Learning rules from multisource data for cardiac monitoring

TL;DR

This paper introduces an ILP-based method for learning interpretable rules from multisource cardiac data, improving diagnosis accuracy and process efficiency in cardiac monitoring.

Contribution

It presents a novel strategy to handle high-dimensional multisource data with ILP, enhancing rule learning feasibility and accuracy in cardiac diagnosis.

Findings

Method significantly improves rule learning efficiency.

Using multiple sources enhances cardiac arrhythmia diagnosis.

Approach maintains high interpretability of learned rules.

Abstract

This paper formalises the concept of learning symbolic rules from multisource data in a cardiac monitoring context. Our sources, electrocardiograms and arterial blood pressure measures, describe cardiac behaviours from different viewpoints. To learn interpretable rules, we use an Inductive Logic Programming (ILP) method. We develop an original strategy to cope with the dimensionality issues caused by using this ILP technique on a rich multisource language. The results show that our method greatly improves the feasibility and the efficiency of the process while staying accurate. They also confirm the benefits of using multiple sources to improve the diagnosis of cardiac arrhythmias.