Learning rules with complex temporal patterns in biomedical domains

Authors:
Lucia Sacchi;Riccardo Bellazzi;Cristiana Larizza;Riccardo Porreca;Paolo Magni
Affiliations:
Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy;Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy;Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy;Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy;Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy
Venue:
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Year:
2005

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Abstract

This paper presents a novel algorithm for extracting rules expressing complex patterns from temporal data. Typically, a temporal rule describes a temporal relationship between the antecedent and the consequent, which are often time-stamped events. In this paper we introduce a new method to learn rules with complex temporal patterns in both the antecedent and the consequent, which can be applied in a variety of biomedical domains. Within the proposed approach, the user defines a set of complex interesting patterns that will constitute the basis for the construction of the temporal rules. Such complex patterns are represented with a Temporal Abstraction formalism. An APRIORI-like algorithm then extracts precedence temporal relationships between the complex patterns. The paper presents the results obtained by the rule extraction algorithm in two different biomedical applications. The first domain is the analysis of time series coming from the monitoring of hemodialysis sessions, while the other deals with the biological problem of inferring regulatory networks from gene expression data.