Mining Physiological Data for Discovering Temporal Patterns on Disease Stages

Authors:
Corrado Loglisci;Michelangelo Ceci;Donato Malerba
Affiliations:
Department of Computer Science, University of Bari "Aldo Moro", Italy, email: {loglisci, ceci, malerba}@di.uniba.it;Department of Computer Science, University of Bari "Aldo Moro", Italy, email: {loglisci, ceci, malerba}@di.uniba.it;Department of Computer Science, University of Bari "Aldo Moro", Italy, email: {loglisci, ceci, malerba}@di.uniba.it
Venue:
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Year:
2010

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Abstract

Analyzing physiological data can be of great importance in unearthing information on the course of a disease. In this paper we propose a data mining approach to analyze these data and acquire knowledge, in the form of temporal patterns, on the physiological events which can frequently trigger particular stages of disease. The application to the sleep sickness scenario is addressed to discover patterns, expressed in terms of breathing and cardiovascular system time-annotated disorders, which may trigger particular sleep stages.