Aleksander Øhrn

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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(More)
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(More)
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(More)
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(More)
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(More)
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(More)
Search engines traditionally index unstructured text and return ranked lists of documents that match a given query. As search engines functionally move in the direction of traditional databases and offer scalable and efficient handling also of structured textual and numerical data, this way of presenting results needs to be revisited and augmented with(More)
A medical database with 257 patients thought to have acute appendicitis has been analyzed. Binary classifiers composed of very simple univari-ate if-then classification rules (1R rules) were synthesized, and are shown to perform well for determining the true disease status. Discriminatory performance was measured by the area under the receiver operating(More)