Parallel universes to improve the diagnosis of cardiac arrhythmias

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.