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G F Cooper

Publications and source records attributed to G F Cooper.

At least 37 records · Page 2Linked to original sources
PubMed1995

Evaluation of a belief-network-based reminder system that learns from utility feedback.

PRETRIEVE is a belief-network-based, unsolicited information-retrieval system that performs machine learning based on user feedback. We report here on the document-ordering and document-retrieval performance of PRETRIEVE. We developed a test collection of 410 judgments of document utility in a simulated medical order-entry context. We characterized the validity of these judgments, which were elicited from domain experts, by measuring interrater and intrarater reproducibility. We developed a measure of the quality of document orderings similar to the ROC-curve analysis used to evaluate document-retrieval systems. We found that the ordering performance of the PRETRIEVE system was (1) substantially better than random, (2) somewhat less than ideal, and (3) superior to that of versions of the PRETRIEVE system that used relevance feedback instead of utility feedback. Under a set of assumptions, which we make explicit, we found that the documents retrieved by a version of PRETRIEVE that modeled time cost were of higher utility than those retrieved by a similar rule-based system.

Artificial Intelligence↗
PubMed1995

Image engine: an integrated multimedia clinical information system.

Image Engine is a microcomputer-based system for the integration, storage, retrieval, and sharing of digitized clinical images. The system seeks to address the problem of integrating a wide range of clinically important images with the text-based electronic patient record. Rather than create a single, integrated database system for all clinical data, we are developing a separate image database system that creates real-time, dynamic links to other network-based clinical databases. To the user, this system will present an integrated multimedia representation of the patient record, providing access to both the image and text-based data required for effective clinical decision making.

Abstracting and Indexing↗
PubMed1995

Temporal reasoning abstractions in QMR.

A medical decision-support system (MDSS) employs either explicit or implicit temporal representation and reasoning (TRR). In this paper we first examine the factors that make explicit TRR necessary. We argue that for diagnostic MDSSs in large domains, such as internal medicine, implicit TRR is often sufficient for acceptable diagnostic performance. A necessary prerequisite for implementing implicit TRR is the identification of a set of proper TRR abstractions. We analyze the implicit TRR utilized in QMR, a MDSS operating in the domain of general internal medicine, and describe three classes of TRR abstractions. We discuss our findings in relation to work on temporal reasoning in medical informatics.

Animals↗
PubMed1994

A temporal analysis of QMR: abstracted temporal representation and reasoning and initial assessment of diagnostic performance trade-offs.

Explicit temporal representation and reasoning (TRR) in medical decision-support systems (MDSS) is generally considered to be a useful but often neglected aspect of system design and implementation. Given the great burden of explicit TRR both in knowledge acquisition and computational efficiency, developers of general-purpose large-scale systems typically utilize implicit (i.e., abstracted) forms of TRR. We are interested in understanding better the trade-offs of not incorporating explicit TRR in large general-purpose MDSS along the dimensions of system expressive power and diagnostic accuracy. In particular, we examine the types of abstracted TRR employed in QMR, a diagnostic system in the domain of general internal medicine, and the high-level effects of such an implicit treatment of time in the system's diagnostic performance. We present our findings and discuss implications for MDSS design and implementation practices.

Artificial Intelligence↗
PubMed1993

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 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.

Bayes Theorem↗
PubMed1993

Data explorer: a prototype expert system for statistical analysis.

The inadequate analysis of medical research data, due mainly to the unavailability of local statistical expertise, seriously jeopardizes the quality of new medical knowledge. Data Explorer is a prototype Expert System that builds on the versatility and power of existing statistical software, to provide automatic analyses and interpretation of medical data. The system draws much of its power by using belief network methods in place of more traditional, but difficult to automate, classical multivariate statistical techniques. Data Explorer identifies statistically significant relationships among variables, and using power-size analysis, belief network inference/learning and various explanatory techniques helps the user understand the importance of the findings. Finally the system can be used as a tool for the automatic development of predictive/diagnostic models from patient databases.

Artificial Intelligence↗
PubMed1993

Decision-theoretic information pretrieval: a generalization of reminding.

Reminder systems and clinical medical librarian services often provide information to clinicians without requiring that a clinician actively seek information. This characteristic may explain in part the effectiveness and high clinician acceptance of these systems. We term systems with this characteristic "information pretrieval systems" to distinguish them from information retrieval systems, which require a clinician to articulate an information need in the form of a query. Because of the increasing importance of information pretrieval systems in medical care, we have developed a decision-theoretic model of an ideal information pretrieval system. In this paper, we present this model and suggest its use as an analytic framework for understanding existing approaches, and as a formal basis for a functioning pretrieval system.

Decision Support Techniques↗
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↗
PubMed1987

Synthesis and gastric antisecretory properties of allenic 16-phenoxy-omega-tetranor prostaglandin E analogs.

In order to improve the modest oral activity of PGE2 as an inhibitor of gastric acid secretion, analogs were prepared and tested orally in histamine-challenged rats. Insertion of a double bond at C-4, resulting in the 4,5-allene analog of PGE1, gave a small increase in activity. Introduction of the omega-tetranor-16-phenoxy lower sidechain, a modification known to enhance activity in the PGF series, gave an eight-fold increase in activity. The analog having both modifications (enprostil, 2) showed a six hundred-fold increase in oral antisecretory activity over PGE2, which may reflect a potentiation effect. Modification of enprostil at C-1 (various esters) and at C-11 (11-methyl, 11-deoxy) generally resulted in compounds of high activity while modifications at other sites generally resulted in significant reductions in activity.

Animals↗