Biomedical subjects

A Ohrn

Publications and source records attributed to A Ohrn.

7 recordsLinked to original sources

PubMed1999

Modelling prognostic power of cardiac tests using rough sets.

Rough sets (Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning about Data, Dordrecht: Kluwer Academic Publishers, 1991) is a relatively new approach to representing and reasoning with incomplete and uncertain knowledge. This article introduces the basic concepts of rough sets and Boolean reasoning (Brown FM. Boolean Reasoning: The Logic of Boolean Equations, Dordrecht: Kluwer Academic Publishers, 1990). A rough set framework is then set up to investigate the prognosis of cardiac events in a set of patients with chest pain that was earlier studied by Geleijnse et al. (J Am Coll Cardiol 1996;28(2):447-454). That study used logistic regression to find that the single most important independent predictor for future hard cardiac events (cardiac death or non-fatal myocardial infarction) was an abnormal scintigraphic scan pattern. However, performing a scintigraphic scan is a relatively expensive procedure, and may for some patients not really be fully necessary as knowledge of the outcome of the scan may be redundant with respect to making a prognosis. Using an approach based on rough sets, this paper explores how a patient group in need of a scintigraphic scan can be identified for subsequent modelling. Identification of such patients may potentially contribute to lowering the cost of medical care and to improving its quality since, virtually without loss of information, fewer patients may be referred for this procedure.

Adolescent↗

PubMed1999

Using Boolean reasoning to anonymize databases.

This paper investigates how Boolean reasoning can be used to make the records in a database anonymous. In a medical setting, this is of particular interest due to privacy issues and to prevent the possible misuse of confidential information. As electronic medical records and medical data repositories get more common and widespread, the issue of making sensitive data anonymous becomes increasingly important. A theoretically well-founded algorithm is proposed that via cell suppression can be used to make a database anonymous before releasing or sharing it to the outside world. The degree of anonymity can be tailored according to the specific needs of the recipient, and according to the amount of trust we place in the recipient. Furthermore, the required measure of anonymity can be specified as far down as to the individual objects in the database. The algorithm can also be used for anonymization relative to a particular piece of information, effectively blocking deterministic inferences about sensitive database fields.

Adult↗

PubMed1998

Improving machine learning performance by removing redundant cases in medical data sets.

Neural network models and other machine learning methods have successfully been applied to several medical classification problems. These models can be periodically refined and retrained as new cases become available. Since training neural networks by backpropagation is time consuming, it is desirable that a minimum number of representative cases be kept in the training set (i.e., redundant cases should be removed). The removal of redundant cases should be carefully monitored so that classification performance is not significantly affected. We made experiments on data removal on a data set of 700 patients suspected of having myocardial infarction and show that there is no statistical difference in classification performance (measured by the differences in areas under the ROC curve on two previously unknown sets of 553 and 500 cases) when as many as 86% of the cases are randomly removed. A proportional reduction in the amount of time required to train the neural network model is achieved.

Area Under Curve↗

PubMed1998

Comparison of multiple prediction models for ambulation following spinal cord injury.

Few studies have properly compared predictive performance of different models using the same medical data set. We developed and compared 3 models (logistic regression, neural networks, and rough sets) in the in prediction of ambulation at hospital discharge following spinal cord injury. We used the multi-center Spinal Cord Injury Model System database. All models performed well and had areas under the receiver operating characteristic curve in the 0.88-0.91 range. All models had sensitivity, specificity, and accuracy greater than 80% at ideal thresholds. The performance of neural network and logistic regression methods was not statistically different (p = 0.48). The rough sets classifier performed statistically worse than either the neural network or logistic regression models (p-values 0.002 and 0.015 respectively).

Acute Disease↗

PubMed1998

Building manageable rough set classifiers.

An interesting aspect of techniques for data mining and knowledge discovery is their potential for generating hypotheses by discovering underlying relationships buried in the data. However, the set of possible hypotheses is often very large and the extracted models may become prohibitively complex. It is therefore typically desirable to only consider the "strongest" hypotheses, so that smaller models can be obtained that also retain good classificatory capabilities. This paper outlines how rule-based classifiers based on rough set theory and Boolean reasoning that are both small and perform well can be developed. Applied to a real-world medical dataset, the final models are shown to exhibit good performance using only a subset of the available information. Furthermore, the number of resulting rules is low and enables practical a posteriori inspection and interpretation of the models.

Classification↗

PubMed1997

Modelling cardiac patient set residuals using rough sets.

Many medical studies deal with the assessment of the prognostic or diagnostic power of some particular test with respect to some particular medical condition. However, even though a test is deemed to be powerful in this respect, the test may not be strictly needed to perform for everyone. If the test is costly or invasive, this issue is of particular interest. This paper presents a methodology based on rough set theory and Boolean reasoning that can be used to identify those patients for whom performing the test is redundant or superfluous. Furthermore, the methodology enables one to automatically construct a set of descriptive and minimal if-then rules that model the patient group in need of the test. A reanalysis of a previously published real-world dataset of patients with chest pain is used as a case study.

Coronary Disease↗

PubMedDate not supplied

Rough sets: a knowledge discovery technique for multifactorial medical outcomes.

Rough sets is a fairly new and promising technique for data mining and knowledge discovery from databases. This tutorial article presents the fundamentals of rough set theory in a nontechnical manner and outlines how the technique can be used to extract minimal if-then rules from tables of empirical data that either fully or approximately describe given example classifications. An example application for prediction of ambulation for patients with spinal cord injury is given. Because such rules are readily interpretable, they can be inspected to yield possible new insight into how various contributing factors interact and, thus, serve as hypothesis generators for further research. Additionally, the set of mined rules may function as a classifier of new, unseen cases.

Data Interpretation, Statistical↗