Authors:
Hadi Banaee
;
Mobyen Uddin Ahmed
and
Amy Loutfi
Affiliation:
Örebro University, Sweden
Keyword(s):
Rule Mining, Pattern Abstraction, Health Parameters, Physiological Time Series, Clinical Condition.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Clinical Problems and Applications
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Design and Development Methodologies for Healthcare IT
;
Devices
;
Enterprise Information Systems
;
Health Information Systems
;
Human-Computer Interaction
;
Pattern Recognition and Machine Learning
;
Physiological Computing Systems
;
Physiological Modeling
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Wearable Sensors and Systems
Abstract:
This paper presents an approach to automatically mine rules in time series data representing physiological
parameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined for
each physiological time series data. The generated rules based on the prototypical patterns are then described
in a textual representation which captures trends in each physiological parameter and their relation to the other
physiological data. In this paper, a method for measuring similarity of rule sets is introduced in order to
validate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classes
in the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rule
mining technique is able to acquire a distinctive model for each clinical condition, and represent the generated
rules in a human understandable textual representation.