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Authors: Hadi Banaee ; Mobyen Uddin Ahmed and Amy Loutfi

Affiliation: Örebro University, Sweden

ISBN: 978-989-758-068-0

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.

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Paper citation in several formats:
Banaee, H.; Ahmed, M. and Loutfi, A. (2015). Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data.In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 103-113. DOI: 10.5220/0005220901030113

@conference{healthinf15,
author={Hadi Banaee. and Mobyen Uddin Ahmed. and Amy Loutfi.},
title={Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},
year={2015},
pages={103-113},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005220901030113},
isbn={978-989-758-068-0},
}

TY - CONF

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data
SN - 978-989-758-068-0
AU - Banaee, H.
AU - Ahmed, M.
AU - Loutfi, A.
PY - 2015
SP - 103
EP - 113
DO - 10.5220/0005220901030113

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