Title: Learning rules from multisource data for cardiac monitoring
Authors: Fromont, Elisa ×
Quiniou, R
Cordier, MO #
Issue Date: 2005
Publisher: Springer-verlag berlin
Series Title: Artificial intelligence in medicine, proceedings vol:3581 pages:484-493
Abstract: This paper aims at formalizing the concept of learning rules from multisource data in a cardiac monitoring context. Our method has been implemented and evaluated on learning from data describing cardiac behaviors from different viewpoints, here electrocardiograms and arterial blood pressure measures. In order to cope with the dimensionality problems of multisource learning, we propose an Inductive Logic Programming method using a two-step strategy. Firstly, rules are learned independently from each sources. Secondly, the learned rules are used to bias a new learning process from the aggregated data. The results show that the the proposed method is much more efficient than learning directly from the aggregated data. Furthermore, it yields rules having better or equal accuracy than rules obtained by monosource learning.
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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