Relational Mining for Temporal Medical Data

R. Ichise and M. Numao (Japan)


Machine learning, Temporal mining, Active mining, Inductive Logic Programming


In managing medical data, handling time-series data, which contain irregularities, presents the greatest difficulty. In the present paper, we propose a first-order rule discovery method for handling such data. The present method is an attempt to use graph structure to represent time-series data and reduce the graph using specified rules for inducing hy pothesis. In order to evaluate the proposed method, we con ducted experiments using real-world medical data.

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