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Parallel universes to improve the diagnosis of cardiac arrhythmias Fromont, Elisa Quiniou, René Cordier, Marie-Odile Parallel universes inductive logic programming medical application declarative bias We are interested in using parallel universes to learn interpretable models that can be subsequently used to automatically diagnose cardiac arrythmias. In our study, parallel universes are heterogeneous sources such as electrocardiograms, blood pressure measurements, phonocardiograms etc. that give relevant information about the cardiac state of a patient. To learn interpretable rules, we use an inductive logic programming (ILP) method on a symbolic version of our data. Aggregating the symbolic data coming from all the sources before learning, increases both the number of possible relations that can be learned and the richness of the language. We propose a two-step strategy to deal with these dimensionality problems when using ILP. First, rules are learned independently in each universe. Second, the learned rules are used to bias a new learning process from the aggregated data. The results show that this method is much more efficient than learning directly from the aggregated data. Furthermore the good accuracy results confirm the benefits of using multiple sources when trying to improve the diagnosis of cardiac arrythmias. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Elisa Fromont and René Quiniou and Marie-Odile Cordier 2007 Is Part Of Dagstuhl Seminar Proceedings, Volume 7181, Parallel Universes and Local Patterns (2007) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.07181.7 urn:nbn:de:0030-drops-12600 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07181.7 eng https://creativecommons.org/licenses/by/4.0/legalcode