Data mining techniques and applications in medicine.
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Artificial Intelligence↗
Biomedical subjects
B Zupan
Publications and source records attributed to B Zupan.
Relating clinical and neurophysiological assessment of spasticity by machine learning.
Spasticity following spinal cord injury (SCI) is most often assessed clinically using a five-point Ashworth score (AS). A more objective assessment of altered motor control may be achieved by using a comprehensive protocol based on a surface electromyographic (sEMG) activity recorded from thigh and leg muscles. However, the relationship between the clinical and neurophysiological assessments is still unknown. In this paper we employ three different classification methods to investigate this relationship. The experimental results indicate that, if the appropriate set of sEMG features is used, the neurophysiological assessment is related to clinical findings and can be used to predict the AS. A comprehensive sEMG assessment may be proven useful as an objective method of evaluating the effectiveness of various interventions and for follow-up of SCI patients.
Artificial Intelligence↗
Cognitive vulnerability in children at risk for depression.
Cognitive, developmental, and psychodynamic theories all hypothesize that negative self-concepts acquired in childhood may induce vulnerability to depression. Children at risk because of maternal major affective disorder, compared with children of medically ill and normal mothers, were examined for evidence of negative cognitions about themselves, and were found to have more negative self-concept, less positive self-schemas, and more negative attributional style. It was further predicted that negative cognitions about the self would be related to maternal depression and chronic stress, and to the quality of perceived and actual interactions with the mother. In general, the predicted associations were obtained, supporting speculations about how maternal affective disorder is associated with stress and with relatively negative and unsupportive relationships with children that in turn diminish children's self-regard.
Adolescent↗
Acquiring background knowledge for machine learning using function decomposition: a case study in rheumatology.
Domain or background knowledge is often needed in order to solve difficult problems of learning medical diagnostic rules. Earlier experiments have demonstrated the utility of background knowledge when learning rules for early diagnosis of rheumatic diseases. A particular form of background knowledge comprising typical co-occurrences of several groups of attributes was provided by a medical expert. This paper explores the possibility of automating the process of acquiring background knowledge of this kind and studies the utility of such methods in the problem domain of rheumatic diseases. A method based on function decomposition is proposed that identifies typical co-occurrences for a given set of attributes. The method is evaluated by comparing the typical co-occurrences it identifies as well as their contribution to the performance of machine learning algorithms, to the ones provided by a medical expert.
Algorithms↗