Application of ILP to Cardiac Arrhythmia Characterization for Chronicle Recognition

Authors:
Rene Quiniou;Marie-Odile Cordier;Guy Carrault;Feng Wang
Affiliations:
-;-;-;-
Venue:
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Year:
2001

Citing 9
Cited 2

KARDIO: a study in deep and qualitative knowledge for expert systems

KARDIO: a study in deep and qualitative knowledge for expert systems
Inductive logic programming: issues, results and the challenge of learning language in logic

Artificial Intelligence - Special issue on applications of artificial intelligence
Advances in Inductive Logic Programming

Advances in Inductive Logic Programming
A Model-Based Approach for Learning to Identify Cardiac Arrhythmias

AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
On Multi-class Problems and Discretization in Inductive Logic Programming

ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Inductive Constraint Logic

ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Analyzing and Learning ECG Waveforms

ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction

AI Communications
Situation recognition: representation and algorithms

IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1

Improving multiclass ILP by combining partial rules with winnow algorithm: results on classification of dopamine antagonist molecules

JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose to use ILP techniques to learn sets of temporally constrained events called chronicles that a monitoring tool will use to detect pathological situations. ICL, a system providing a declarative bias language, was used for the experiments on learning cardiac arrhythmias. We show how to obtain properties, such as compactness, robustness or readability, by varying the learning bias.