This paper is involved with the analysis of
serial measures concerning a set of patients.
For every patient, a variable number of series is measured, in different periods along time.
A usual way to analyze this kind of information is to reduce all series belonging to the same patient to some synthesis measure,as a mean curve per patient. However, this kind of synthesis measures involve some implicit hypothesis that often are not taken into account. If those hypothesis don’t hold, the results of analyzing the synthesis curves may be very far from reality. In this paper, a real data set concerning psychofisiological effects of electroconvulsive therapy which includes repeated serial measures per patient is analysed in two ways: first, using the classical approach of analysing one mean curve per patient; secondly, using a new methodology called KDSM. After all, results obtained in both cases are compared and some conclusions raise.
In Europe, and other developed areas, senior citizens are a fast growing part of population. This increases proportion of disabled persons and proportion of persons with reduced quality of life. The concept of disability itself is not always precise and quantifiable. To improve agreement on the concept of disability, the World Health Organization (WHO) developed the clinical test WHO Disability Assessment Schedule, (WHO-DASII) that includes physical, mental, and social wellbeing, as a generic measure of functioning. From the medical point of view, the purpose of this work is to extract knowledge about the different kinds of disabilities from the responses to the WHO-DAS II of a sample of patients from an Italian hospital. This Knowledge Discovery problem has been faced by using clustering based on rules, an hybrid AI and Statistics technique introduced by Gibert (1994), which combines some Inductive Learning (from AI) with clustering (from Statistics) to extract knowledge from certain complex domains in form of typical profiles. In this paper, the results of applying this technique to the WHODAS II results is presented together with a comparison of other more classical analysis approaches. Four profiles of increasing degree of disability are identified together with the main characteristics associated to them.