Biomedical subjects

G F Cooper

Publications and source records attributed to G F Cooper.

At least 19 recordsLinked to original sources

PubMed2002

Discovery of causal relationships in a gene-regulation pathway from a mixture of experimental and observational DNA microarray data.

This paper reports the methods and results of a computer-based search for causal relationships in the gene-regulation pathway of galactose metabolism in the yeast Saccharomyces cerevisiae. The search uses recently published data from cDNA microarray experiments. A Bayesian method was applied to learn causal networks from a mixture of observational and experimental gene-expression data. The observational data were gene-expression levels obtained from unmanipulated "wild-type" cells. The experimental data were produced by deleting ("knocking out") genes and observing the expression levels of other genes. Causal relations predicted from the analysis on 36 galactose gene pairs are reported and compared with the known galactose pathway. Additional exploratory analyses are also reported.

Animals↗

PubMed2001

A simple algorithm for identifying negated findings and diseases in discharge summaries.

Narrative reports in medical records contain a wealth of information that may augment structured data for managing patient information and predicting trends in diseases. Pertinent negatives are evident in text but are not usually indexed in structured databases. The objective of the study reported here was to test a simple algorithm for determining whether a finding or disease mentioned within narrative medical reports is present or absent. We developed a simple regular expression algorithm called NegEx that implements several phrases indicating negation, filters out sentences containing phrases that falsely appear to be negation phrases, and limits the scope of the negation phrases. We compared NegEx against a baseline algorithm that has a limited set of negation phrases and a simpler notion of scope. In a test of 1235 findings and diseases in 1000 sentences taken from discharge summaries indexed by physicians, NegEx had a specificity of 94.5% (versus 85.3% for the baseline), a positive predictive value of 84.5% (versus 68.4% for the baseline) while maintaining a reasonable sensitivity of 77.8% (versus 88.3% for the baseline). We conclude that with little implementation effort a simple regular expression algorithm for determining whether a finding or disease is absent can identify a large portion of the pertinent negatives from discharge summaries.

Algorithms↗

PubMed2001

Evaluation of negation phrases in narrative clinical reports.

OBJECTIVE: Automatically identifying findings or diseases described in clinical textual reports requires determining whether clinical observations are present or absent. We evaluate the use of negation phrases and the frequency of negation in free-text clinical reports. METHODS: A simple negation algorithm was applied to ten types of clinical reports (n=42,160) dictated during July 2000. We counted how often each of 66 negation phrases was used to mark a clinical observation as absent. Physicians read a random sample of 400 sentences, and precision was calculated for the negation phrases. We measured what proportion of clinical observations were marked as absent. RESULTS: The negation algorithm was triggered by sixty negation phrases with just seven of the phrases accounting for 90% of the negations. The negation phrases received an overall precision of 97%, with "not" earning the lowest precision of 63%. Between 39% and 83% of all clinical observations were identified as absent by the negation algorithm, depending on the type of report analyzed. The most frequently used clinical observations were negated the majority of the time. CONCLUSION: Because clinical observations in textual patient records are frequently negated, identifying accurate negation phrases is important to any system processing these reports.

Algorithms↗

PubMed2000

Predicting ICU mortality: a comparison of stationary and nonstationary temporal models.

OBJECTIVE: This study evaluates the effectiveness of the stationarity assumption in predicting the mortality of intensive care unit (ICU) patients at the ICU discharge. DESIGN: This is a comparative study. A stationary temporal Bayesian network learned from data was compared to a set of (33) nonstationary temporal Bayesian networks learned from data. A process observed as a sequence of events is stationary if its stochastic properties stay the same when the sequence is shifted in a positive or negative direction by a constant time parameter. The temporal Bayesian networks forecast mortalities of patients, where each patient has one record per day. The predictive performance of the stationary model is compared with nonstationary models using the area under the receiver operating characteristics (ROC) curves. RESULTS: The stationary model usually performed best. However, one nonstationary model using large data sets performed significantly better than the stationary model. CONCLUSION: Results suggest that using a combination of stationary and nonstationary models may predict better than using either alone.

Artificial Intelligence↗

PubMed2000

Causal discovery from medical textual data.

Medical records usually incorporate investigative reports, historical notes, patient encounters or discharge summaries as textual data. This study focused on learning causal relationships from intensive care unit (ICU) discharge summaries of 1611 patients. Identification of the causal factors of clinical conditions and outcomes can help us formulate better management, prevention and control strategies for the improvement of health care. For causal discovery we applied the Local Causal Discovery (LCD) algorithm, which uses the framework of causal Bayesian Networks to represent causal relationships among model variables. LCD takes as input a dataset and outputs causes of the form variable Y causally influences variable Z. Using the words that occur in the discharge summaries as attributes for input, LCD output 8 purported causal relationships. The relationships ranked as most probable subjectively appear to be most causally plausible.

Algorithms↗

PubMed1999

A study in causal discovery from population-based infant birth and death records.

In the domain of medicine, identification of the causal factors of diseases and outcomes, helps us formulate better management, prevention and control strategies for the improvement of health care. With the goal of exploring, evaluating and refining techniques to learn causal relationships from observational data, such as data routinely collected in healthcare settings, we focused on investigating factors that may contribute causally to infant mortality in the United States. We used the U.S. Linked Birth/Infant Death dataset for 1991 with more than four million records and about 200 variables for each record. Our sample consisted of 41,155 records randomly selected from the whole dataset. Each record had maternal, paternal and child factors and the outcome at the end of the first year--whether the infant survived or not. For causal discovery we used a modified Local Causal Discovery (LCD2) algorithm, which uses the framework of causal Bayesian Networks to represent causal relationships among model variables. LCD2 takes as input a dataset and outputs causes of the form variable X causes variable Y. Using the infant birth and death dataset as input, LCD2 output nine purported causal relationships. Eight out of the nine relationships seem plausible. Even though we have not yet discovered a clinically novel causal link, we plan to look for novel causal pathways using the full sample after refining the algorithm and developing a more efficient implementation.

Algorithms↗

PubMed1999

Identifying patient subgroups with simple Bayes'.

Medical records can form the basis of retrospective studies, be used to evaluate hospital practices and guidelines, and provide examples for teaching medicine. Each of these tasks presumes the ability to accurately identify patient subgroups. We describe a method for selecting patient subgroups based on the text of their medical records and demonstrate its effectiveness. We also describe a modification of the basic system that does not assume the existence of a preclassified training set, and illustrate its effectiveness in one retrieval task.

Artificial Intelligence↗

PubMed1998

Temporal representation design principles: an assessment in the domain of liver transplantation.

Time modeling is an important aspect of medical decision-support systems engineering. At the core of effective time modeling lies the challenge of proper knowledge representation design. In this paper, we focus on two important principles for effective time-modeling languages: (a) hybrid temporal representation, and (b) dynamic temporal abstraction. To explore the significance of these design principles, we extend a previously-defined formalism (single-granularity modifiable temporal belief networks--MTBN-SGs) to accommodate multiple temporal granularities and dynamic query and domain-specific model creation. We call the new formalism multiple-granularity MTBNs (MTBN-MGs). We develop a prototype system for modeling aspects of liver transplantation and analyze the resulting model with respect to its representation power, representational tractability, and inferential tractability. Our experiment demonstrates that the design of formalisms is crucial for effective time modeling. In particular: (i) Hybrid temporal representation is a desirable property of time-modeling languages because it makes knowledge acquisition easier, and increases representational tractability. (ii) Dynamic temporal abstraction improves inferential and representational tractability significantly. We discuss a high-level procedure for extending existing languages to incorporate hybrid temporal representation and dynamic temporal abstraction.

Computer Simulation↗

PubMed1998

Using computer modeling to help identify patient subgroups in clinical data repositories.

OBJECTIVE: The ability to accurately and efficiently identify patient cases of interest in a hospital information system has many important clinical, research, educational and administrative uses. The identification of cases of interest sometimes can be difficult. This paper describes a two-stage method for searching for cases of interest. DESIGN: First, a Boolean search is performed using coded database variables. The user classifies the retrieved cases as being of interest or not. Second, based on the user-classified cases, a computer model of the patient cases of interest is constructed. The model is then used to help locate additional cases. These cases provide an augmented training set for constructing a new computer model of the cases of interest. This cycle of modeling and user classification continues until halted by the user. MEASUREMENTS: This paper describes a pilot study in which this method is used to identify the records of patients who have venous thrombosis. RESULTS: The results indicate that computer modeling enhances the identification of patient cases of interest.

Bayes Theorem↗

PubMed1998

The impact of modeling the dependencies among patient findings on classification accuracy and calibration.

We present a new Bayesian classifier for computer-aided diagnosis. The new classifier builds upon the naive-Bayes classifier, and models the dependencies among patient findings in an attempt to improve its performance, both in terms of classification accuracy and in terms of calibration of the estimated probabilities. This work finds motivation in the argument that highly calibrated probabilities are necessary for the clinician to be able to rely on the model's recommendations. Experimental results are presented, supporting the conclusion that modeling the dependencies among findings improves calibration.

Bayes Theorem↗

PubMed1997

Representing and developing temporally abstracted knowledge as a means towards facilitating time modeling in medical decision-support systems.

The utilization of the appropriate level of temporal abstraction is an important aspect of time modeling. We discuss some aspects of the relation of temporal abstraction to important knowledge engineering parameters such as model correctness, ease of model specification, knowledge availability, query completeness, inference tractability, and semantic clarity. We propose that versatile and efficient time-modeling formalisms should encompass ways to represent and reason at more than one level of abstraction, and we discuss such a hybrid formalism. Although many research efforts have concentrated on the automation of specific temporal abstractions, much research needs to be done in understanding and developing provably optimal abstractions. We provide an initial framework for studying this problem in a manner that is independent of the particular problem domain and knowledge representation, and suggest several research challenges that appear worth pursuing.

Artificial Intelligence↗

PubMed1997

An evaluation of machine-learning methods for predicting pneumonia mortality.

This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted survival as a function of the fraction of patients predicted to survive. This metric is useful in assessing a model's potential to assist a clinician in deciding whether to treat a given patient in the hospital or at home. We examined the error rates of the models when predicting that a given fraction of patients will survive. We examined survival fractions between 0.1 and 0.6. Over this range, each model's predictive error rate was within 1% of the error rate of every other model. When predicting that approximately 30% of the patients will survive, all the models have an error rate of less than 1.5%. The models are distinguished more by the number of variables and parameters that they contain than by their error rates; these differences suggest which models may be the most amenable to future implementation as paper-based guidelines.

Artificial Intelligence↗

PubMed1996

Human causal discovery from observational data.

Utilizing Bayesian belief networks as a model of causality, we examined medical students' ability to discover causal relationships from observational data. Nine sets of patient cases were generated from relatively simple causal belief networks by stochastic simulation. Twenty participants examined the data sets and attempted to discover the underlying causal relationships. Performance was poor in general, except at discovering the absence of a causal relationship. This work supports the potential for combining human and computer methods for causal discovery.

Bayes Theorem↗

PubMed1996

Representing CARE rules in a decision-theoretic formalism.

Improvement in the performance of reminder systems may be facilitated by the use of new representations. A decision-theoretic representation, for example, may enable a reminder system to represent and reason about the probabilities that a reminder will be a true or a false alarm and the relative utilities of these events. We extended a previously described decision-theoretic model to include such events. The model now represents explicitly the uncertainty, costs, and benefits of sending a reminder. We also extended the model to remove an assumption of reminder independence. As a step towards testing a hypothesis that this approach will support better performance than a rule-based approach, we analyzed a set of CARE rules and showed that our representation can represent these rules.

Decision Support Techniques↗

PubMed1995

Progress in the development of trauma systems in the United States. Results of a national survey.

OBJECTIVE: To examine the status of trauma system development and key structural and operational characteristics of these systems. DESIGN AND SETTING: National survey of trauma systems with enabling state statute, regulation, or executive orders and for which designated trauma centers were present. PARTICIPANTS: Trauma system administrators and directors of 37 state and regional organizations that had legal authority to administer trauma systems, which represented a response rate of 90.2%. MAIN OUTCOME MEASURES: Trauma system components that had been implemented or were under development. RESULTS: From 1988 to 1993, the number of states meeting one set of criteria for a complete trauma system criteria increased from two to five. The most common deficiency in establishing trauma systems was failure to limit the number of designated trauma centers based on community need. Although most existing trauma systems have developed formal processes for designating trauma centers, prehospital triage protocols to allow hospital bypass, and centralized trauma registries, several systems lack standardized policies for interhospital transfer and systemwide evaluation. CONCLUSION: State and regional organizations have accomplished a great deal but still have substantial work ahead in developing comprehensive trauma systems. Research is needed to better understand the relationship between trauma volume and outcomes of care as well as the impact of trauma system structure and operational characteristics on care delivery. Improved measures of patient outcome are also needed so that effective system evaluation can take place.

Clinical Protocols↗

PubMed1995

Building a medical multimedia database system to integrate clinical information: an application of high-performance computing and communications technology.

The rapid growth of diagnostic-imaging technologies over the past two decades has dramatically increased the amount of nontextual data generated in clinical medicine. The architecture of traditional, text-oriented, clinical information systems has made the integration of digitized clinical images with the patient record problematic. Systems for the classification, retrieval, and integration of clinical images are in their infancy. Recent advances in high-performance computing, imaging, and networking technology now make it technologically and economically feasible to develop an integrated, multimedia, electronic patient record. As part of The National Library of Medicine's Biomedical Applications of High-Performance Computing and Communications program, we plan to develop Image Engine, a prototype microcomputer-based system for the storage, retrieval, integration, and sharing of a wide range of clinically important digital images. Images stored in the Image Engine database will be indexed and organized using the Unified Medical Language System Metathesaurus and will be dynamically linked to data in a text-based, clinical information system. We will evaluate Image Engine by initially implementing it in three clinical domains (oncology, gastroenterology, and clinical pathology) at the University of Pittsburgh Medical Center.

Abstracting and Indexing↗

PubMed1995

A new formalism for temporal modeling in medical decision-support systems.

We present a new mathematical formalism, which we call modifiable temporal belief networks (MTBNs) that extends the concept of an ordinary belief network (BN) to incorporate a dynamic causal structure and explicit temporal semantics. An important feature of MTBNs is that they allow portions of the model to be abstract and portions of it to be temporally explicit. We show how this property can lead to substantial knowledge acquisition and computational complexity savings. In addition to temporal modeling, the language of MTBNs can be an important analytical tool, as well as temporal language for causal discovery.

Decision Making, Computer-Assisted↗

PubMed1995

The use of misclassification costs to learn rule-based decision support models for cost-effective hospital admission strategies.

Cost-effective health care is at the forefront of today's important health-related issues. A research team at the University of Pittsburgh has been interested in lowering the cost of medical care by attempting to define a subset of patients with community-acquire pneumonia for whom outpatient therapy is appropriate and safe. Sensitivity and specificity requirements for this domain make it difficult to use rule-based learning algorithms with standard measures of performance based on accuracy. This paper describes the use of misclassification costs to assist a rule-based machine-learning program in deriving a decision-support aid for choosing outpatient therapy for patients with community-acquired pneumonia.

Algorithms↗