Dagstuhl Seminar Proceedings. 07181 - Parallel Universes and Local Patterns
Doi
Abstract
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