Knowledge discovery on functional disabilities: Clustering based on rules versus other approaches

K. Gibert, R. Annicchiarico, U. Cortés, C. Caltagirone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In Europe senior citizens are a fast growing part of population, increasing proportion of disabled persons and that of persons with reduced quality of life. The concept of disability itself is not always precise and quantifiable. To improve agreement on it, the World Health Organization (WHO) developed the clinical test WHO Disability Assessment Schedule, (WHO-DASII) that includes physical, mental, and social well-being, as a generic measure of functioning. From the medical point of view, the purpose of this work is to extract knowledge about performance of the WHO-DASII using 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 tipical profiles. In this paper, the results of applying this technique to the WHO-DASII results is presented together with a comparison of other more classical analysis approaches.

Original languageEnglish
Title of host publicationStudies in Health Technology and Informatics
Pages163-168
Number of pages6
Volume116
Publication statusPublished - 2005
Event19th International Congress of the European Federation for Medical Informatics, MIE 2005 - Geneva, Switzerland
Duration: Aug 28 2005Sep 1 2005

Other

Other19th International Congress of the European Federation for Medical Informatics, MIE 2005
CountrySwitzerland
CityGeneva
Period8/28/059/1/05

Keywords

  • Assessment
  • Clustering based on rules
  • Disability
  • Knowledge discovery
  • Knowledge-based applications in medicine
  • Neurological disease
  • Scale (clinical test)

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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