

Stimulation of DP, but not TP or FP, prostanoid receptors has previously been shown to reduce intraocular pressure (IOP) in rabbits. However the role of EP receptors (EP1, EP2, and EP3 subtypes) has not been studied extensively. Sulprostone, RS-61565, and RS-20216 have been studied for effects on rabbit IOP, and their prostanoid-receptor profiles characterized. The data suggest that the EP3, but not EP2, FP, or TP activity of these agonists correlated with the intraocular hypotensive effects. Moreover, RS-20216 lowered IOP at a dose of 5 micrograms for up to 12 hr after administration. In contrast to PGE1 and PGE2, which elicited both hyper- and hypotensive responses, sulprostone, RS-61565, and RS-20216 elicited only a hypotensive responses with no signs of ocular irritation. Thus stimulation of the EP3 receptor results in a lowering of IOP in rabbits. Compounds specific for this receptor subtype may act as novel therapeutic agents for the treatment of glaucoma.

15-Hydroxy-11 alpha,9 alpha-(epoxymethano)prosta-5↗
PubMed1989

Effects of the natural and unnatural isomers and degradation products of enprostil on gastric acid secretion and gastrointestinal motility in the rat.

The gastric antisecretory and gastrointestinal (GI) motility activity of the natural and unnatural allenic isomers and degradation products of enprostil (methyl(+/-)-7-[(1R*,2R*,3R*)-3-hydroxy-2-[(E)-(3R*)-3-hydroxy-4-phenoxy - 1-butenyl]-5-oxocyclopentyl]-4,5-heptadienoate, RS-84135-004) were studied in the rat. The natural R-allenic isomer of enprostil was the most potent antisecretory compound. The 8-iso-enprostil, enprostil free acid, and the 5-acetylene isomer had somewhat less activity while the other compounds were relatively inactive. The natural and unnatural allenic isomers increased intestinal dye transit with the same rank potency as the gastric antisecretory activity. Enprostil, 8-iso-enprostil, prostaglandin A-enprostil and enprostil free acid, all increased intestinal dye transit.

Animals↗
PubMedDate not supplied

Case-based tutoring from a medical knowledge base.

The past decade has seen the emergence of programs that make use of large knowledge bases to assist physicians in diagnosis within the general field of internal medicine. One such program, Internist-I, contains knowledge about over 600 diseases, covering a significant proportion of internal medicine. This paper describes the process of converting a subset of this knowledge base--in the area of cardiovascular diseases--into a probabilistic format, and the use of this resulting knowledge base to teach medical diagnostic knowledge. The system (called KBSimulator--for Knowledge-Based patient Simulator) generates simulated patient cases and uses these cases as a focal point from which to teach medical knowledge. This project demonstrates the feasibility of building an intelligent, flexible instructional system that uses a knowledge base constructed primarily for medical diagnosis.

Artificial Intelligence↗