Learning rules from multisource data for cardiac monitoring

2009/02/19 by Marie-Odile Cordier, Mathis Cordier, Cordier, Marie-Odile +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial intelligence #Biomedical Text Mining and Ontologies #Computer science #Data science #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Machine Learning (cs.LG) #Semantic Web and Ontologies #cs.LG

paper · pdf · open access · doi:10.48550/arxiv.0902.3373

published in arXiv (Cornell University) (Cornell University)

arxiv created 2009/02/19 · openalex publication_date 2009/02/19 · arxiv updated 2009/12/01 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28

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.

Related

• Parallel universes to improve the diagnosis of cardiac arrhythmias
• Learning Temporal Logical Properties Discriminating ECG models of Cardiac Arrhytmias
• Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?
• Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification
• Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata